docs: add benchmark collaboration call, voxtral in powered-by section

WER vs RTF scatter plot showing all backend/policy/model combos
on the 30s English file. Sweet spot zone highlights the best
tradeoffs. Added to both BENCHMARK.md and README.md.

.gitignore

@@ -54,21 +54,6 @@ coverage.xml
 # Translations
 *.mo
 *.pot
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
 
 # PyBuilder
 target/
@@ -134,7 +119,11 @@ run_*.sh
 *.pt 
 
 # Debug & testing
-test_*.py
+/test_*.py
+!test_backend_offline.py
 launch.json
 .DS_Store
-test/*
+/test/
+!tests/
+nllb-200-distilled-600M-ctranslate2/*
+*.mp3

BENCHMARK.md

@@ -0,0 +1,205 @@
+# WhisperLiveKit Benchmark Report
+
+Benchmark comparing all supported ASR backends, streaming policies, and model sizes on Apple Silicon.
+All tests run through the full AudioProcessor pipeline (same code path as production WebSocket).
+
+## Test Environment
+
+| Property | Value |
+|----------|-------|
+| Hardware | Apple M4, 32 GB RAM |
+| OS | macOS 25.3.0 (arm64) |
+| Python | 3.13 |
+| faster-whisper | 1.2.1 |
+| mlx-whisper | installed (via mlx) |
+| Voxtral MLX | native MLX backend |
+| Voxtral (HF) | transformers-based |
+| VAC (Silero VAD) | enabled unless noted |
+| Chunk size | 100 ms |
+| Pacing | no-realtime (as fast as possible) |
+
+## Audio Test Files
+
+| File | Duration | Language | Speakers | Description |
+|------|----------|----------|----------|-------------|
+| `00_00_07_english_1_speaker.wav` | 7.2 s | English | 1 | Short dictation with pauses |
+| `00_00_16_french_1_speaker.wav` | 16.3 s | French | 1 | French speech with intentional silence gaps |
+| `00_00_30_english_3_speakers.wav` | 30.0 s | English | 3 | Multi-speaker conversation |
+
+Ground truth transcripts (`.transcript.json`) with per-word timestamps are hand-verified.
+
+---
+
+## Results
+
+### English -- Short (7.2 s, 1 speaker)
+
+| Backend | Policy | Model | RTF | WER | Timestamp MAE |
+|---------|--------|-------|-----|-----|---------------|
+| faster-whisper | LocalAgreement | base | 0.20x | 21.1% | 0.080 s |
+| faster-whisper | SimulStreaming | base | 0.14x | 0.0% | 0.239 s |
+| faster-whisper | LocalAgreement | small | 0.59x | 21.1% | 0.089 s |
+| faster-whisper | SimulStreaming | small | 0.39x | 0.0% | 0.221 s |
+| mlx-whisper | LocalAgreement | base | 0.05x | 21.1% | 0.080 s |
+| mlx-whisper | SimulStreaming | base | 0.14x | 10.5% | 0.245 s |
+| mlx-whisper | LocalAgreement | small | 0.16x | 21.1% | 0.089 s |
+| mlx-whisper | SimulStreaming | small | 0.20x | 10.5% | 0.226 s |
+| voxtral-mlx | voxtral | 4B | 0.32x | 0.0% | 0.254 s |
+| voxtral (HF) | voxtral | 4B | 1.29x | 0.0% | 1.876 s |
+
+### English -- Multi-speaker (30.0 s, 3 speakers)
+
+| Backend | Policy | Model | RTF | WER | Timestamp MAE |
+|---------|--------|-------|-----|-----|---------------|
+| faster-whisper | LocalAgreement | base | 0.24x | 44.7% | 0.235 s |
+| faster-whisper | SimulStreaming | base | 0.10x | 5.3% | 0.398 s |
+| faster-whisper | LocalAgreement | small | 0.59x | 25.0% | 0.226 s |
+| faster-whisper | SimulStreaming | small | 0.26x | 5.3% | 0.387 s |
+| mlx-whisper | LocalAgreement | base | 0.06x | 23.7% | 0.237 s |
+| mlx-whisper | SimulStreaming | base | 0.11x | 5.3% | 0.395 s |
+| mlx-whisper | LocalAgreement | small | 0.13x | 25.0% | 0.226 s |
+| mlx-whisper | SimulStreaming | small | 0.20x | 5.3% | 0.394 s |
+| voxtral-mlx | voxtral | 4B | 0.31x | 9.2% | 0.176 s |
+| voxtral (HF) | voxtral | 4B | 1.00x | 32.9% | 1.034 s |
+
+<p align="center">
+<img src="benchmark_chart.png" alt="Benchmark comparison on 30s English" width="800">
+</p>
+
+<p align="center">
+<img src="benchmark_scatter.png" alt="Speed vs Accuracy tradeoff" width="700">
+</p>
+
+### French (16.3 s, 1 speaker, `--language fr`)
+
+| Backend | Policy | Model | RTF | WER | Timestamp MAE |
+|---------|--------|-------|-----|-----|---------------|
+| faster-whisper | LocalAgreement | base | 0.22x | 25.7% | 3.460 s |
+| faster-whisper | SimulStreaming | base | 0.10x | 31.4% | 3.660 s |
+| faster-whisper | LocalAgreement | small | 0.76x | 42.9% | 0.051 s |
+| faster-whisper | SimulStreaming | small | 0.29x | 25.7% | 0.219 s |
+| mlx-whisper | LocalAgreement | base | 0.09x | ~45%* | ~5.0 s* |
+| mlx-whisper | SimulStreaming | base | 0.09x | 40.0% | 3.540 s |
+| mlx-whisper | LocalAgreement | small | 0.14x | 25.7% | 0.083 s |
+| mlx-whisper | SimulStreaming | small | 0.17x | 31.4% | 0.203 s |
+| voxtral-mlx | voxtral | 4B | 0.18x | 37.1% | 3.422 s |
+| voxtral (HF) | voxtral | 4B | 0.63x | 28.6% | 4.040 s |
+
+\* mlx-whisper + LocalAgreement + base is unstable on this French file (WER fluctuates 34-1037% across runs due to hallucination loops). The `small` model does not have this problem.
+
+**Timestamp note:** The base model produces very high timestamp MAE (3.4-3.7s) on this French file because it misaligns words around the silence gaps. The small model handles this much better (0.05-0.22s MAE). Voxtral also drifts on the silence gaps.
+
+---
+
+## Model Size Comparison (base vs small)
+
+| | base | small | Observation |
+|--|------|-------|-------------|
+| **RTF** | 0.05-0.24x | 0.13-0.76x | small is 2-3x slower |
+| **English WER (SS)** | 0-5.3% | 0-5.3% | No improvement: SimulStreaming already saturates on base |
+| **English WER (LA)** | 21-44.7% | 21-25% | small reduces LA errors on longer audio |
+| **French WER** | 25-40% | 25-43% | Mixed: depends on backend/policy combo |
+| **French timestamps** | 3.4-5.0s MAE | 0.05-0.22s MAE | small is dramatically better for French timestamps |
+
+In short: **base + SimulStreaming** gives the best speed/accuracy tradeoff for English. The small model only helps if you need LocalAgreement (for subtitle-grade timestamps) or non-English languages.
+
+---
+
+## Key Findings
+
+### Speed (RTF = processing time / audio duration, lower is better)
+
+1. **mlx-whisper + LocalAgreement + base** is the fastest combo on Apple Silicon: 0.05-0.06x RTF on English. 30 seconds of audio in under 2 seconds.
+2. For **faster-whisper**, SimulStreaming is faster than LocalAgreement. For **mlx-whisper**, it is the opposite: LocalAgreement (0.05-0.06x) outperforms SimulStreaming (0.11-0.14x) on speed.
+3. **voxtral-mlx** runs at 0.18-0.32x RTF -- 3-5x slower than mlx-whisper base, but well within real-time.
+4. **voxtral (HF transformers)** hits 1.0-1.3x RTF. At the real-time boundary on Apple Silicon. Use the MLX variant instead.
+5. The **small** model is 2-3x slower than base across all backends.
+
+### Accuracy (WER = Word Error Rate, lower is better)
+
+1. **SimulStreaming** gives dramatically lower WER than LocalAgreement on the whisper backends. On the 30s English file: 5.3% vs 23-44%.
+2. **voxtral-mlx** hits 0% on short English and 9.2% on multi-speaker. It auto-detects language natively. Whisper also supports `--language auto`, but tends to bias towards English on short segments.
+3. **LocalAgreement** tends to repeat the last sentence at end-of-stream (a known LCP artifact), inflating WER. This is visible in the 21% WER on the 7s file -- the same 4 extra words appear in every LA run.
+4. On **French** with the correct `--language fr`, whisper base achieves 25-40% WER -- comparable to Voxtral's 28-37%. The small model does not consistently improve French WER.
+
+### Timestamps (MAE = Mean Absolute Error on word start times)
+
+1. **LocalAgreement** gives the best timestamps on English (0.08-0.09s MAE).
+2. **SimulStreaming** is less precise (0.22-0.40s MAE) but good enough for most applications.
+3. On French with silence gaps, **base model timestamps are unreliable** (3.4-5s MAE). The **small model fixes this** (0.05-0.22s MAE). This is the strongest argument for using `small` over `base`.
+4. **voxtral-mlx** has good timestamps on English (0.18-0.25s MAE) but drifts on audio with long silence gaps (3.4s MAE on the French file).
+
+### VAC (Voice Activity Classification) Impact
+
+| Backend | Policy | VAC | 7s English WER | 30s English WER |
+|---------|--------|-----|----------------|-----------------|
+| faster-whisper | LocalAgreement | on | 21.1% | 44.7% |
+| faster-whisper | LocalAgreement | off | 100.0% | 100.0% |
+| voxtral-mlx | voxtral | on | 0.0% | 9.2% |
+| voxtral-mlx | voxtral | off | 0.0% | 9.2% |
+
+- **Whisper backends need VAC** to work in streaming mode. Without it the buffer logic breaks down and you get empty or garbage output.
+- **Voxtral is unaffected by VAC** since it handles its own internal chunking. Identical results with or without. VAC still saves compute on silent segments.
+
+---
+
+## Recommendations
+
+| Use Case | Backend | Policy | Model | Notes |
+|----------|---------|--------|-------|-------|
+| Fastest English (Apple Silicon) | mlx-whisper | SimulStreaming | base | 0.11x RTF, 5.3% WER |
+| Fastest English (Linux/GPU) | faster-whisper | SimulStreaming | base | 0.10x RTF, 5.3% WER |
+| Best accuracy, English | faster-whisper | SimulStreaming | small | 0.26x RTF, 5.3% WER, still fast |
+| Multilingual / auto-detect | voxtral-mlx | voxtral | 4B | 100+ languages, 0.18-0.32x RTF |
+| Best timestamps | any | LocalAgreement | small | 0.05-0.09s MAE, good for subtitles |
+| Low memory / embedded | mlx-whisper | SimulStreaming | base | Smallest footprint, fastest response |
+
+---
+
+## Caveats
+
+- **3 test files, ~53 seconds total.** Results give relative rankings between backends but should not be taken as definitive WER numbers. Run on your own data for production decisions.
+- **RTF varies between runs** (up to +/-30%) depending on thermal state, background processes, and model caching. The numbers above are single sequential runs on a warm machine.
+- **Only base and small tested.** Medium and large-v3 would likely improve WER at the cost of higher RTF. We did not test them here because they are slow on Apple Silicon without GPU.
+
+---
+
+## Reproducing These Benchmarks
+
+```bash
+# Install test dependencies
+pip install -e ".[test]"
+
+# Single backend test
+python test_backend_offline.py --backend faster-whisper --policy simulstreaming --model base --no-realtime
+
+# With a specific language
+python test_backend_offline.py --backend mlx-whisper --policy simulstreaming --model small --lan fr --no-realtime
+
+# Multi-backend auto-detect benchmark
+python test_backend_offline.py --benchmark --no-realtime
+
+# Export to JSON
+python test_backend_offline.py --benchmark --no-realtime --json results.json
+
+# Test with your own audio
+python test_backend_offline.py --backend voxtral-mlx --audio your_file.wav --no-realtime
+```
+
+The benchmark harness computes WER and timestamp accuracy automatically when ground truth
+`.transcript.json` files exist alongside the audio files. See `audio_tests/` for the format.
+
+---
+
+## Help Us Benchmark on More Hardware
+
+These results are from a single Apple M4 machine. We'd love to see numbers from other setups: Linux with CUDA GPUs, older Macs, different CPU architectures, cloud instances, etc.
+
+If you run the benchmark on your hardware, please open an issue or PR with your results and we will add them here. The more data points we have, the better the recommendations get.
+
+What we are especially interested in:
+- **NVIDIA GPUs** (RTX 3090, 4090, A100, T4, etc.) with faster-whisper
+- **Older Apple Silicon** (M1, M2, M3) with mlx-whisper and voxtral-mlx
+- **Medium and large-v3 models** (we only tested base and small so far)
+- **Longer audio files** or domain-specific audio (medical, legal, call center)
+- **Other languages** beyond English and French

DEV_NOTES.md

@@ -18,8 +18,29 @@ Decoder weights: 59110771 bytes
 Encoder weights: 15268874 bytes
 
 
+# 2. Translation: Faster model for each system
 
-# 2. SortFormer Diarization: 4-to-2 Speaker Constraint Algorithm
+## Benchmark Results
+
+Testing on MacBook M3 with NLLB-200-distilled-600M model:
+
+### Standard Transformers vs CTranslate2
+
+| Test Text | Standard Inference Time | CTranslate2 Inference Time | Speedup |
+|-----------|-------------------------|---------------------------|---------|
+| UN Chief says there is no military solution in Syria | 0.9395s | 2.0472s | 0.5x |
+| The rapid advancement of AI technology is transforming various industries | 0.7171s | 1.7516s | 0.4x |
+| Climate change poses a significant threat to global ecosystems | 0.8533s | 1.8323s | 0.5x |
+| International cooperation is essential for addressing global challenges | 0.7209s | 1.3575s | 0.5x |
+| The development of renewable energy sources is crucial for a sustainable future | 0.8760s | 1.5589s | 0.6x |
+
+**Results:**
+- Total Standard time: 4.1068s
+- Total CTranslate2 time: 8.5476s
+- CTranslate2 is slower on this system --> Use Transformers, and ideally we would have an mlx implementation.
+
+
+# 3. SortFormer Diarization: 4-to-2 Speaker Constraint Algorithm
 
 Transform a diarization model that predicts up to 4 speakers into one that predicts up to 2 speakers by mapping the output predictions.
 
@@ -67,4 +88,4 @@ ELSE:
     AS_2 ← B
 
 to finish
-```
+```

Dockerfile

@@ -37,9 +37,10 @@ RUN pip3 install --upgrade pip setuptools wheel && \
 COPY . .
 
 # Install WhisperLiveKit directly, allowing for optional dependencies
+# Example: --build-arg EXTRAS="translation"
 RUN if [ -n "$EXTRAS" ]; then \
       echo "Installing with extras: [$EXTRAS]"; \
-      pip install --no-cache-dir whisperlivekit[$EXTRAS]; \
+      pip install --no-cache-dir "whisperlivekit[$EXTRAS]"; \
     else \
       echo "Installing base package only"; \
       pip install --no-cache-dir whisperlivekit; \
@@ -78,6 +79,9 @@ RUN if [ -n "$HF_TKN_FILE" ]; then \
 
 EXPOSE 8000
 
+HEALTHCHECK --interval=30s --timeout=5s --start-period=120s --retries=3 \
+    CMD python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/')" || exit 1
+
 ENTRYPOINT ["whisperlivekit-server", "--host", "0.0.0.0"]
 
 CMD ["--model", "medium"]

Dockerfile.cpu

@@ -55,6 +55,9 @@ RUN if [ -n "$HF_TKN_FILE" ]; then \
 # Expose port for the transcription server
 EXPOSE 8000
 
+HEALTHCHECK --interval=30s --timeout=5s --start-period=120s --retries=3 \
+    CMD python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/')" || exit 1
+
 ENTRYPOINT ["whisperlivekit-server", "--host", "0.0.0.0"]
 
 # Default args - you might want to use a smaller model for CPU

LICENSE

@@ -1,52 +1,210 @@
-# License
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
 
-## Main Software License
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
 
-MIT License
+   1. Definitions.
 
-Copyright (c) 2025 Quentin Fuxa.  
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
 
-Permission is hereby granted, free of charge, to any person obtaining a copy  
-of this software and associated documentation files (the "Software"), to deal  
-in the Software without restriction, including without limitation the rights  
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell  
-copies of the Software, and to permit persons to whom the Software is  
-furnished to do so, subject to the following conditions:
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+      the copyright owner that is granting the License.
 
-The above copyright notice and this permission notice shall be included in all  
-copies or substantial portions of the Software.
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+      "You" (or "Your") shall mean an individual or Legal Entity
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+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2025 Quentin Fuxa
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
 ---
 
 ## Based on:
-- **whisper_streaming** by ÚFAL – MIT License – https://github.com/ufal/whisper_streaming. The original work by ÚFAL. License: https://github.com/ufal/whisper_streaming/blob/main/LICENSE  
-- **silero-vad** by Snakers4 – MIT License – https://github.com/snakers4/silero-vad. The work by Snakers4 (silero-vad). License: https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/LICENSE  
-- **Diart** by juanmc2005 – MIT License – https://github.com/juanmc2005/diart. The work in Diart by juanmc2005. License: https://github.com/juanmc2005/diart/blob/main/LICENSE
-- **SimulStreaming** by ÚFAL – Dual License (PolyForm Noncommercial License 1.0.0 / Commercial License) – https://github.com/ufal/SimulStreaming
+- **SimulWhisper** by Speech and Audio Technology LAB of Tsinghua University – Apache-2.0 – https://github.com/ufal/SimulStreaming
+- **SimulStreaming** by ÚFAL – MIT License – https://github.com/ufal/SimulStreaming
+- **NeMo** by NVidia - Apache-2.0 - https://github.com/NVIDIA-NeMo/NeMo 
+- **whisper_streaming** by ÚFAL – MIT License – https://github.com/ufal/whisper_streaming.
+- **silero-vad** by Snakers4 – MIT License – https://github.com/snakers4/silero-vad.
+- **Diart** by juanmc2005 – MIT License – https://github.com/juanmc2005/diart.

README.md

@@ -1,27 +1,31 @@
-<h1 align="center">WhisperLiveKit</h1>
+<h1 align="center">WLK</h1>
+<p align="center"><b>WhisperLiveKit: Ultra-low-latency, self-hosted speech-to-text with speaker identification</b></p>
+
 
 <p align="center">
 <img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/demo.png" alt="WhisperLiveKit Demo" width="730">
 </p>
 
-<p align="center"><b>Real-time, Fully Local Speech-to-Text with Speaker Identification</b></p>
 
 <p align="center">
 <a href="https://pypi.org/project/whisperlivekit/"><img alt="PyPI Version" src="https://img.shields.io/pypi/v/whisperlivekit?color=g"></a>
 <a href="https://pepy.tech/project/whisperlivekit"><img alt="PyPI Downloads" src="https://static.pepy.tech/personalized-badge/whisperlivekit?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=installations"></a>
 <a href="https://pypi.org/project/whisperlivekit/"><img alt="Python Versions" src="https://img.shields.io/badge/python-3.9--3.15-dark_green"></a>
-<a href="https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-MIT/Dual Licensed-dark_green"></a>
+<a href="https://huggingface.co/qfuxa/whisper-base-french-lora">
+  <img alt="Hugging Face Weights" src="https://img.shields.io/badge/🤗-Hugging%20Face%20Weights-yellow" />
+</a>
+<a href="https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache 2.0-dark_green"></a>
 </p>
 
 
-Real-time speech transcription directly to your browser, with a ready-to-use backend+server and a simple frontend. ✨
-
 #### Powered by Leading Research:
 
-- [SimulStreaming](https://github.com/ufal/SimulStreaming) (SOTA 2025) - Ultra-low latency transcription with AlignAtt policy
-- [WhisperStreaming](https://github.com/ufal/whisper_streaming) (SOTA 2023) - Low latency transcription with LocalAgreement policy
+- Simul-[Whisper](https://arxiv.org/pdf/2406.10052)/[Streaming](https://arxiv.org/abs/2506.17077) (SOTA 2025) - Ultra-low latency transcription using [AlignAtt policy](https://arxiv.org/pdf/2305.11408)
+- [NLLW](https://github.com/QuentinFuxa/NoLanguageLeftWaiting) (2025), based on [distilled](https://huggingface.co/entai2965/nllb-200-distilled-600M-ctranslate2) [NLLB](https://arxiv.org/abs/2207.04672) (2022, 2024) - Simulatenous translation from & to 200 languages.
+- [WhisperStreaming](https://github.com/ufal/whisper_streaming) (SOTA 2023) - Low latency transcription using [LocalAgreement policy](https://www.isca-archive.org/interspeech_2020/liu20s_interspeech.pdf)
 - [Streaming Sortformer](https://arxiv.org/abs/2507.18446) (SOTA 2025) - Advanced real-time speaker diarization
 - [Diart](https://github.com/juanmc2005/diart) (SOTA 2021) - Real-time speaker diarization
+- [Voxtral Mini](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) (2025) - 4B-parameter multilingual speech model by Mistral AI
 - [Silero VAD](https://github.com/snakers4/silero-vad) (2024) - Enterprise-grade Voice Activity Detection
 
 
@@ -39,65 +43,101 @@ Real-time speech transcription directly to your browser, with a ready-to-use bac
 ```bash
 pip install whisperlivekit
 ```
-
->  **FFmpeg is required** and must be installed before using WhisperLiveKit
-> 
-> | OS | How to install |
-> |-----------|-------------|
->  | Ubuntu/Debian | `sudo apt install ffmpeg` |
-> | MacOS | `brew install ffmpeg` |
-> | Windows | Download .exe from https://ffmpeg.org/download.html and add to PATH |
+> You can also clone the repo and `pip install -e .` for the latest version.
 
 #### Quick Start
 1. **Start the transcription server:**
    ```bash
-   whisperlivekit-server --model base --language en
+   wlk --model base --language en
    ```
 
 2. **Open your browser** and navigate to `http://localhost:8000`. Start speaking and watch your words appear in real-time!
 
 
-> - See [tokenizer.py](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py) for the list of all available languages.
+> - See [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py) for the list of all available languages.
+> - Check the [troubleshooting guide](docs/troubleshooting.md) for step-by-step fixes collected from recent GPU setup/env issues.
+> - The CLI entry point is exposed as both `wlk` and `whisperlivekit-server`; they are equivalent.
 > - For HTTPS requirements, see the **Parameters** section for SSL configuration options.
 
- 
+
+#### Use it to capture audio from web pages.
+
+Go to `chrome-extension` for instructions.
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/chrome-extension/demo-extension.png" alt="WhisperLiveKit Demo" width="600">
+</p>
+
+
 
 #### Optional Dependencies
 
 | Optional | `pip install` |
 |-----------|-------------|
-| **Speaker diarization with Sortformer** | `git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]` |
-| **Apple Silicon optimized backend** | `mlx-whisper` |
+| **Windows/Linux optimizations** | `faster-whisper` |
+| **Apple Silicon optimizations** | `mlx-whisper` |
+| **Voxtral (multilingual, auto-detect)** | `transformers torch` (or use built-in `voxtral-mlx` on Apple Silicon) |
+| **Translation** | `nllw` |
+| **Speaker diarization** | `git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]` |
+| OpenAI API | `openai` |
 | *[Not recommanded]*  Speaker diarization with Diart | `diart` |
-| *[Not recommanded]*  Original Whisper backend | `whisper` |
-| *[Not recommanded]*  Improved timestamps backend | `whisper-timestamped` |
-| OpenAI API backend | `openai` |
 
-See  **Parameters & Configuration** below on how to use them.
+See **Parameters & Configuration** below on how to use them.
+
+<p align="center">
+<img src="benchmark_scatter.png" alt="Speed vs Accuracy tradeoff" width="700">
+</p>
+
+See **[BENCHMARK.md](BENCHMARK.md)** for the full benchmark with tables, model size comparison, and more.
+We are actively looking for benchmark results on other hardware (NVIDIA GPUs, different Apple Silicon chips, cloud instances). If you run the benchmarks on your machine, please share your results via an issue or PR!
 
 
 
+### Voxtral Backend
+
+WhisperLiveKit supports [Voxtral Mini](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602),
+a 4B-parameter speech model from Mistral AI that natively handles 100+ languages with automatic
+language detection. Whisper also supports auto-detection (`--language auto`), but Voxtral's per-chunk
+detection is more reliable and does not bias towards English.
+
+```bash
+# Apple Silicon (native MLX, recommended)
+wlk --backend voxtral-mlx
+
+# Linux/GPU (HuggingFace transformers)
+pip install transformers torch
+wlk --backend voxtral
+```
+
+Voxtral uses its own streaming policy and does not use LocalAgreement or SimulStreaming.
+See [BENCHMARK.md](BENCHMARK.md) for performance numbers.
+
 ### Usage Examples
 
 **Command-line Interface**: Start the transcription server with various options:
 
 ```bash
-# Use better model than default (small)
-whisperlivekit-server --model large-v3
+# Large model and translate from french to danish
+wlk --model large-v3 --language fr --target-language da
 
-# Advanced configuration with diarization and language
-whisperlivekit-server --host 0.0.0.0 --port 8000 --model medium --diarization --language fr
+# Diarization and server listening on */80
+wlk --host 0.0.0.0 --port 80 --model medium --diarization --language fr
+
+# Voxtral multilingual (auto-detects language)
+wlk --backend voxtral-mlx
 ```
 
 
 **Python API Integration**: Check [basic_server](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/basic_server.py) for a more complete example of how to use the functions and classes.
 
 ```python
-from whisperlivekit import TranscriptionEngine, AudioProcessor, parse_args
+import asyncio
+from contextlib import asynccontextmanager
+
 from fastapi import FastAPI, WebSocket, WebSocketDisconnect
 from fastapi.responses import HTMLResponse
-from contextlib import asynccontextmanager
-import asyncio
+
+from whisperlivekit import AudioProcessor, TranscriptionEngine, parse_args
 
 transcription_engine = None
 
@@ -133,36 +173,44 @@ async def websocket_endpoint(websocket: WebSocket):
 
 ## Parameters & Configuration
 
-An important list of parameters can be changed. But what *should* you change?
-- the `--model` size. List and recommandations [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/available_models.md)
-- the `--language`.  List [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py). If you use `auto`, the model attempts to detect the language automatically, but it tends to bias towards English.
-- the `--backend` ? you can switch to `--backend faster-whisper` if  `simulstreaming` does not work correctly or if you prefer to avoid the dual-license requirements.
-- `--warmup-file`, if you have one
-- `--task translate`, to translate in english
-- `--host`, `--port`, `--ssl-certfile`, `--ssl-keyfile`, if you set up a server
-- `--diarization`, if you want to use it.
-
-The rest I don't recommend. But below are your options.
 
 | Parameter | Description | Default |
 |-----------|-------------|---------|
-| `--model` | Whisper model size. | `small` |
-| `--language` | Source language code or `auto` | `auto` |
-| `--task` | `transcribe` or `translate` | `transcribe` |
-| `--backend` | Processing backend | `simulstreaming` |
-| `--min-chunk-size` | Minimum audio chunk size (seconds) | `1.0` |
-| `--no-vac` | Disable Voice Activity Controller | `False` |
-| `--no-vad` | Disable Voice Activity Detection | `False` |
+| `--model` | Whisper model size. List and recommandations [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/default_and_custom_models.md) | `small` |
+| `--model-path` | Local .pt file/directory **or** Hugging Face repo ID containing the Whisper model. Overrides `--model`. Recommandations [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/default_and_custom_models.md) | `None` |
+| `--language` | List [here](docs/supported_languages.md). If you use `auto`, the model attempts to detect the language automatically, but it tends to bias towards English. | `auto` |
+| `--target-language` | If sets, translates using [NLLW](https://github.com/QuentinFuxa/NoLanguageLeftWaiting). [200 languages available](docs/supported_languages.md). If you want to translate to english, you can also use `--direct-english-translation`. The STT model will try to directly output the translation. | `None` |
+| `--diarization` | Enable speaker identification | `False` |
+| `--backend-policy` | Streaming strategy: `1`/`simulstreaming` uses AlignAtt SimulStreaming, `2`/`localagreement` uses the LocalAgreement policy | `simulstreaming` |
+| `--backend` | ASR backend selector. `auto` picks MLX on macOS (if installed), otherwise Faster-Whisper, otherwise vanilla Whisper. Options: `mlx-whisper`, `faster-whisper`, `whisper`, `openai-api` (LocalAgreement only), `voxtral-mlx` (Apple Silicon), `voxtral` (HuggingFace) | `auto` |
+| `--no-vac` | Disable Voice Activity Controller. NOT ADVISED | `False` |
+| `--no-vad` | Disable Voice Activity Detection. NOT ADVISED | `False` |
 | `--warmup-file` | Audio file path for model warmup | `jfk.wav` |
 | `--host` | Server host address | `localhost` |
 | `--port` | Server port | `8000` |
 | `--ssl-certfile` | Path to the SSL certificate file (for HTTPS support) | `None` |
 | `--ssl-keyfile` | Path to the SSL private key file (for HTTPS support) | `None` |
+| `--forwarded-allow-ips` | Ip or Ips allowed to reverse proxy the whisperlivekit-server. Supported types are  IP Addresses (e.g. 127.0.0.1), IP Networks (e.g. 10.100.0.0/16), or Literals (e.g. /path/to/socket.sock) | `None` |
+| `--pcm-input` | raw PCM (s16le) data is expected as input and FFmpeg will be bypassed. Frontend will use AudioWorklet instead of MediaRecorder | `False` |
+| `--lora-path` | Path or Hugging Face repo ID for LoRA adapter weights (e.g., `qfuxa/whisper-base-french-lora`). Only works with native Whisper backend (`--backend whisper`) | `None` |
 
+| Translation options | Description | Default |
+|-----------|-------------|---------|
+| `--nllb-backend` | `transformers` or `ctranslate2` | `ctranslate2` |
+| `--nllb-size` | `600M` or `1.3B` | `600M` |
+
+| Diarization options | Description | Default |
+|-----------|-------------|---------|
+| `--diarization-backend` |  `diart` or `sortformer` | `sortformer` |
+| `--disable-punctuation-split` | [NOT FUNCTIONAL IN 0.2.15 / 0.2.16] Disable punctuation based splits. See #214 | `False` |
+| `--segmentation-model` | Hugging Face model ID for Diart segmentation model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `pyannote/segmentation-3.0` |
+| `--embedding-model` | Hugging Face model ID for Diart embedding model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `speechbrain/spkrec-ecapa-voxceleb` |
 
 | SimulStreaming backend options | Description | Default |
 |-----------|-------------|---------|
 | `--disable-fast-encoder` | Disable Faster Whisper or MLX Whisper backends for the encoder (if installed). Inference can be slower but helpful when GPU memory is limited | `False` |
+| `--custom-alignment-heads` | Use your own alignment heads, useful when `--model-dir` is used. Use `scripts/determine_alignment_heads.py` to extract them. <img src="scripts/alignment_heads.png" alt="WhisperLiveKit Demo" width="300">
+ | `None` |
 | `--frame-threshold` | AlignAtt frame threshold (lower = faster, higher = more accurate) | `25` |
 | `--beams` | Number of beams for beam search (1 = greedy decoding) | `1` |
 | `--decoder` | Force decoder type (`beam` or `greedy`) | `auto` |
@@ -172,9 +220,8 @@ The rest I don't recommend. But below are your options.
 | `--never-fire` | Never truncate incomplete words | `False` |
 | `--init-prompt` | Initial prompt for the model | `None` |
 | `--static-init-prompt` | Static prompt that doesn't scroll | `None` |
-| `--max-context-tokens` | Maximum context tokens | `None` |
-| `--model-path` | Direct path to .pt model file. Download it if not found | `./base.pt` |
-| `--preloaded-model-count` | Optional. Number of models to preload in memory to speed up loading (set up to the expected number of concurrent users) | `1` |
+| `--max-context-tokens` | Maximum context tokens | Depends on model used, but usually 448. |
+
 
 
 | WhisperStreaming backend options | Description | Default |
@@ -182,19 +229,10 @@ The rest I don't recommend. But below are your options.
 | `--confidence-validation` | Use confidence scores for faster validation | `False` |
 | `--buffer_trimming` | Buffer trimming strategy (`sentence` or `segment`) | `segment` |
 
-| Diarization options | Description | Default |
-|-----------|-------------|---------|
-| `--diarization` | Enable speaker identification | `False` |
-| `--diarization-backend` |  `diart` or `sortformer` | `sortformer` |
-| `--segmentation-model` | Hugging Face model ID for Diart segmentation model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `pyannote/segmentation-3.0` |
-| `--embedding-model` | Hugging Face model ID for Diart embedding model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `speechbrain/spkrec-ecapa-voxceleb` |
 
 
-> For diarization using Diart, you need access to pyannote.audio models:
-> 1. [Accept user conditions](https://huggingface.co/pyannote/segmentation) for the `pyannote/segmentation` model
-> 2. [Accept user conditions](https://huggingface.co/pyannote/segmentation-3.0) for the `pyannote/segmentation-3.0` model
-> 3. [Accept user conditions](https://huggingface.co/pyannote/embedding) for the `pyannote/embedding` model
->4. Login with HuggingFace: `huggingface-cli login`
+
+> For diarization using Diart, you need to accept user conditions [here](https://huggingface.co/pyannote/segmentation) for the `pyannote/segmentation` model, [here](https://huggingface.co/pyannote/segmentation-3.0) for the `pyannote/segmentation-3.0` model and [here](https://huggingface.co/pyannote/embedding) for the `pyannote/embedding` model. **Then**, login to HuggingFace: `huggingface-cli login`
 
 ### 🚀 Deployment Guide
 
@@ -260,9 +298,33 @@ docker run --gpus all -p 8000:8000 --name wlk wlk --model large-v3 --language fr
 #### Customization
 
 - `--build-arg` Options:
-  - `EXTRAS="whisper-timestamped"` - Add extras to the image's installation (no spaces). Remember to set necessary container options!
+  - `EXTRAS="translation"` - Add extras to the image's installation (no spaces). Remember to set necessary container options!
   - `HF_PRECACHE_DIR="./.cache/"` - Pre-load a model cache for faster first-time start
   - `HF_TKN_FILE="./token"` - Add your Hugging Face Hub access token to download gated models
 
-## 🔮 Use Cases
+## Testing & Benchmarks
+
+WhisperLiveKit includes a unit test suite and an offline benchmark harness.
+
+```bash
+# Install test dependencies
+pip install -e ".[test]"
+
+# Run unit tests (no model download required)
+pytest tests/ -v
+
+# Benchmark a single backend
+python test_backend_offline.py --backend faster-whisper --no-realtime
+
+# Benchmark all installed backends
+python test_backend_offline.py --benchmark --no-realtime
+
+# Export benchmark results as JSON
+python test_backend_offline.py --benchmark --no-realtime --json results.json
+```
+
+See [BENCHMARK.md](BENCHMARK.md) for a full comparison of backends, policies, WER, speed, and
+timestamp accuracy on Apple Silicon.
+
+## Use Cases
 Capture discussions in real-time for meeting transcription, help hearing-impaired users follow conversations through accessibility tools, transcribe podcasts or videos automatically for content creation, transcribe support calls with speaker identification for customer service...

ReadmeJP.md

@@ -1,258 +0,0 @@
-<h1 align="center">WhisperLiveKit</h1>
-
-<p align="center">
-<img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/demo.png" alt="WhisperLiveKit Demo" width="730">
-</p>
-
-<p align="center"><b>話者識別機能付き、リアルタイム、完全ローカルな音声テキスト変換</b></p>
-
-<p align="center">
-<a href="https://pypi.org/project/whisperlivekit/"><img alt="PyPI Version" src="https://img.shields.io/pypi/v/whisperlivekit?color=g"></a>
-<a href="https://pepy.tech/project/whisperlivekit"><img alt="PyPI Downloads" src="https://static.pepy.tech/personalized-badge/whisperlivekit?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=installations"></a>
-<a href="https://pypi.org/project/whisperlivekit/"><img alt="Python Versions" src="https://img.shields.io/badge/python-3.9--3.13-dark_green"></a>
-<a href="https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-MIT/Dual Licensed-dark_green"></a>
-</p>
-
-すぐに使えるバックエンド+サーバーとシンプルなフロントエンドで、リアルタイムの音声文字起こしをブラウザに直接提供します。✨
-
-#### 主要な研究による技術：
-
-- [SimulStreaming](https://github.com/ufal/SimulStreaming) (SOTA 2025) - AlignAttポリシーによる超低遅延文字起こし
-- [WhisperStreaming](https://github.com/ufal/whisper_streaming) (SOTA 2023) - LocalAgreementポリシーによる低遅延文字起こし
-- [Streaming Sortformer](https://arxiv.org/abs/2507.18446) (SOTA 2025) - 高度なリアルタイム話者ダイアライゼーション
-- [Diart](https://github.com/juanmc2005/diart) (SOTA 2021) - リアルタイム話者ダイアライゼーション
-- [Silero VAD](https://github.com/snakers4/silero-vad) (2024) - エンタープライズグレードの音声区間検出
-
-> **なぜ各音声バッチで単純なWhisperモデルを実行しないのか？** Whisperは完全な発話向けに設計されており、リアルタイムのチャンク向けではありません。小さなセグメントを処理するとコンテキストが失われ、単語が音節の途中で途切れ、質の悪い文字起こしになります。WhisperLiveKitは、インテリジェントなバッファリングとインクリメンタルな処理のために、最先端の同時音声研究を利用しています。
-
-### アーキテクチャ
-
-<img alt="Architecture" src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/architecture.png" />
-
-*バックエンドは複数の同時ユーザーをサポートします。音声が検出されない場合、音声区間検出がオーバーヘッドを削減します。*
-
-### インストールとクイックスタート
-
-```bash
-pip install whisperlivekit
-```
-
->  **FFmpegが必要です** WhisperLiveKitを使用する前にインストールする必要があります。
->
-> | OS | インストール方法 |
-> |-----------|-------------|
->  | Ubuntu/Debian | `sudo apt install ffmpeg` |
-> | MacOS | `brew install ffmpeg` |
-> | Windows | https://ffmpeg.org/download.html から.exeをダウンロードし、PATHに追加 |
-
-#### クイックスタート
-1. **文字起こしサーバーを起動します:**
-   ```bash
-   whisperlivekit-server --model base --language en
-   ```
-
-2. **ブラウザを開き** `http://localhost:8000` にアクセスします。話し始めると、あなたの言葉がリアルタイムで表示されます！
-
-
-> - 利用可能なすべての言語のリストについては、[tokenizer.py](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py) を参照してください。
-> - HTTPSの要件については、**パラメータ**セクションのSSL設定オプションを参照してください。
-
-#### オプションの依存関係
-
-| オプション | `pip install` |
-|-----------|-------------|
-| **Sortformerによる話者ダイアライゼーション** | `git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]` |
-| Diartによる話者ダイアライゼーション | `diart` |
-| オリジナルのWhisperバックエンド | `whisper` |
-| タイムスタンプ改善バックエンド | `whisper-timestamped` |
-| Apple Silicon最適化バックエンド | `mlx-whisper` |
-| OpenAI APIバックエンド | `openai` |
-
-それらの使用方法については、以下の**パラメータと設定**を参照してください。
-
-### 使用例
-
-**コマンドラインインターフェース**: 様々なオプションで文字起こしサーバーを起動します:
-
-```bash
-# デフォルト(small)より良いモデルを使用
-whisperlivekit-server --model large-v3
-
-# ダイアライゼーションと言語を指定した高度な設定
-whisperlivekit-server --host 0.0.0.0 --port 8000 --model medium --diarization --language fr
-```
-
-**Python API連携**: 関数やクラスの使用方法のより完全な例については、[basic_server](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/basic_server.py) を確認してください。
-
-```python
-from whisperlivekit import TranscriptionEngine, AudioProcessor, parse_args
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
-from fastapi.responses import HTMLResponse
-from contextlib import asynccontextmanager
-import asyncio
-
-transcription_engine = None
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    global transcription_engine
-    transcription_engine = TranscriptionEngine(model="medium", diarization=True, lan="en")
-    yield
-
-app = FastAPI(lifespan=lifespan)
-
-async def handle_websocket_results(websocket: WebSocket, results_generator):
-    async for response in results_generator:
-        await websocket.send_json(response)
-    await websocket.send_json({"type": "ready_to_stop"})
-
-@app.websocket("/asr")
-async def websocket_endpoint(websocket: WebSocket):
-    global transcription_engine
-
-    # 接続ごとに新しいAudioProcessorを作成し、共有エンジンを渡す
-    audio_processor = AudioProcessor(transcription_engine=transcription_engine)
-    results_generator = await audio_processor.create_tasks()
-    results_task = asyncio.create_task(handle_websocket_results(websocket, results_generator))
-    await websocket.accept()
-    while True:
-        message = await websocket.receive_bytes()
-        await audio_processor.process_audio(message)
-```
-
-**フロントエンド実装**: パッケージにはHTML/JavaScript実装が[ここ](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/web/live_transcription.html)に含まれています。`from whisperlivekit import get_web_interface_html` & `page = get_web_interface_html()` を使ってインポートすることもできます。
-
-
-## パラメータと設定
-
-重要なパラメータのリストを変更できます。しかし、何を*変更すべき*でしょうか？
-- `--model` サイズ。リストと推奨事項は[こちら](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/available_models.md)
-- `--language`。リストは[こちら](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py)。`auto`を使用すると、モデルは自動的に言語を検出しようとしますが、英語に偏る傾向があります。
-- `--backend`？ `simulstreaming`が正しく動作しない場合や、デュアルライセンス要件を避けたい場合は`--backend faster-whisper`に切り替えることができます。
-- `--warmup-file`、もしあれば
-- `--host`, `--port`, `--ssl-certfile`, `--ssl-keyfile`、サーバーをセットアップする場合
-- `--diarization`、使用したい場合。
-
-残りは推奨しません。しかし、以下があなたのオプションです。
-
-| パラメータ | 説明 | デフォルト |
-|-----------|-------------|---------|
-| `--model` | Whisperモデルのサイズ。 | `small` |
-| `--language` | ソース言語コードまたは`auto` | `auto` |
-| `--task` | `transcribe`または`translate` | `transcribe` |
-| `--backend` | 処理バックエンド | `simulstreaming` |
-| `--min-chunk-size` | 最小音声チャンクサイズ（秒） | `1.0` |
-| `--no-vac` | 音声アクティビティコントローラーを無効化 | `False` |
-| `--no-vad` | 音声区間検出を無効化 | `False` |
-| `--warmup-file` | モデルのウォームアップ用音声ファイルパス | `jfk.wav` |
-| `--host` | サーバーホストアドレス | `localhost` |
-| `--port` | サーバーポート | `8000` |
-| `--ssl-certfile` | SSL証明書ファイルへのパス（HTTPSサポート用） | `None` |
-| `--ssl-keyfile` | SSL秘密鍵ファイルへのパス（HTTPSサポート用） | `None` |
-
-
-| WhisperStreamingバックエンドオプション | 説明 | デフォルト |
-|-----------|-------------|---------|
-| `--confidence-validation` | 高速な検証のために信頼スコアを使用 | `False` |
-| `--buffer_trimming` | バッファトリミング戦略（`sentence`または`segment`） | `segment` |
-
-
-| SimulStreamingバックエンドオプション | 説明 | デフォルト |
-|-----------|-------------|---------|
-| `--frame-threshold` | AlignAttフレームしきい値（低いほど速く、高いほど正確） | `25` |
-| `--beams` | ビームサーチのビーム数（1 = 貪欲デコーディング） | `1` |
-| `--decoder` | デコーダタイプを強制（`beam`または`greedy`） | `auto` |
-| `--audio-max-len` | 最大音声バッファ長（秒） | `30.0` |
-| `--audio-min-len` | 処理する最小音声長（秒） | `0.0` |
-| `--cif-ckpt-path` | 単語境界検出用CIFモデルへのパス | `None` |
-| `--never-fire` | 未完了の単語を決して切り捨てない | `False` |
-| `--init-prompt` | モデルの初期プロンプト | `None` |
-| `--static-init-prompt` | スクロールしない静的プロンプト | `None` |
-| `--max-context-tokens` | 最大コンテキストトークン数 | `None` |
-| `--model-path` | .ptモデルファイルへの直接パス。見つからない場合はダウンロード | `./base.pt` |
-| `--preloaded-model-count` | オプション。メモリにプリロードするモデルの数（予想される同時ユーザー数まで設定） | `1` |
-
-| ダイアライゼーションオプション | 説明 | デフォルト |
-|-----------|-------------|---------|
-| `--diarization` | 話者識別を有効化 | `False` |
-| `--diarization-backend` | `diart`または`sortformer` | `sortformer` |
-| `--segmentation-model` | DiartセグメンテーションモデルのHugging FaceモデルID。[利用可能なモデル](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `pyannote/segmentation-3.0` |
-| `--embedding-model` | Diart埋め込みモデルのHugging FaceモデルID。[利用可能なモデル](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `speechbrain/spkrec-ecapa-voxceleb` |
-
-
-> Diartを使用したダイアライゼーションには、pyannote.audioモデルへのアクセスが必要です：
-> 1. `pyannote/segmentation`モデルの[ユーザー条件に同意](https://huggingface.co/pyannote/segmentation)
-> 2. `pyannote/segmentation-3.0`モデルの[ユーザー条件に同意](https://huggingface.co/pyannote/segmentation-3.0)
-> 3. `pyannote/embedding`モデルの[ユーザー条件に同意](https://huggingface.co/pyannote/embedding)
->4. HuggingFaceでログイン: `huggingface-cli login`
-
-### 🚀 デプロイガイド
-
-WhisperLiveKitを本番環境にデプロイするには：
-
-1. **サーバーセットアップ**: 本番用ASGIサーバーをインストールし、複数のワーカーで起動します
-   ```bash
-   pip install uvicorn gunicorn
-   gunicorn -k uvicorn.workers.UvicornWorker -w 4 your_app:app
-   ```
-
-2. **フロントエンド**: カスタマイズした`html`のバージョンをホストし、WebSocket接続が正しくポイントするようにします
-
-3. **Nginx設定** (本番環境で推奨):
-    ```nginx
-   server {
-       listen 80;
-       server_name your-domain.com;
-        location / {
-            proxy_pass http://localhost:8000;
-            proxy_set_header Upgrade $http_upgrade;
-            proxy_set_header Connection "upgrade";
-            proxy_set_header Host $host;
-    }}
-    ```
-
-4. **HTTPSサポート**: 安全なデプロイメントのために、WebSocket URLで "ws://" の代わりに "wss://" を使用します
-
-## 🐋 Docker
-
-GPUまたはCPUサポート付きでDockerを使用してアプリケーションを簡単にデプロイします。
-
-### 前提条件
-- Dockerがシステムにインストールされていること
-- GPUサポートの場合: NVIDIA Dockerランタイムがインストールされていること
-
-### クイックスタート
-
-**GPUアクセラレーション付き (推奨):**
-```bash
-docker build -t wlk .
-docker run --gpus all -p 8000:8000 --name wlk wlk
-```
-
-**CPUのみ:**
-```bash
-docker build -f Dockerfile.cpu -t wlk .
-docker run -p 8000:8000 --name wlk wlk
-```
-
-### 高度な使用法
-
-**カスタム設定:**
-```bash
-# カスタムモデルと言語の例
-docker run --gpus all -p 8000:8000 --name wlk wlk --model large-v3 --language fr
-```
-
-### メモリ要件
-- **大規模モデル**: Dockerランタイムに十分なメモリが割り当てられていることを確認してください
-
-
-#### カスタマイズ
-
-- `--build-arg` オプション:
-  - `EXTRAS="whisper-timestamped"` - イメージのインストールにエクストラを追加します（スペースなし）。必要なコンテナオプションを設定することを忘れないでください！
-  - `HF_PRECACHE_DIR="./.cache/"` - 初回起動を高速化するためにモデルキャッシュをプリロードします
-  - `HF_TKN_FILE="./token"` - ゲート付きモデルをダウンロードするためにHugging Face Hubアクセストークンを追加します
-
-## 🔮 ユースケース
-会議の文字起こしのためにリアルタイムで議論をキャプチャする、聴覚障害のあるユーザーがアクセシビリティツールを通じて会話を追うのを助ける、コンテンツ作成のためにポッドキャストやビデオを自動的に文字起こしする、カスタマーサービスのために話者識別付きでサポートコールを文字起こしする...

audio_tests/00_00_07_english_1_speaker.transcript.json

@@ -0,0 +1,97 @@
+[
+  {
+    "word": "This",
+    "start": 0.0,
+    "end": 0.24
+  },
+  {
+    "word": "is",
+    "start": 0.24,
+    "end": 0.56
+  },
+  {
+    "word": "a",
+    "start": 0.56,
+    "end": 0.76
+  },
+  {
+    "word": "transcription",
+    "start": 0.76,
+    "end": 1.32
+  },
+  {
+    "word": "test.",
+    "start": 1.32,
+    "end": 2.0
+  },
+  {
+    "word": "We",
+    "start": 2.4,
+    "end": 2.5
+  },
+  {
+    "word": "want",
+    "start": 2.5,
+    "end": 2.66
+  },
+  {
+    "word": "to",
+    "start": 2.66,
+    "end": 2.84
+  },
+  {
+    "word": "see",
+    "start": 2.84,
+    "end": 3.1
+  },
+  {
+    "word": "if",
+    "start": 3.1,
+    "end": 3.34
+  },
+  {
+    "word": "we",
+    "start": 3.34,
+    "end": 3.5
+  },
+  {
+    "word": "can",
+    "start": 3.5,
+    "end": 3.68
+  },
+  {
+    "word": "use",
+    "start": 3.68,
+    "end": 4.04
+  },
+  {
+    "word": "smaller",
+    "start": 4.04,
+    "end": 4.76
+  },
+  {
+    "word": "chunks.",
+    "start": 4.76,
+    "end": 5.16
+  },
+  {
+    "word": "What",
+    "start": 6.06,
+    "end": 6.32
+  },
+  {
+    "word": "do",
+    "start": 6.32,
+    "end": 6.44
+  },
+  {
+    "word": "you",
+    "start": 6.44,
+    "end": 6.58
+  },
+  {
+    "word": "think?",
+    "start": 6.58,
+    "end": 6.84
+  }
+]

audio_tests/00_00_16_french_1_speaker.transcript.json

@@ -0,0 +1,177 @@
+[
+  {
+    "word": "Ok,",
+    "start": 2.02,
+    "end": 2.38
+  },
+  {
+    "word": "là",
+    "start": 2.52,
+    "end": 2.58
+  },
+  {
+    "word": "c",
+    "start": 2.58,
+    "end": 2.74
+  },
+  {
+    "word": "'est",
+    "start": 2.74,
+    "end": 2.76
+  },
+  {
+    "word": "un",
+    "start": 2.76,
+    "end": 2.86
+  },
+  {
+    "word": "test,",
+    "start": 2.86,
+    "end": 3.2
+  },
+  {
+    "word": "on",
+    "start": 3.34,
+    "end": 3.34
+  },
+  {
+    "word": "veut",
+    "start": 3.34,
+    "end": 3.48
+  },
+  {
+    "word": "voir",
+    "start": 3.48,
+    "end": 3.86
+  },
+  {
+    "word": "si",
+    "start": 3.86,
+    "end": 4.14
+  },
+  {
+    "word": "ça",
+    "start": 4.14,
+    "end": 4.26
+  },
+  {
+    "word": "arrive",
+    "start": 4.26,
+    "end": 4.36
+  },
+  {
+    "word": "à",
+    "start": 4.36,
+    "end": 4.5
+  },
+  {
+    "word": "capté",
+    "start": 4.5,
+    "end": 4.78
+  },
+  {
+    "word": "le",
+    "start": 4.78,
+    "end": 4.9
+  },
+  {
+    "word": "silence.",
+    "start": 4.9,
+    "end": 5.44
+  },
+  {
+    "word": "Là",
+    "start": 9.24,
+    "end": 9.6
+  },
+  {
+    "word": "il",
+    "start": 9.6,
+    "end": 9.78
+  },
+  {
+    "word": "est",
+    "start": 9.78,
+    "end": 9.84
+  },
+  {
+    "word": "une",
+    "start": 9.84,
+    "end": 9.96
+  },
+  {
+    "word": "telle",
+    "start": 9.96,
+    "end": 10.12
+  },
+  {
+    "word": "seconde",
+    "start": 10.12,
+    "end": 10.38
+  },
+  {
+    "word": "de",
+    "start": 10.38,
+    "end": 10.48
+  },
+  {
+    "word": "silence",
+    "start": 10.48,
+    "end": 10.78
+  },
+  {
+    "word": "et",
+    "start": 10.78,
+    "end": 11.06
+  },
+  {
+    "word": "je",
+    "start": 11.06,
+    "end": 11.16
+  },
+  {
+    "word": "vous",
+    "start": 11.16,
+    "end": 11.32
+  },
+  {
+    "word": "parle.",
+    "start": 11.32,
+    "end": 11.68
+  },
+  {
+    "word": "Et",
+    "start": 13.28,
+    "end": 13.64
+  },
+  {
+    "word": "voilà,",
+    "start": 13.64,
+    "end": 13.96
+  },
+  {
+    "word": "allez",
+    "start": 14.36,
+    "end": 14.62
+  },
+  {
+    "word": "on",
+    "start": 14.62,
+    "end": 14.78
+  },
+  {
+    "word": "va",
+    "start": 14.78,
+    "end": 14.88
+  },
+  {
+    "word": "tester",
+    "start": 14.88,
+    "end": 15.06
+  },
+  {
+    "word": "ça.",
+    "start": 15.06,
+    "end": 15.36
+  }
+]

audio_tests/00_00_30_english_3_speakers.transcript.json

@@ -0,0 +1,382 @@
+[
+  {
+    "word": "Transcription",
+    "start": 0.0,
+    "end": 0.6
+  },
+  {
+    "word": "technology",
+    "start": 0.6,
+    "end": 1.24
+  },
+  {
+    "word": "has",
+    "start": 1.24,
+    "end": 1.5
+  },
+  {
+    "word": "improved",
+    "start": 1.5,
+    "end": 1.96
+  },
+  {
+    "word": "so",
+    "start": 1.96,
+    "end": 2.32
+  },
+  {
+    "word": "much",
+    "start": 2.32,
+    "end": 2.68
+  },
+  {
+    "word": "in",
+    "start": 2.68,
+    "end": 2.94
+  },
+  {
+    "word": "the",
+    "start": 2.94,
+    "end": 3.02
+  },
+  {
+    "word": "past",
+    "start": 3.02,
+    "end": 3.24
+  },
+  {
+    "word": "few",
+    "start": 3.24,
+    "end": 3.5
+  },
+  {
+    "word": "years.",
+    "start": 3.5,
+    "end": 3.96
+  },
+  {
+    "word": "Have",
+    "start": 4.56,
+    "end": 4.74
+  },
+  {
+    "word": "you",
+    "start": 4.74,
+    "end": 4.9
+  },
+  {
+    "word": "noticed",
+    "start": 4.9,
+    "end": 5.26
+  },
+  {
+    "word": "how",
+    "start": 5.26,
+    "end": 5.52
+  },
+  {
+    "word": "accurate",
+    "start": 5.52,
+    "end": 6.08
+  },
+  {
+    "word": "real",
+    "start": 6.08,
+    "end": 6.42
+  },
+  {
+    "word": "-time",
+    "start": 6.42,
+    "end": 6.74
+  },
+  {
+    "word": "speech",
+    "start": 6.74,
+    "end": 7.24
+  },
+  {
+    "word": "to",
+    "start": 7.24,
+    "end": 7.46
+  },
+  {
+    "word": "text",
+    "start": 7.46,
+    "end": 7.78
+  },
+  {
+    "word": "is",
+    "start": 7.78,
+    "end": 8.0
+  },
+  {
+    "word": "now?",
+    "start": 8.0,
+    "end": 8.3
+  },
+  {
+    "word": "Absolutely.",
+    "start": 8.7,
+    "end": 9.16
+  },
+  {
+    "word": "I",
+    "start": 10.04,
+    "end": 10.38
+  },
+  {
+    "word": "use",
+    "start": 10.38,
+    "end": 10.56
+  },
+  {
+    "word": "it",
+    "start": 10.56,
+    "end": 10.76
+  },
+  {
+    "word": "all",
+    "start": 10.76,
+    "end": 10.9
+  },
+  {
+    "word": "the",
+    "start": 10.9,
+    "end": 11.04
+  },
+  {
+    "word": "time",
+    "start": 11.04,
+    "end": 11.32
+  },
+  {
+    "word": "for",
+    "start": 11.32,
+    "end": 11.54
+  },
+  {
+    "word": "taking",
+    "start": 11.54,
+    "end": 11.86
+  },
+  {
+    "word": "notes",
+    "start": 11.86,
+    "end": 12.16
+  },
+  {
+    "word": "during",
+    "start": 12.16,
+    "end": 12.54
+  },
+  {
+    "word": "meetings.",
+    "start": 12.54,
+    "end": 12.94
+  },
+  {
+    "word": "It's",
+    "start": 13.6,
+    "end": 13.8
+  },
+  {
+    "word": "amazing",
+    "start": 13.8,
+    "end": 14.1
+  },
+  {
+    "word": "how",
+    "start": 14.1,
+    "end": 14.48
+  },
+  {
+    "word": "it",
+    "start": 14.48,
+    "end": 14.62
+  },
+  {
+    "word": "can",
+    "start": 14.62,
+    "end": 14.74
+  },
+  {
+    "word": "recognise",
+    "start": 14.74,
+    "end": 15.24
+  },
+  {
+    "word": "different",
+    "start": 15.24,
+    "end": 15.68
+  },
+  {
+    "word": "speakers",
+    "start": 15.68,
+    "end": 16.16
+  },
+  {
+    "word": "and",
+    "start": 16.16,
+    "end": 16.8
+  },
+  {
+    "word": "even",
+    "start": 16.8,
+    "end": 17.1
+  },
+  {
+    "word": "add",
+    "start": 17.1,
+    "end": 17.44
+  },
+  {
+    "word": "punctuation.",
+    "start": 17.44,
+    "end": 18.36
+  },
+  {
+    "word": "Yeah,",
+    "start": 18.88,
+    "end": 19.16
+  },
+  {
+    "word": "but",
+    "start": 19.36,
+    "end": 19.52
+  },
+  {
+    "word": "sometimes",
+    "start": 19.52,
+    "end": 20.16
+  },
+  {
+    "word": "noise",
+    "start": 20.16,
+    "end": 20.54
+  },
+  {
+    "word": "can",
+    "start": 20.54,
+    "end": 20.8
+  },
+  {
+    "word": "still",
+    "start": 20.8,
+    "end": 21.1
+  },
+  {
+    "word": "cause",
+    "start": 21.1,
+    "end": 21.44
+  },
+  {
+    "word": "mistakes.",
+    "start": 21.44,
+    "end": 21.94
+  },
+  {
+    "word": "Does",
+    "start": 22.68,
+    "end": 22.9
+  },
+  {
+    "word": "this",
+    "start": 22.9,
+    "end": 23.12
+  },
+  {
+    "word": "system",
+    "start": 23.12,
+    "end": 23.46
+  },
+  {
+    "word": "handle",
+    "start": 23.46,
+    "end": 23.88
+  },
+  {
+    "word": "that",
+    "start": 23.88,
+    "end": 24.12
+  },
+  {
+    "word": "well?",
+    "start": 24.12,
+    "end": 24.42
+  },
+  {
+    "word": "It",
+    "start": 24.42,
+    "end": 25.32
+  },
+  {
+    "word": "does",
+    "start": 25.32,
+    "end": 25.48
+  },
+  {
+    "word": "a",
+    "start": 25.48,
+    "end": 25.62
+  },
+  {
+    "word": "pretty",
+    "start": 25.62,
+    "end": 25.88
+  },
+  {
+    "word": "good",
+    "start": 25.88,
+    "end": 26.08
+  },
+  {
+    "word": "job",
+    "start": 26.08,
+    "end": 26.32
+  },
+  {
+    "word": "filtering",
+    "start": 26.32,
+    "end": 26.8
+  },
+  {
+    "word": "noise,",
+    "start": 26.8,
+    "end": 27.18
+  },
+  {
+    "word": "especially",
+    "start": 27.36,
+    "end": 28.0
+  },
+  {
+    "word": "with",
+    "start": 28.0,
+    "end": 28.28
+  },
+  {
+    "word": "models",
+    "start": 28.28,
+    "end": 28.62
+  },
+  {
+    "word": "that",
+    "start": 28.62,
+    "end": 28.94
+  },
+  {
+    "word": "use",
+    "start": 28.94,
+    "end": 29.22
+  },
+  {
+    "word": "voice",
+    "start": 29.22,
+    "end": 29.54
+  },
+  {
+    "word": "active.",
+    "start": 29.54,
+    "end": 29.9
+  }
+]

audio_tests/generate_transcripts.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+"""Generate word-level timestamped transcripts using faster-whisper (offline).
+
+Produces one JSON file per audio with: [{word, start, end}, ...]
+"""
+
+import json
+import os
+from faster_whisper import WhisperModel
+
+AUDIO_DIR = os.path.dirname(os.path.abspath(__file__))
+
+FILES = [
+    ("00_00_07_english_1_speaker.wav", "en"),
+    ("00_00_16_french_1_speaker.wav", "fr"),
+    ("00_00_30_english_3_speakers.wav", "en"),
+]
+
+def main():
+    print("Loading faster-whisper model (base, cpu, float32)...")
+    model = WhisperModel("base", device="cpu", compute_type="float32")
+
+    for filename, lang in FILES:
+        audio_path = os.path.join(AUDIO_DIR, filename)
+        out_path = os.path.join(
+            AUDIO_DIR, filename.rsplit(".", 1)[0] + ".transcript.json"
+        )
+
+        print(f"\n{'='*60}")
+        print(f"Transcribing: {filename} (language={lang})")
+        print(f"{'='*60}")
+
+        segments, info = model.transcribe(
+            audio_path, word_timestamps=True, language=lang
+        )
+
+        words = []
+        for segment in segments:
+            if segment.words:
+                for w in segment.words:
+                    words.append({
+                        "word": w.word.strip(),
+                        "start": round(w.start, 3),
+                        "end": round(w.end, 3),
+                    })
+                    print(f"  {w.start:6.2f} - {w.end:6.2f}  {w.word.strip()}")
+
+        with open(out_path, "w", encoding="utf-8") as f:
+            json.dump(words, f, indent=2, ensure_ascii=False)
+
+        print(f"\n  -> {len(words)} words written to {os.path.basename(out_path)}")
+
+    print("\nDone.")
+
+
+if __name__ == "__main__":
+    main()

available_models.md

@@ -1,72 +0,0 @@
-# Available model sizes:
-
-- tiny.en (english only)
-- tiny
-- base.en (english only)
-- base
-- small.en (english only)
-- small
-- medium.en (english only)
-- medium
-- large-v1
-- large-v2
-- large-v3
-- large-v3-turbo
-
-## How to choose?
-
-### Language Support
-- **English only**: Use `.en` models for better accuracy and faster processing when you only need English transcription
-- **Multilingual**: Do not use `.en` models.
-
-### Resource Constraints
-- **Limited GPU/CPU or need for very low latency**: Choose `small` or smaller models
-  - `tiny`: Fastest, lowest resource usage, acceptable quality for simple audio
-  - `base`: Good balance of speed and accuracy for basic use cases
-  - `small`: Better accuracy while still being resource-efficient
-- **Good resources available**: Use `large` models for best accuracy
-  - `large-v2`: Excellent accuracy, good multilingual support
-  - `large-v3`: Best overall accuracy and language support
-
-### Special Cases
-- **No translation needed**: Use `large-v3-turbo`
-  - Same transcription quality as `large-v2` but significantly faster
-  - **Important**: Does not translate correctly, only transcribes
-
-### Model Comparison Table
-
-| Model | Speed | Accuracy | Multilingual | Translation | Best Use Case |
-|-------|--------|----------|--------------|-------------|---------------|
-| tiny(.en) | Fastest | Basic | Yes/No | Yes/No | Real-time, low resources |
-| base(.en) | Fast | Good | Yes/No | Yes/No | Balanced performance |
-| small(.en) | Medium | Better | Yes/No | Yes/No | Quality on limited hardware |
-| medium(.en) | Slow | High | Yes/No | Yes/No | High quality, moderate resources |
-| large-v2 | Slowest | Excellent | Yes | Yes | Best overall quality |
-| large-v3 | Slowest | Excellent | Yes | Yes | Maximum accuracy |
-| large-v3-turbo | Fast | Excellent | Yes | No | Fast, high-quality transcription |
-
-### Additional Considerations
-
-**Model Performance**:
-- Accuracy improves significantly from tiny to large models
-- English-only models are ~10-15% more accurate for English audio
-- Newer versions (v2, v3) have better punctuation and formatting
-
-**Hardware Requirements**:
-- `tiny`: ~1GB VRAM
-- `base`: ~1GB VRAM  
-- `small`: ~2GB VRAM
-- `medium`: ~5GB VRAM
-- `large`: ~10GB VRAM
-
-**Audio Quality Impact**:
-- Clean, clear audio: smaller models may suffice
-- Noisy, accented, or technical audio: larger models recommended
-- Phone/low-quality audio: use at least `small` model
-
-### Quick Decision Tree
-1. English only? → Add `.en` to your choice
-2. Limited resources or need speed? → `small` or smaller
-3. Good hardware and want best quality? → `large-v3`
-4. Need fast, high-quality transcription without translation? → `large-v3-turbo`
-5. Need translation capabilities? → `large-v2` or `large-v3` (avoid turbo)

chrome-extension/README.md

@@ -0,0 +1,19 @@
+## WhisperLiveKit Chrome Extension v0.1.1
+Capture the audio of your current tab, transcribe diarize and translate it using WhisperliveKit, in Chrome and other Chromium-based browsers.
+
+> Currently, only the tab audio is captured; your microphone audio is not recorded.
+
+<img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/chrome-extension/demo-extension.png" alt="WhisperLiveKit Demo" width="730">
+
+## Running this extension
+1. Run `python scripts/sync_extension.py` to copy frontend files to the `chrome-extension` directory.
+2. Load the `chrome-extension` directory in Chrome as an unpacked extension.
+
+
+## Devs:
+- Impossible to capture audio from tabs if extension is a pannel, unfortunately: 
+- https://issues.chromium.org/issues/40926394
+- https://groups.google.com/a/chromium.org/g/chromium-extensions/c/DET2SXCFnDg
+- https://issues.chromium.org/issues/40916430
+
+- To capture microphone in an extension, there are tricks: https://github.com/justinmann/sidepanel-audio-issue , https://medium.com/@lynchee.owo/how-to-enable-microphone-access-in-chrome-extensions-by-code-924295170080 (comments)

chrome-extension/background.js

@@ -0,0 +1,9 @@
+chrome.runtime.onInstalled.addListener((details) => {
+    if (details.reason.search(/install/g) === -1) {
+        return
+    }
+    chrome.tabs.create({
+        url: chrome.runtime.getURL("welcome.html"),
+        active: true
+    })
+})

chrome-extension/manifest.json

@@ -0,0 +1,23 @@
+{
+    "manifest_version": 3,
+    "name": "WhisperLiveKit Tab Capture",
+    "version": "1.0",
+    "description": "Capture and transcribe audio from browser tabs using WhisperLiveKit.",
+    "icons": {
+        "16": "icons/icon16.png",
+        "32": "icons/icon32.png",
+        "48": "icons/icon48.png",
+        "128": "icons/icon128.png"
+    },
+    "action": {
+        "default_title": "WhisperLiveKit Tab Capture",
+        "default_popup": "live_transcription.html"
+    },
+    "permissions": [
+        "scripting",
+        "tabCapture",
+        "offscreen",
+        "activeTab",
+        "storage"
+    ]
+}

chrome-extension/requestPermissions.html

@@ -0,0 +1,12 @@
+<!DOCTYPE html>
+<html>
+  <head>
+    <title>Request Permissions</title>
+    <script src="requestPermissions.js"></script>
+  </head>
+  <body>
+    This page exists to workaround an issue with Chrome that blocks permission
+    requests from chrome extensions
+    <button id="requestMicrophone">Request Microphone</button>
+  </body>
+</html>

chrome-extension/requestPermissions.js

@@ -0,0 +1,17 @@
+/**
+ * Requests user permission for microphone access.
+ * @returns {Promise<void>} A Promise that resolves when permission is granted or rejects with an error.
+ */
+async function getUserPermission() {
+  console.log("Getting user permission for microphone access...");
+  await navigator.mediaDevices.getUserMedia({ audio: true });
+  const micPermission = await navigator.permissions.query({
+    name: "microphone",
+  });
+  if (micPermission.state == "granted") {
+    window.close();
+  }
+}
+
+// Call the function to request microphone permission
+getUserPermission();

chrome-extension/sidepanel.js

@@ -0,0 +1,29 @@
+console.log("sidepanel.js");
+
+async function run() {
+  const micPermission = await navigator.permissions.query({
+    name: "microphone",
+  });
+
+  document.getElementById(
+    "audioPermission"
+  ).innerText = `MICROPHONE: ${micPermission.state}`;
+
+  if (micPermission.state !== "granted") {
+    chrome.tabs.create({ url: "requestPermissions.html" });
+  }
+
+  const intervalId = setInterval(async () => {
+    const micPermission = await navigator.permissions.query({
+      name: "microphone",
+    });
+    if (micPermission.state === "granted") {
+      document.getElementById(
+        "audioPermission"
+      ).innerText = `MICROPHONE: ${micPermission.state}`;
+      clearInterval(intervalId);
+    }
+  }, 100);
+}
+
+void run();

docs/API.md

@@ -0,0 +1,264 @@
+# WhisperLiveKit WebSocket API Documentation
+
+> !! **Note**: The new API structure described in this document is currently under deployment. 
+This documentation is intended for devs who want to build custom frontends.
+
+WLK provides real-time speech transcription, speaker diarization, and translation through a WebSocket API. The server sends incremental updates as audio is processed, allowing clients to display live transcription results with minimal latency.
+
+---
+
+## Legacy API (Current)
+
+### Message Structure
+
+The current API sends complete state snapshots on each update (several time per second)
+
+```typescript
+{
+  "type": str,
+  "status": str,
+  "lines": [
+    {
+      "speaker": int,
+      "text": str,
+      "start": float,
+      "end": float,
+      "translation": str | null,
+      "detected_language": str
+    }
+  ],
+  "buffer_transcription": str,
+  "buffer_diarization": str,
+  "remaining_time_transcription": float,
+  "remaining_time_diarization": float
+}
+```
+
+---
+
+## New API (Under Development)
+
+### Philosophy
+
+Principles:
+
+- **Incremental Updates**: Only updates and new segments are sent
+- **Ephemeral Buffers**: Temporary, unvalidated data displayed in real-time but overwritten on next update, at speaker level
+
+
+## Message Format
+
+
+```typescript
+{
+  "type": "transcript_update",
+  "status": "active_transcription" | "no_audio_detected",
+  "segments": [
+    {
+      "id": number,
+      "speaker": number,
+      "text": string,
+      "start_speaker": float,
+      "start": float,
+      "end": float,
+      "language": string | null,
+      "translation": string,
+      "words": [
+        {
+          "text": string,
+          "start": float,
+          "end": float,
+          "validated": {
+            "text": boolean,
+            "speaker": boolean,
+          }
+        }
+      ],
+      "buffer": {
+        "transcription": string,
+        "diarization": string,
+        "translation": string
+      }
+    }
+  ],
+  "metadata": {
+    "remaining_time_transcription": float,
+    "remaining_time_diarization": float
+  }
+}
+```
+
+### Other Message Types
+
+#### Config Message (sent on connection)
+```json
+{
+  "type": "config",
+  "useAudioWorklet": true / false
+}
+```
+
+#### Ready to Stop Message (sent after processing complete)
+```json
+{
+  "type": "ready_to_stop"
+}
+```
+
+---
+
+## Field Descriptions
+
+### Segment Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | `number` | Unique identifier for this segment. Used by clients to update specific segments efficiently. |
+| `speaker` | `number` | Speaker ID (1, 2, 3...). Special value `-2` indicates silence. |
+| `text` | `string` | Validated transcription text for this update. Should be **appended** to the segment's text on the client side.  |
+| `start_speaker` | `float` | Timestamp (seconds) when this speaker segment began. |
+| `start` | `float` | Timestamp (seconds) of the first word in this update. |
+| `end` | `float` | Timestamp (seconds) of the last word in this update. |
+| `language` | `string \| null` | ISO language code (e.g., "en", "fr"). `null` until language is detected. |
+| `translation` | `string` | Validated translation text for this update. Should be **appended** to the segment's translation on the client side. |
+| `words` | `Array` | Array of word-level objects with timing and validation information. |
+| `buffer` | `Object` | Per-segment temporary buffers, see below |
+
+### Word Object
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `text` | `string` | The word text. |
+| `start` | `number` | Start timestamp (seconds) of this word. |
+| `end` | `number` | End timestamp (seconds) of this word. |
+| `validated.text` | `boolean` | Whether the transcription text has been validated. if false, word is also in buffer: transcription |
+| `validated.speaker` | `boolean` | Whether the speaker assignment has been validated. if false, word is also in buffer: diarization |
+| `validated.language` | `boolean` | Whether the language detection has been validated. if false, word is also in buffer: translation |
+
+### Buffer Object (Per-Segment)
+
+Buffers are **ephemeral**. They should be displayed to the user but not stored permanently in the frontend. Each update may contain a completely different buffer value, and previous buffer is likely to be in the next validated text.
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `transcription` | `string` | Pending transcription text. Displayed immediately but **overwritten** on next update. |
+| `diarization` | `string` | Pending diarization text (text waiting for speaker assignment). Displayed immediately but **overwritten** on next update. |
+| `translation` | `string` | Pending translation text. Displayed immediately but **overwritten** on next update. |
+
+
+### Metadata Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `remaining_time_transcription` | `float` | Seconds of audio waiting for transcription processing. |
+| `remaining_time_diarization` | `float` | Seconds of audio waiting for speaker diarization. |
+
+### Status Values
+
+| Status | Description |
+|--------|-------------|
+| `active_transcription` | Normal operation, transcription is active. |
+| `no_audio_detected` | No audio has been detected yet. |
+
+---
+
+## Update Behavior
+
+### Incremental Updates
+
+The API sends **only changed or new segments**. Clients should:
+
+1. Maintain a local map of segments by ID
+2. When receiving an update, merge/update segments by ID
+3. Render only the changed segments
+
+### Language Detection
+
+When language is detected for a segment:
+
+```jsonc
+// Update 1: No language yet
+{
+  "segments": [
+    {"id": 1, "speaker": 1, "text": "May see", "language": null}
+  ]
+}
+
+// Update 2: Same segment ID, language now detected
+{
+  "segments": [
+    {"id": 1, "speaker": 1, "text": "Merci", "language": "fr"}
+  ]
+}
+```
+
+**Client behavior**: **Replace** the existing segment with the same ID.
+
+### Buffer Behavior
+
+Buffers are **per-segment** to handle multi-speaker scenarios correctly.
+
+#### Example: Translation with diarization and translation
+
+```jsonc
+// Update 1
+{
+  "segments": [
+    {
+      "id": 1,
+      "speaker": 1,
+      "text": "Hello world, how are",
+      "translation": "",
+      "buffer": {
+        "transcription": "",
+        "diarization": " you on",
+        "translation": "Bonjour le monde"
+      }
+    }
+  ]
+}
+
+
+// ==== Frontend ====
+// <SPEAKER>1</SPEAKER>
+// <TRANSCRIPTION>Hello world, how are <DIARIZATION BUFFER> you on</DIARIZATION BUFFER></TRANSCRIPTION>
+// <TRANSLATION><TRANSLATION BUFFER>Bonjour le monde</TRANSLATION BUFFER></TRANSLATION>
+
+
+// Update 2
+{
+  "segments": [
+    {
+      "id": 1,
+      "speaker": 1,
+      "text": " you on this",
+      "translation": "Bonjour tout le monde",
+      "buffer": {
+        "transcription": "",
+        "diarization": " beautiful day",
+        "translation": ",comment"
+      }
+    },
+  ]
+}
+
+
+// ==== Frontend ====
+// <SPEAKER>1</SPEAKER>
+// <TRANSCRIPTION>Hello world, how are you on this<DIARIZATION BUFFER>  beautiful day</DIARIZATION BUFFER></TRANSCRIPTION>
+// <TRANSLATION>Bonjour tout le monde<TRANSLATION BUFFER>, comment</TRANSLATION BUFFER><TRANSLATION>
+```
+
+### Silence Segments
+
+Silence is represented with the speaker id = `-2`:
+
+```jsonc
+{
+  "id": 5,
+  "speaker": -2,
+  "text": "",
+  "start": 10.5,
+  "end": 12.3
+}
+```

docs/alignement_principles.md

@@ -0,0 +1,71 @@
+### Alignment between STT Tokens and Diarization Segments 
+
+- Example 1: The punctuation from STT and the speaker change from Diariation come in the prediction `t`
+- Example 2: The punctuation from STT comes from prediction `t`, but the speaker change from Diariation come in the prediction `t-1`
+- Example 3: The punctuation from STT comes from prediction `t-1`, but the speaker change from Diariation come in the prediction `t`
+
+> `#` Is the split between the `t-1` prediction and `t` prediction.  
+
+
+## Example 1:
+```text
+punctuations_segments : __#_______.__________________!____
+diarization_segments:
+SPK1                    __#____________
+SPK2                      #            ___________________
+-->
+ALIGNED SPK1            __#_______.
+ALIGNED SPK2              #        __________________!____
+
+t-1 output:
+SPK1:                   __#
+SPK2: NO
+DIARIZATION BUFFER: NO
+
+t output:
+SPK1:                       __#__.
+SPK2:                             __________________!____
+DIARIZATION BUFFER: No
+```
+
+## Example 2:
+```text
+punctuations_segments : _____#__.___________
+diarization_segments:
+SPK1                    ___  #
+SPK2                       __#______________
+-->
+ALIGNED SPK1            _____#__.
+ALIGNED SPK2                 #   ___________
+
+t-1 output:
+SPK1:                   ___  #
+SPK2:
+DIARIZATION BUFFER:        __#
+
+t output:
+SPK1:                      __#__.
+SPK2:                            ___________
+DIARIZATION BUFFER: No
+```
+
+## Example 3:
+```text
+punctuations_segments : ___.__#__________
+diarization_segments:
+SPK1                    ______#__
+SPK2                          #  ________
+-->
+ALIGNED SPK1            ___.  #
+ALIGNED SPK2                __#__________
+
+t-1 output:
+SPK1:                   ___.  #
+SPK2:
+DIARIZATION BUFFER:         __#
+
+t output:
+SPK1:                         #
+SPK2:                       __#___________
+DIARIZATION BUFFER: NO
+```

docs/default_and_custom_models.md

@@ -0,0 +1,106 @@
+# Models and Model Paths
+
+## Defaults
+
+**Default Whisper Model**: `base`  
+When no model is specified, WhisperLiveKit uses the `base` model, which provides a good balance of speed and accuracy for most use cases.
+
+**Default Model Cache Directory**: `~/.cache/whisper`  
+Models are automatically downloaded from OpenAI's model hub and cached in this directory. You can override this with `--model_cache_dir`.
+
+**Default Translation Model**: `600M` (NLLB-200-distilled)  
+When translation is enabled, the 600M distilled NLLB model is used by default. This provides good quality with minimal resource usage.
+
+**Default Translation Backend**: `transformers`  
+The translation backend defaults to Transformers. On Apple Silicon, this automatically uses MPS acceleration for better performance.
+
+---
+
+
+## Available Whisper model sizes:
+
+| Available Model    | Speed    | Accuracy  | Multilingual | Translation | Hardware Requirements | Best Use Case                   |
+|--------------------|----------|-----------|--------------|-------------|----------------------|----------------------------------|
+| tiny(.en)          | Fastest  | Basic     | Yes/No       | Yes/No      | ~1GB VRAM            | Real-time, low resources         |
+| base(.en)          | Fast     | Good      | Yes/No       | Yes/No      | ~1GB VRAM            | Balanced performance             |
+| small(.en)         | Medium   | Better    | Yes/No       | Yes/No      | ~2GB VRAM            | Quality on limited hardware      |
+| medium(.en)        | Slow     | High      | Yes/No       | Yes/No      | ~5GB VRAM            | High quality, moderate resources |
+| large-v2           | Slowest  | Excellent | Yes          | Yes         | ~10GB VRAM           | Good overall accuracy & language support          |
+| large-v3           | Slowest  | Excellent | Yes          | Yes         | ~10GB VRAM           | Best overall accuracy & language support                |
+| large-v3-turbo     | Fast     | Excellent | Yes          | No          | ~6GB VRAM            | Fast, high-quality transcription |
+
+
+### How to choose?
+
+#### Language Support
+- **English only**: Use `.en` (ex: `base.en`) models for better accuracy and faster processing when you only need English transcription
+- **Multilingual**: Do not use `.en` models.
+      
+#### Special Cases
+- **No translation needed**: Use `large-v3-turbo`
+  - Same transcription quality as `large-v2` but significantly faster
+  - **Important**: Does not translate correctly, only transcribes
+
+### Additional Considerations
+
+**Model Performance**:
+- Accuracy improves significantly from tiny to large models
+- English-only models are ~10-15% more accurate for English audio
+- Newer versions (v2, v3) have better punctuation and formatting
+
+**Audio Quality Impact**:
+- Clean, clear audio: smaller models may suffice
+- Noisy, accented, or technical audio: larger models recommended
+- Phone/low-quality audio: use at least `small` model
+
+_______________________
+
+
+# Custom Models:
+
+The `--model-path` parameter accepts:
+
+## File Path
+- **`.pt` / `.bin` / `.safetensor` formats** Should be openable by pytorch/safetensor.
+
+## Directory Path (recommended)
+Must contain:
+- **`.pt` / `.bin` / `.safetensor` file** (required for decoder)
+
+May optionally contain:
+- **`.bin` file** - faster-whisper model for encoder (requires faster-whisper)
+- **`weights.npz`** or **`weights.safetensors`** - for encoder (requires whisper-mlx)
+
+## Hugging Face Repo ID
+- Provide the repo ID (e.g. `openai/whisper-large-v3`) and WhisperLiveKit will download and cache the snapshot automatically. For gated repos, authenticate via `huggingface-cli login` first.
+
+To improve speed/reduce hallucinations, you may want to use `scripts/determine_alignment_heads.py` to determine the alignment heads to use for your model, and use the `--custom-alignment-heads` to pass them to WLK. If not, alignment heads are set to be all the heads of the last half layer of decoder.
+
+
+_______________________
+
+# Translation Models and Backend
+
+**Language Support**: ~200 languages
+
+## Distilled Model Sizes Available
+
+| Model | Size | Parameters | VRAM (FP16) | VRAM (INT8) | Quality |
+|-------|------|------------|-------------|-------------|---------|
+| 600M | 2.46 GB | 600M | ~1.5GB | ~800MB | Good, understandable |
+| 1.3B | 5.48 GB | 1.3B | ~3GB | ~1.5GB | Better accuracy, context |
+
+**Quality Impact**: 1.3B has ~15-25% better BLEU scores vs 600M across language pairs.
+
+## Backend Performance
+
+| Backend | Speed vs Base | Memory Usage | Quality Loss |
+|---------|---------------|--------------|--------------|
+| CTranslate2 | 6-10x faster | 40-60% less | ~5% BLEU drop |
+| Transformers | Baseline | High | None |
+| Transformers + MPS (on Apple Silicon) | 2x faster | Medium | None |
+
+**Metrics**:
+- CTranslate2: 50-100+ tokens/sec
+- Transformers: 10-30 tokens/sec
+- Apple Silicon with MPS: Up to 2x faster than CTranslate2

docs/supported_languages.md

@@ -0,0 +1,373 @@
+# Transcription: Supported Language
+
+WLK supports transcription in the following languages:
+
+| ISO Code | Language Name        |
+|----------|---------------------|
+| en       | English             |
+| zh       | Chinese             |
+| de       | German              |
+| es       | Spanish             |
+| ru       | Russian             |
+| ko       | Korean              |
+| fr       | French              |
+| ja       | Japanese            |
+| pt       | Portuguese          |
+| tr       | Turkish             |
+| pl       | Polish              |
+| ca       | Catalan             |
+| nl       | Dutch               |
+| ar       | Arabic              |
+| sv       | Swedish             |
+| it       | Italian             |
+| id       | Indonesian          |
+| hi       | Hindi               |
+| fi       | Finnish             |
+| vi       | Vietnamese          |
+| he       | Hebrew              |
+| uk       | Ukrainian           |
+| el       | Greek               |
+| ms       | Malay               |
+| cs       | Czech               |
+| ro       | Romanian            |
+| da       | Danish              |
+| hu       | Hungarian           |
+| ta       | Tamil               |
+| no       | Norwegian           |
+| th       | Thai                |
+| ur       | Urdu                |
+| hr       | Croatian            |
+| bg       | Bulgarian           |
+| lt       | Lithuanian          |
+| la       | Latin               |
+| mi       | Maori               |
+| ml       | Malayalam           |
+| cy       | Welsh               |
+| sk       | Slovak              |
+| te       | Telugu              |
+| fa       | Persian             |
+| lv       | Latvian             |
+| bn       | Bengali             |
+| sr       | Serbian             |
+| az       | Azerbaijani         |
+| sl       | Slovenian           |
+| kn       | Kannada             |
+| et       | Estonian            |
+| mk       | Macedonian          |
+| br       | Breton              |
+| eu       | Basque              |
+| is       | Icelandic           |
+| hy       | Armenian            |
+| ne       | Nepali              |
+| mn       | Mongolian           |
+| bs       | Bosnian             |
+| kk       | Kazakh              |
+| sq       | Albanian            |
+| sw       | Swahili             |
+| gl       | Galician            |
+| mr       | Marathi             |
+| pa       | Punjabi             |
+| si       | Sinhala             |
+| km       | Khmer               |
+| sn       | Shona               |
+| yo       | Yoruba              |
+| so       | Somali              |
+| af       | Afrikaans           |
+| oc       | Occitan             |
+| ka       | Georgian            |
+| be       | Belarusian          |
+| tg       | Tajik               |
+| sd       | Sindhi              |
+| gu       | Gujarati            |
+| am       | Amharic             |
+| yi       | Yiddish             |
+| lo       | Lao                 |
+| uz       | Uzbek               |
+| fo       | Faroese             |
+| ht       | Haitian Creole      |
+| ps       | Pashto              |
+| tk       | Turkmen             |
+| nn       | Nynorsk             |
+| mt       | Maltese             |
+| sa       | Sanskrit            |
+| lb       | Luxembourgish       |
+| my       | Myanmar             |
+| bo       | Tibetan             |
+| tl       | Tagalog             |
+| mg       | Malagasy            |
+| as       | Assamese            |
+| tt       | Tatar               |
+| haw      | Hawaiian            |
+| ln       | Lingala             |
+| ha       | Hausa               |
+| ba       | Bashkir             |
+| jw       | Javanese            |
+| su       | Sundanese           |
+| yue      | Cantonese           |
+
+
+# Translation: Supported Languages 
+
+WLK supports translation into **201 languages** from the FLORES-200 dataset through the [NLLW](https://github.com/QuentinFuxa/NoLanguageLeftWaiting) translation system. 
+
+## How to Specify Languages
+
+You can specify languages in **three different ways**:
+
+1. **Language Name** (case-insensitive): `"English"`, `"French"`, `"Spanish"`
+2. **ISO Language Code**: `"en"`, `"fr"`, `"es"`
+3. **NLLB Code** (FLORES-200): `"eng_Latn"`, `"fra_Latn"`, `"spa_Latn"`
+
+## Usage Examples
+
+### Command Line
+```bash
+# Using language name
+whisperlivekit-server --target-language "French"
+
+# Using ISO code
+whisperlivekit-server --target-language fr
+
+# Using NLLB code
+whisperlivekit-server --target-language fra_Latn
+```
+
+### Python API
+```python
+from nllw.translation import get_language_info
+
+# Get language information by name
+lang_info = get_language_info("French")
+print(lang_info)
+# {'name': 'French', 'nllb': 'fra_Latn', 'language_code': 'fr'}
+
+# Get language information by ISO code
+lang_info = get_language_info("fr")
+
+# Get language information by NLLB code
+lang_info = get_language_info("fra_Latn")
+
+# All three return the same result
+```
+
+## Complete Language List
+
+The following table lists all 201 supported languages with their corresponding codes:
+
+| Language Name | ISO Code | NLLB Code |
+|---------------|----------|-----------|
+| Acehnese (Arabic script) | ace_Arab | ace_Arab |
+| Acehnese (Latin script) | ace_Latn | ace_Latn |
+| Mesopotamian Arabic | acm_Arab | acm_Arab |
+| Ta'izzi-Adeni Arabic | acq_Arab | acq_Arab |
+| Tunisian Arabic | aeb_Arab | aeb_Arab |
+| Afrikaans | af | afr_Latn |
+| South Levantine Arabic | ajp_Arab | ajp_Arab |
+| Akan | ak | aka_Latn |
+| Tosk Albanian | als | als_Latn |
+| Amharic | am | amh_Ethi |
+| North Levantine Arabic | apc_Arab | apc_Arab |
+| Modern Standard Arabic | ar | arb_Arab |
+| Modern Standard Arabic (Romanized) | arb_Latn | arb_Latn |
+| Najdi Arabic | ars_Arab | ars_Arab |
+| Moroccan Arabic | ary_Arab | ary_Arab |
+| Egyptian Arabic | arz_Arab | arz_Arab |
+| Assamese | as | asm_Beng |
+| Asturian | ast | ast_Latn |
+| Awadhi | awa | awa_Deva |
+| Central Aymara | ay | ayr_Latn |
+| South Azerbaijani | azb | azb_Arab |
+| North Azerbaijani | az | azj_Latn |
+| Bashkir | ba | bak_Cyrl |
+| Bambara | bm | bam_Latn |
+| Balinese | ban | ban_Latn |
+| Belarusian | be | bel_Cyrl |
+| Bemba | bem | bem_Latn |
+| Bengali | bn | ben_Beng |
+| Bhojpuri | bho | bho_Deva |
+| Banjar (Arabic script) | bjn_Arab | bjn_Arab |
+| Banjar (Latin script) | bjn_Latn | bjn_Latn |
+| Standard Tibetan | bo | bod_Tibt |
+| Bosnian | bs | bos_Latn |
+| Buginese | bug | bug_Latn |
+| Bulgarian | bg | bul_Cyrl |
+| Catalan | ca | cat_Latn |
+| Cebuano | ceb | ceb_Latn |
+| Czech | cs | ces_Latn |
+| Chokwe | cjk | cjk_Latn |
+| Central Kurdish | ckb | ckb_Arab |
+| Crimean Tatar | crh | crh_Latn |
+| Welsh | cy | cym_Latn |
+| Danish | da | dan_Latn |
+| German | de | deu_Latn |
+| Southwestern Dinka | dik | dik_Latn |
+| Dyula | dyu | dyu_Latn |
+| Dzongkha | dz | dzo_Tibt |
+| Greek | el | ell_Grek |
+| English | en | eng_Latn |
+| Esperanto | eo | epo_Latn |
+| Estonian | et | est_Latn |
+| Basque | eu | eus_Latn |
+| Ewe | ee | ewe_Latn |
+| Faroese | fo | fao_Latn |
+| Fijian | fj | fij_Latn |
+| Finnish | fi | fin_Latn |
+| Fon | fon | fon_Latn |
+| French | fr | fra_Latn |
+| Friulian | fur-IT | fur_Latn |
+| Nigerian Fulfulde | fuv | fuv_Latn |
+| West Central Oromo | om | gaz_Latn |
+| Scottish Gaelic | gd | gla_Latn |
+| Irish | ga-IE | gle_Latn |
+| Galician | gl | glg_Latn |
+| Guarani | gn | grn_Latn |
+| Gujarati | gu-IN | guj_Gujr |
+| Haitian Creole | ht | hat_Latn |
+| Hausa | ha | hau_Latn |
+| Hebrew | he | heb_Hebr |
+| Hindi | hi | hin_Deva |
+| Chhattisgarhi | hne | hne_Deva |
+| Croatian | hr | hrv_Latn |
+| Hungarian | hu | hun_Latn |
+| Armenian | hy-AM | hye_Armn |
+| Igbo | ig | ibo_Latn |
+| Ilocano | ilo | ilo_Latn |
+| Indonesian | id | ind_Latn |
+| Icelandic | is | isl_Latn |
+| Italian | it | ita_Latn |
+| Javanese | jv | jav_Latn |
+| Japanese | ja | jpn_Jpan |
+| Kabyle | kab | kab_Latn |
+| Jingpho | kac | kac_Latn |
+| Kamba | kam | kam_Latn |
+| Kannada | kn | kan_Knda |
+| Kashmiri (Arabic script) | kas_Arab | kas_Arab |
+| Kashmiri (Devanagari script) | kas_Deva | kas_Deva |
+| Georgian | ka | kat_Geor |
+| Kazakh | kk | kaz_Cyrl |
+| Kabiyè | kbp | kbp_Latn |
+| Kabuverdianu | kea | kea_Latn |
+| Halh Mongolian | mn | khk_Cyrl |
+| Khmer | km | khm_Khmr |
+| Kikuyu | ki | kik_Latn |
+| Kinyarwanda | rw | kin_Latn |
+| Kyrgyz | ky | kir_Cyrl |
+| Kimbundu | kmb | kmb_Latn |
+| Northern Kurdish | kmr | kmr_Latn |
+| Central Kanuri (Arabic script) | knc_Arab | knc_Arab |
+| Central Kanuri (Latin script) | knc_Latn | knc_Latn |
+| Kikongo | kg | kon_Latn |
+| Korean | ko | kor_Hang |
+| Lao | lo | lao_Laoo |
+| Ligurian | lij | lij_Latn |
+| Limburgish | li | lim_Latn |
+| Lingala | ln | lin_Latn |
+| Lithuanian | lt | lit_Latn |
+| Lombard | lmo | lmo_Latn |
+| Latgalian | ltg | ltg_Latn |
+| Luxembourgish | lb | ltz_Latn |
+| Luba-Kasai | lua | lua_Latn |
+| Ganda | lg | lug_Latn |
+| Luo | luo | luo_Latn |
+| Mizo | lus | lus_Latn |
+| Standard Latvian | lv | lvs_Latn |
+| Magahi | mag | mag_Deva |
+| Maithili | mai | mai_Deva |
+| Malayalam | ml-IN | mal_Mlym |
+| Marathi | mr | mar_Deva |
+| Minangkabau (Arabic script) | min_Arab | min_Arab |
+| Minangkabau (Latin script) | min_Latn | min_Latn |
+| Macedonian | mk | mkd_Cyrl |
+| Maltese | mt | mlt_Latn |
+| Meitei (Bengali script) | mni | mni_Beng |
+| Mossi | mos | mos_Latn |
+| Maori | mi | mri_Latn |
+| Burmese | my | mya_Mymr |
+| Dutch | nl | nld_Latn |
+| Norwegian Nynorsk | nn-NO | nno_Latn |
+| Norwegian Bokmål | nb | nob_Latn |
+| Nepali | ne-NP | npi_Deva |
+| Northern Sotho | nso | nso_Latn |
+| Nuer | nus | nus_Latn |
+| Nyanja | ny | nya_Latn |
+| Occitan | oc | oci_Latn |
+| Odia | or | ory_Orya |
+| Pangasinan | pag | pag_Latn |
+| Eastern Panjabi | pa | pan_Guru |
+| Papiamento | pap | pap_Latn |
+| Southern Pashto | pbt | pbt_Arab |
+| Western Persian | fa | pes_Arab |
+| Plateau Malagasy | mg | plt_Latn |
+| Polish | pl | pol_Latn |
+| Portuguese | pt-PT | por_Latn |
+| Dari | fa-AF | prs_Arab |
+| Ayacucho Quechua | qu | quy_Latn |
+| Romanian | ro | ron_Latn |
+| Rundi | rn | run_Latn |
+| Russian | ru | rus_Cyrl |
+| Sango | sg | sag_Latn |
+| Sanskrit | sa | san_Deva |
+| Santali | sat | sat_Olck |
+| Sicilian | scn | scn_Latn |
+| Shan | shn | shn_Mymr |
+| Sinhala | si-LK | sin_Sinh |
+| Slovak | sk | slk_Latn |
+| Slovenian | sl | slv_Latn |
+| Samoan | sm | smo_Latn |
+| Shona | sn | sna_Latn |
+| Sindhi | sd | snd_Arab |
+| Somali | so | som_Latn |
+| Southern Sotho | st | sot_Latn |
+| Spanish | es-ES | spa_Latn |
+| Sardinian | sc | srd_Latn |
+| Serbian | sr | srp_Cyrl |
+| Swati | ss | ssw_Latn |
+| Sundanese | su | sun_Latn |
+| Swedish | sv-SE | swe_Latn |
+| Swahili | sw | swh_Latn |
+| Silesian | szl | szl_Latn |
+| Tamil | ta | tam_Taml |
+| Tamasheq (Latin script) | taq_Latn | taq_Latn |
+| Tamasheq (Tifinagh script) | taq_Tfng | taq_Tfng |
+| Tatar | tt-RU | tat_Cyrl |
+| Telugu | te | tel_Telu |
+| Tajik | tg | tgk_Cyrl |
+| Tagalog | tl | tgl_Latn |
+| Thai | th | tha_Thai |
+| Tigrinya | ti | tir_Ethi |
+| Tok Pisin | tpi | tpi_Latn |
+| Tswana | tn | tsn_Latn |
+| Tsonga | ts | tso_Latn |
+| Turkmen | tk | tuk_Latn |
+| Tumbuka | tum | tum_Latn |
+| Turkish | tr | tur_Latn |
+| Twi | tw | twi_Latn |
+| Central Atlas Tamazight | tzm | tzm_Tfng |
+| Uyghur | ug | uig_Arab |
+| Ukrainian | uk | ukr_Cyrl |
+| Umbundu | umb | umb_Latn |
+| Urdu | ur | urd_Arab |
+| Northern Uzbek | uz | uzn_Latn |
+| Venetian | vec | vec_Latn |
+| Vietnamese | vi | vie_Latn |
+| Waray | war | war_Latn |
+| Wolof | wo | wol_Latn |
+| Xhosa | xh | xho_Latn |
+| Eastern Yiddish | yi | ydd_Hebr |
+| Yoruba | yo | yor_Latn |
+| Yue Chinese | yue | yue_Hant |
+| Chinese (Simplified) | zh-CN | zho_Hans |
+| Chinese (Traditional) | zh-TW | zho_Hant |
+| Standard Malay | ms | zsm_Latn |
+| Zulu | zu | zul_Latn |
+
+## Special Features
+
+### Multiple Script Support
+Several languages are available in multiple scripts (e.g., Arabic and Latin):
+- **Acehnese**: Arabic (`ace_Arab`) and Latin (`ace_Latn`)
+- **Banjar**: Arabic (`bjn_Arab`) and Latin (`bjn_Latn`)
+- **Kashmiri**: Arabic (`kas_Arab`) and Devanagari (`kas_Deva`)
+- **Minangkabau**: Arabic (`min_Arab`) and Latin (`min_Latn`)
+- **Tamasheq**: Latin (`taq_Latn`) and Tifinagh (`taq_Tfng`)
+- **Central Kanuri**: Arabic (`knc_Arab`) and Latin (`knc_Latn`)

docs/technical_integration.md

@@ -0,0 +1,43 @@
+# Technical Integration Guide
+
+This document introduce how to reuse the core components when you do **not** want to ship the bundled frontend, FastAPI server, or even the provided CLI.
+
+---
+
+## 1. Runtime Components
+
+| Layer | File(s) | Purpose |
+|-------|---------|---------|
+| Transport | `whisperlivekit/basic_server.py`, any ASGI/WebSocket server | Accepts audio over WebSocket (MediaRecorder WebM or raw PCM chunks) and streams JSON updates back |
+| Audio processing | `whisperlivekit/audio_processor.py` | Buffers audio, orchestrates transcription, diarization, translation, handles FFmpeg/PCM input |
+| Engines | `whisperlivekit/core.py`, `whisperlivekit/simul_whisper/*`, `whisperlivekit/local_agreement/*` | Load models once (SimulStreaming or LocalAgreement), expose `TranscriptionEngine` and helpers |
+| Frontends | `whisperlivekit/web/*`, `chrome-extension/*` | Optional UI layers feeding the WebSocket endpoint |
+
+**Key idea:** The server boundary is just `AudioProcessor.process_audio()` for incoming bytes and the async generator returned by `AudioProcessor.create_tasks()` for outgoing updates (`FrontData`). Everything else is optional.
+
+---
+
+## 2. Running Without the Bundled Frontend
+
+1. Start the server/engine however you like:
+   ```bash
+   wlk --model small --language en --host 0.0.0.0 --port 9000
+   # or launch your own app that instantiates TranscriptionEngine(...)
+   ```
+2. Build your own client (browser, mobile, desktop) that:
+   - Opens `ws(s)://<host>:<port>/asr`
+   - Sends either MediaRecorder/Opus WebM blobs **or** raw PCM (`--pcm-input` on the server tells the client to use the AudioWorklet).
+   - Consumes the JSON payload defined in `docs/API.md`.
+
+---
+
+## 3. Running Without FastAPI
+
+`whisperlivekit/basic_server.py` is just an example. Any async framework works, as long as you:
+
+1. Create a global `TranscriptionEngine` (expensive to initialize; reuse it).
+2. Instantiate `AudioProcessor(transcription_engine=engine)` for each connection.
+3. Call `create_tasks()` to get the async generator, `process_audio()` with incoming bytes, and ensure `cleanup()` runs when the client disconnects.
+
+
+If you prefer to send compressed audio, instantiate `AudioProcessor(pcm_input=False)` and pipe encoded chunks through `FFmpegManager` transparently. Just ensure `ffmpeg` is available.

docs/troubleshooting.md

@@ -0,0 +1,140 @@
+# Troubleshooting
+
+
+## GPU drivers & cuDNN visibility
+
+### Linux error: `Unable to load libcudnn_ops.so* / cudnnCreateTensorDescriptor`
+> Reported in issue #271 (Arch/CachyOS)
+
+`faster-whisper` (used for the SimulStreaming encoder) dynamically loads cuDNN.  
+If the runtime cannot find `libcudnn_*`, verify that CUDA and cuDNN match the PyTorch build you installed:
+
+1. **Install CUDA + cuDNN** (Arch/CachyOS example):
+   ```bash
+   sudo pacman -S cuda cudnn
+   sudo ldconfig
+   ```
+2. **Make sure the shared objects are visible**:
+   ```bash
+   ls /usr/lib/libcudnn*
+   ```
+3. **Check what CUDA version PyTorch expects** and match that with the driver you installed:
+   ```bash
+   python - <<'EOF'
+   import torch
+   print(torch.version.cuda)
+   EOF
+   nvcc --version
+   ```
+4. If you installed CUDA in a non-default location, export `CUDA_HOME` and add `$CUDA_HOME/lib64` to `LD_LIBRARY_PATH`.
+
+Once the CUDA/cuDNN versions match, `whisperlivekit-server` starts normally.
+
+### Windows error: `Could not locate cudnn_ops64_9.dll`
+> Reported in issue #286 (Conda on Windows)
+
+PyTorch bundles cuDNN DLLs inside your environment (`<env>\Lib\site-packages\torch\lib`).  
+When `ctranslate2` or `faster-whisper` cannot find `cudnn_ops64_9.dll`:
+
+1. Locate the DLL shipped with PyTorch, e.g.
+   ```
+   E:\conda\envs\WhisperLiveKit\Lib\site-packages\torch\lib\cudnn_ops64_9.dll
+   ```
+2. Add that directory to your `PATH` **or** copy the `cudnn_*64_9.dll` files into a directory that is already on `PATH` (such as the environment's `Scripts/` folder).
+3. Restart the shell before launching `wlk`.
+
+Installing NVIDIA's standalone cuDNN 9.x and pointing `PATH`/`CUDNN_PATH` to it works as well, but is usually not required.
+
+---
+
+## PyTorch / CTranslate2 GPU builds
+
+### `Torch not compiled with CUDA enabled`
+> Reported in issue #284
+
+If `torch.zeros(1).cuda()` raises that assertion it means you installed a CPU-only wheel.  
+Install the GPU-enabled wheels that match your CUDA toolkit:
+
+```bash
+pip install --upgrade torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130
+```
+
+Replace `cu130` with the CUDA version supported by your driver (see [PyTorch install selector](https://pytorch.org/get-started/locally/)).  
+Validate with:
+
+```python
+import torch
+print(torch.cuda.is_available(), torch.cuda.get_device_name())
+```
+
+### `CTranslate2 device count: 0` or `Could not infer dtype of ctranslate2._ext.StorageView`
+> Follow-up in issue #284
+
+`ctranslate2` publishes separate CPU and CUDA wheels. The default `pip install ctranslate2` brings the CPU build, which makes WhisperLiveKit fall back to CPU tensors and leads to the dtype error above.
+
+1. Uninstall the CPU build: `pip uninstall -y ctranslate2`.
+2. Install the CUDA wheel that matches your toolkit (example for CUDA 13.0):
+   ```bash
+   pip install ctranslate2==4.5.0 -f https://opennmt.net/ctranslate2/whl/cu130
+   ```
+   (See the [CTranslate2 installation table](https://opennmt.net/CTranslate2/installation.html) for other CUDA versions.)
+3. Verify:
+   ```python
+   import ctranslate2
+   print("CUDA devices:", ctranslate2.get_cuda_device_count())
+   print("CUDA compute types:", ctranslate2.get_supported_compute_types("cuda", 0))
+   ```
+
+**Note for aarch64 systems (e.g., NVIDIA DGX Spark):** Pre-built CUDA wheels may not be available for all CUDA versions on ARM architectures. If the wheel installation fails, you may need to compile CTranslate2 from source with CUDA support enabled.
+
+If you intentionally want CPU inference, run `wlk --backend whisper` to avoid mixing CPU-only CTranslate2 with a GPU Torch build.
+
+---
+
+## Hopper / Blackwell (`sm_121a`) systems
+> Reported in issues #276 and #284 (NVIDIA DGX Spark)
+
+CUDA 12.1a GPUs (e.g., NVIDIA GB10 on DGX Spark) ship before some toolchains know about the architecture ID, so Triton/PTXAS need manual configuration.
+
+### Error: `ptxas fatal : Value 'sm_121a' is not defined for option 'gpu-name'`
+
+If you encounter this error after compiling CTranslate2 from source on aarch64 systems, Triton's bundled `ptxas` may not support the `sm_121a` architecture. The solution is to replace Triton's `ptxas` with the system's CUDA `ptxas`:
+
+```bash
+# Find your Python environment's Triton directory
+python -c "import triton; import os; print(os.path.dirname(triton.__file__))"
+
+# Copy the system ptxas to Triton's backend directory
+# Replace <triton_path> with the output above
+cp /usr/local/cuda/bin/ptxas <triton_path>/backends/nvidia/bin/ptxas
+```
+
+For example, in a virtual environment:
+```bash
+cp /usr/local/cuda/bin/ptxas ~/wlk/lib/python3.12/site-packages/triton/backends/nvidia/bin/ptxas
+```
+
+**Note:** On DGX Spark systems, CUDA is typically already in `PATH` (`/usr/local/cuda/bin`), so explicit `CUDA_HOME` and `PATH` exports may not be necessary. Verify with `which ptxas` before copying.
+
+### Alternative: Environment variable approach
+
+If the above doesn't work, you can try setting environment variables (though this may not resolve the `sm_121a` issue on all systems):
+
+```bash
+export CUDA_HOME="/usr/local/cuda-13.0"
+export PATH="$CUDA_HOME/bin:$PATH"
+export LD_LIBRARY_PATH="$CUDA_HOME/lib64:$LD_LIBRARY_PATH"
+
+# Tell Triton where the new ptxas lives
+export TRITON_PTXAS_PATH="$CUDA_HOME/bin/ptxas"
+
+# Force PyTorch to JIT kernels for all needed architectures
+export TORCH_CUDA_ARCH_LIST="8.0 9.0 10.0 12.0 12.1a"
+```
+
+After applying the fix, restart `wlk`. Incoming streams will now compile kernels targeting `sm_121a` without crashing.
+
+---
+
+Need help with another recurring issue? Open a GitHub discussion or PR and reference this document so we can keep it current.
+

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "whisperlivekit"
-version = "0.2.8"
+version = "0.2.19"
 description = "Real-time speech-to-text with speaker diarization using Whisper"
 readme = "README.md"
 authors = [
@@ -30,28 +30,47 @@ dependencies = [
     "fastapi",
     "librosa",
     "soundfile",
-    "faster-whisper",
     "uvicorn",
     "websockets",
     "torchaudio>=2.0.0",
     "torch>=2.0.0",
+    "huggingface-hub>=0.25.0",
+    "faster-whisper>=1.2.0",
     "tqdm",
     "tiktoken",
     'triton>=2.0.0; platform_machine == "x86_64" and (sys_platform == "linux" or sys_platform == "linux2")'
 ]
 
 [project.optional-dependencies]
-sentence = ["mosestokenizer", "wtpsplit"]
+test = ["pytest>=7.0", "pytest-asyncio>=0.21"]
+translation = ["nllw"]
+sentence_tokenizer = ["mosestokenizer", "wtpsplit"]
+voxtral-hf = ["transformers>=5.2.0", "mistral-common[audio]"]
 
 [project.urls]
 Homepage = "https://github.com/QuentinFuxa/WhisperLiveKit"
 
 [project.scripts]
 whisperlivekit-server = "whisperlivekit.basic_server:main"
+wlk = "whisperlivekit.basic_server:main"
 
 [tool.setuptools]
-packages = ["whisperlivekit", "whisperlivekit.diarization", "whisperlivekit.simul_whisper", "whisperlivekit.simul_whisper.whisper", "whisperlivekit.simul_whisper.whisper.assets", "whisperlivekit.simul_whisper.whisper.normalizers", "whisperlivekit.web", "whisperlivekit.whisper_streaming_custom"]
+packages = [
+    "whisperlivekit",
+    "whisperlivekit.diarization",
+    "whisperlivekit.simul_whisper",
+    "whisperlivekit.simul_whisper.mlx",
+    "whisperlivekit.whisper",
+    "whisperlivekit.whisper.assets",
+    "whisperlivekit.whisper.normalizers",
+    "whisperlivekit.web",
+    "whisperlivekit.local_agreement",
+    "whisperlivekit.voxtral_mlx",
+    "whisperlivekit.silero_vad_models"
+]
 
 [tool.setuptools.package-data]
 whisperlivekit = ["web/*.html", "web/*.css", "web/*.js", "web/src/*.svg"]
-"whisperlivekit.simul_whisper.whisper.assets" = ["*.tiktoken", "*.npz"]
+"whisperlivekit.whisper.assets" = ["*.tiktoken", "*.npz"]
+"whisperlivekit.whisper.normalizers" = ["*.json"]
+"whisperlivekit.silero_vad_models" = ["*.jit", "*.onnx"]

run_benchmark.py

@@ -0,0 +1,291 @@
+#!/usr/bin/env python3
+"""
+Comprehensive benchmark runner for WhisperLiveKit.
+
+Tests all available backend+policy combinations across multiple audio files,
+model sizes, and VAC on/off configurations. Outputs structured JSON that
+is consumed by the report generator.
+
+Usage:
+    python run_benchmark.py                    # full benchmark
+    python run_benchmark.py --quick            # subset (tiny models, fewer combos)
+    python run_benchmark.py --json results.json # custom output path
+"""
+
+import argparse
+import asyncio
+import gc
+import json
+import logging
+import platform
+import subprocess
+import sys
+import time
+from dataclasses import asdict
+from pathlib import Path
+
+logging.basicConfig(level=logging.WARNING, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+logger = logging.getLogger("benchmark")
+logger.setLevel(logging.INFO)
+
+# Re-use harness functions
+sys.path.insert(0, str(Path(__file__).parent))
+from test_backend_offline import (
+    AUDIO_TESTS_DIR,
+    SAMPLE_RATE,
+    TestResult,
+    create_engine,
+    discover_audio_files,
+    download_sample_audio,
+    load_audio,
+    run_test,
+)
+
+CACHE_DIR = Path(__file__).parent / ".test_cache"
+
+
+def get_system_info() -> dict:
+    """Collect system metadata for the report."""
+    info = {
+        "platform": platform.platform(),
+        "machine": platform.machine(),
+        "processor": platform.processor(),
+        "python_version": platform.python_version(),
+    }
+
+    # macOS: get chip info
+    try:
+        chip = subprocess.check_output(
+            ["sysctl", "-n", "machdep.cpu.brand_string"], text=True
+        ).strip()
+        info["cpu"] = chip
+    except Exception:
+        info["cpu"] = platform.processor()
+
+    # RAM
+    try:
+        mem_bytes = int(
+            subprocess.check_output(["sysctl", "-n", "hw.memsize"], text=True).strip()
+        )
+        info["ram_gb"] = round(mem_bytes / (1024**3))
+    except Exception:
+        info["ram_gb"] = None
+
+    # Backend versions
+    versions = {}
+    try:
+        import faster_whisper
+        versions["faster-whisper"] = faster_whisper.__version__
+    except ImportError:
+        pass
+    try:
+        import mlx_whisper  # noqa: F401
+        versions["mlx-whisper"] = "installed"
+    except ImportError:
+        pass
+    try:
+        import mlx.core as mx
+        versions["mlx"] = mx.__version__
+    except ImportError:
+        pass
+    try:
+        import transformers
+        versions["transformers"] = transformers.__version__
+    except ImportError:
+        pass
+    try:
+        import torch
+        versions["torch"] = torch.__version__
+    except ImportError:
+        pass
+
+    info["backend_versions"] = versions
+    return info
+
+
+def detect_combos(quick: bool = False) -> list:
+    """Build list of (backend, policy, model_size) combos to test."""
+    combos = []
+
+    # Model sizes to test
+    model_sizes = ["tiny", "base", "small"] if not quick else ["tiny", "base"]
+
+    # faster-whisper
+    try:
+        import faster_whisper  # noqa: F401
+        for model in model_sizes:
+            combos.append({"backend": "faster-whisper", "policy": "localagreement", "model": model})
+            combos.append({"backend": "faster-whisper", "policy": "simulstreaming", "model": model})
+    except ImportError:
+        pass
+
+    # mlx-whisper
+    try:
+        import mlx_whisper  # noqa: F401
+        for model in model_sizes:
+            combos.append({"backend": "mlx-whisper", "policy": "localagreement", "model": model})
+            combos.append({"backend": "mlx-whisper", "policy": "simulstreaming", "model": model})
+    except ImportError:
+        pass
+
+    # voxtral-mlx (single model, single policy)
+    try:
+        from whisperlivekit.voxtral_mlx import VoxtralMLXModel  # noqa: F401
+        combos.append({"backend": "voxtral-mlx", "policy": "voxtral", "model": ""})
+    except ImportError:
+        pass
+
+    # voxtral HF (single model, single policy)
+    try:
+        from transformers import AutoModelForSpeechSeq2Seq  # noqa: F401
+        combos.append({"backend": "voxtral", "policy": "voxtral", "model": ""})
+    except ImportError:
+        pass
+
+    return combos
+
+
+def collect_audio_files() -> list:
+    """Collect all benchmark audio files."""
+    files = []
+
+    # audio_tests/ directory
+    if AUDIO_TESTS_DIR.is_dir():
+        files.extend(discover_audio_files(str(AUDIO_TESTS_DIR)))
+
+    # JFK sample
+    jfk = CACHE_DIR / "jfk.wav"
+    if not jfk.exists():
+        jfk = download_sample_audio()
+    if jfk.exists():
+        files.append(jfk)
+
+    return files
+
+
+async def run_single_combo(
+    combo: dict, audio_files: list, vac: bool, lan: str, max_duration: float,
+) -> list:
+    """Run one backend+policy+model combo across all audio files."""
+    backend = combo["backend"]
+    policy = combo["policy"]
+    model = combo["model"]
+
+    results = []
+    try:
+        engine = create_engine(
+            backend=backend,
+            model_size=model,
+            lan=lan,
+            vac=vac,
+            policy=policy,
+        )
+
+        # Quiet noisy loggers
+        for mod in (
+            "whisperlivekit.audio_processor",
+            "whisperlivekit.simul_whisper",
+            "whisperlivekit.tokens_alignment",
+            "whisperlivekit.simul_whisper.align_att_base",
+            "whisperlivekit.simul_whisper.simul_whisper",
+        ):
+            logging.getLogger(mod).setLevel(logging.WARNING)
+
+        for audio_path in audio_files:
+            duration = len(load_audio(str(audio_path))) / SAMPLE_RATE
+            if duration > max_duration:
+                logger.info(f"  Skipping {audio_path.name} ({duration:.0f}s > {max_duration:.0f}s)")
+                continue
+
+            file_lan = lan
+            if "french" in audio_path.name.lower() and lan == "en":
+                file_lan = "fr"
+
+            audio = load_audio(str(audio_path))
+            result = await run_test(
+                engine, audio, chunk_ms=100, realtime=False,
+                audio_file=audio_path.name, backend=backend,
+                policy=policy, lan=file_lan,
+            )
+            # Tag with extra metadata
+            result_dict = asdict(result)
+            result_dict["model_size"] = model
+            result_dict["vac"] = vac
+            results.append(result_dict)
+
+    except Exception as e:
+        logger.error(f"  FAILED: {e}")
+        import traceback
+        traceback.print_exc()
+
+    return results
+
+
+async def run_full_benchmark(combos, audio_files, max_duration=60.0):
+    """Run all combos with VAC on and off."""
+    all_results = []
+    total = len(combos) * 2  # x2 for VAC on/off
+    idx = 0
+
+    for combo in combos:
+        for vac in [True, False]:
+            idx += 1
+            vac_str = "VAC=on" if vac else "VAC=off"
+            desc = f"{combo['backend']} / {combo['policy']}"
+            if combo["model"]:
+                desc += f" / {combo['model']}"
+            desc += f" / {vac_str}"
+
+            print(f"\n{'='*70}")
+            print(f"[{idx}/{total}] {desc}")
+            print(f"{'='*70}")
+
+            results = await run_single_combo(
+                combo, audio_files, vac=vac, lan="en", max_duration=max_duration,
+            )
+            all_results.extend(results)
+
+            # Free memory between combos
+            gc.collect()
+
+    return all_results
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run comprehensive WhisperLiveKit benchmark")
+    parser.add_argument("--quick", action="store_true", help="Quick mode: fewer models and combos")
+    parser.add_argument("--json", default="benchmark_results.json", dest="json_output", help="Output JSON path")
+    parser.add_argument("--max-duration", type=float, default=60.0, help="Max audio duration in seconds")
+    args = parser.parse_args()
+
+    system_info = get_system_info()
+    combos = detect_combos(quick=args.quick)
+    audio_files = collect_audio_files()
+
+    print(f"System: {system_info.get('cpu', 'unknown')}, {system_info.get('ram_gb', '?')}GB RAM")
+    print(f"Backends: {list(system_info['backend_versions'].keys())}")
+    print(f"Combos to test: {len(combos)} x 2 (VAC on/off) = {len(combos)*2}")
+    print(f"Audio files: {[f.name for f in audio_files]}")
+    print()
+
+    t0 = time.time()
+    all_results = asyncio.run(
+        run_full_benchmark(combos, audio_files, max_duration=args.max_duration)
+    )
+    total_time = time.time() - t0
+
+    output = {
+        "system_info": system_info,
+        "benchmark_date": time.strftime("%Y-%m-%d %H:%M"),
+        "total_benchmark_time_s": round(total_time, 1),
+        "n_combos": len(combos) * 2,
+        "n_audio_files": len(audio_files),
+        "results": all_results,
+    }
+
+    Path(args.json_output).write_text(json.dumps(output, indent=2, ensure_ascii=False))
+    print(f"\nBenchmark complete in {total_time:.0f}s. Results: {args.json_output}")
+
+
+if __name__ == "__main__":
+    main()

scripts/convert_hf_whisper.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python3
+"""
+Convert a Hugging Face style Whisper checkpoint into a WhisperLiveKit .pt file.
+
+Optionally shrink the supported audio chunk length (in seconds) by trimming the
+encoder positional embeddings and updating the stored model dimensions.
+"""
+
+import argparse
+import json
+import os
+from pathlib import Path
+from typing import Dict, Tuple
+
+import torch
+
+from whisperlivekit.whisper import _convert_hf_state_dict
+from whisperlivekit.whisper.audio import HOP_LENGTH, SAMPLE_RATE
+from whisperlivekit.whisper.model import ModelDimensions
+from whisperlivekit.whisper.utils import exact_div
+
+
+def _load_state_dict(repo_path: Path) -> Dict[str, torch.Tensor]:
+    safetensor_path = repo_path / "model.safetensors"
+    bin_path = repo_path / "pytorch_model.bin"
+
+    if safetensor_path.is_file():
+        try:
+            from safetensors.torch import load_file  # type: ignore
+        except Exception as exc:  # pragma: no cover - import guard
+            raise RuntimeError(
+                "Install safetensors to load model.safetensors "
+                "(pip install safetensors)"
+            ) from exc
+        return load_file(str(safetensor_path))
+
+    if bin_path.is_file():
+        return torch.load(bin_path, map_location="cpu")
+
+    raise FileNotFoundError(
+        f"Could not find model.safetensors or pytorch_model.bin under {repo_path}"
+    )
+
+
+def _load_config(repo_path: Path) -> Dict:
+    config_path = repo_path / "config.json"
+    if not config_path.is_file():
+        raise FileNotFoundError(
+            f"Hugging Face checkpoint at {repo_path} is missing config.json"
+        )
+    with open(config_path, "r", encoding="utf-8") as fp:
+        return json.load(fp)
+
+
+def _derive_audio_ctx(chunk_length: float) -> Tuple[int, int]:
+    n_samples = int(round(chunk_length * SAMPLE_RATE))
+    expected_samples = chunk_length * SAMPLE_RATE
+    if abs(n_samples - expected_samples) > 1e-6:
+        raise ValueError(
+            "chunk_length must align with sample rate so that "
+            "chunk_length * SAMPLE_RATE is an integer"
+        )
+    n_frames = exact_div(n_samples, HOP_LENGTH)
+    n_audio_ctx = exact_div(n_frames, 2)
+    return n_frames, n_audio_ctx
+
+
+def _build_dims(config: Dict, chunk_length: float) -> Dict:
+    base_dims = ModelDimensions(
+        n_mels=config["num_mel_bins"],
+        n_audio_ctx=config["max_source_positions"],
+        n_audio_state=config["d_model"],
+        n_audio_head=config["encoder_attention_heads"],
+        n_audio_layer=config.get("encoder_layers") or config["num_hidden_layers"],
+        n_vocab=config["vocab_size"],
+        n_text_ctx=config["max_target_positions"],
+        n_text_state=config["d_model"],
+        n_text_head=config["decoder_attention_heads"],
+        n_text_layer=config["decoder_layers"],
+    ).__dict__.copy()
+
+    _, n_audio_ctx = _derive_audio_ctx(chunk_length)
+    base_dims["n_audio_ctx"] = n_audio_ctx
+    base_dims["chunk_length"] = chunk_length
+    return base_dims
+
+
+def _trim_positional_embedding(
+    state_dict: Dict[str, torch.Tensor], target_ctx: int
+) -> None:
+    key = "encoder.positional_embedding"
+    if key not in state_dict:
+        raise KeyError(f"{key} missing from converted state dict")
+
+    tensor = state_dict[key]
+    if tensor.shape[0] < target_ctx:
+        raise ValueError(
+            f"Cannot increase encoder ctx from {tensor.shape[0]} to {target_ctx}"
+        )
+    if tensor.shape[0] == target_ctx:
+        return
+    state_dict[key] = tensor[:target_ctx].contiguous()
+
+
+def convert_checkpoint(hf_path: Path, output_path: Path, chunk_length: float) -> None:
+    state_dict = _load_state_dict(hf_path)
+    converted = _convert_hf_state_dict(state_dict)
+
+    config = _load_config(hf_path)
+    dims = _build_dims(config, chunk_length)
+
+    _trim_positional_embedding(converted, dims["n_audio_ctx"])
+
+    package = {"dims": dims, "model_state_dict": converted}
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    torch.save(package, output_path)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Convert Hugging Face Whisper checkpoint to WhisperLiveKit format."
+    )
+    parser.add_argument(
+        "hf_path",
+        type=str,
+        help="Path to the cloned Hugging Face repository (e.g. whisper-tiny.en)",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="converted-whisper.pt",
+        help="Destination path for the .pt file",
+    )
+    parser.add_argument(
+        "--chunk-length",
+        type=float,
+        default=30.0,
+        help="Audio chunk length in seconds to support (default: 30)",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    hf_path = Path(os.path.expanduser(args.hf_path)).resolve()
+    output_path = Path(os.path.expanduser(args.output)).resolve()
+
+    convert_checkpoint(hf_path, output_path, args.chunk_length)
+    print(f"Saved converted checkpoint to {output_path}")
+
+
+if __name__ == "__main__":
+    main()

scripts/determine_alignment_heads.py

@@ -0,0 +1,294 @@
+"""Determine alignment heads for a variants, such as distilled model"""
+from __future__ import annotations
+
+import argparse
+import base64
+import gzip
+import io
+import math
+import pathlib
+import sys
+from typing import List, Optional, Sequence, Tuple, Union
+
+import matplotlib.pyplot as plt
+import numpy as np
+import soundfile as sf
+import torch
+from datasets import Audio as DatasetAudio
+from datasets import load_dataset
+
+REPO_ROOT = pathlib.Path(__file__).resolve().parents[1]
+WHISPER_ROOT = REPO_ROOT / "whisper"
+
+sys.path.insert(0, str(REPO_ROOT))
+sys.path.insert(0, str(WHISPER_ROOT))
+
+from whisper import load_model
+from whisper.audio import load_audio, log_mel_spectrogram, pad_or_trim
+from whisper.tokenizer import get_tokenizer
+
+AudioInput = Union[str, pathlib.Path, np.ndarray, torch.Tensor]
+
+
+def load_dataset_clips(name, config, split, limit):
+    ds = load_dataset(name, config, split=split)
+    ds = ds.cast_column("audio", DatasetAudio(decode=False))
+    clips = []
+    for idx, row in enumerate(ds):
+        if limit is not None and idx >= limit:
+            break
+        audio_field = row["audio"]
+        transcript = row["text"]
+
+        waveform_np, _ = sf.read(io.BytesIO(audio_field["bytes"]), dtype="float32")
+        if waveform_np.ndim > 1:
+            waveform_np = waveform_np.mean(axis=1)
+        waveform = waveform_np
+        transcript = str(transcript)
+
+        clips.append((waveform, transcript))
+    return clips
+
+
+def load_clips(args):
+    return load_dataset_clips(
+        args.dataset,
+        args.dataset_config,
+        args.dataset_split,
+        args.dataset_num_samples,
+    )
+
+
+def _waveform_from_source(source: AudioInput) -> torch.Tensor:
+    waveform = torch.from_numpy(source.astype(np.float32, copy=False))
+    return waveform
+
+
+def _parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="pytorch_model.bin",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda" if torch.cuda.is_available() else "cpu",
+        help="Torch device to run on",
+    )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="librispeech_asr"
+    )
+    parser.add_argument(
+        "--dataset-config",
+        type=str,
+        default="clean" 
+    )
+    parser.add_argument(
+        "--dataset-split",
+        type=str,
+        default="validation[:1%]",
+    )
+    parser.add_argument(
+        "--dataset-num-samples",
+        type=int,
+        default=16,
+    )
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        default=1.5,
+        help="Z score threshold for a head to be selected",
+    )
+    parser.add_argument(
+        "--votes",
+        type=float,
+        default=0.75,
+        help="percentage of clips that must vote for a head",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="alignment_heads.b85",
+    )
+    parser.add_argument(
+        "--visualize-top-k",
+        type=int,
+        default=32,
+    )
+    return parser.parse_args()
+
+
+def collect_heads(
+    model,
+    tokenizer,
+    clips: Sequence[Tuple[AudioInput, str]],
+    threshold: float,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    device = model.device
+    votes = torch.zeros(model.dims.n_text_layer, model.dims.n_text_head, device=device)
+    strengths = torch.zeros_like(votes)
+
+    for audio_source, transcript in clips:
+        waveform = pad_or_trim(_waveform_from_source(audio_source))
+        mel = log_mel_spectrogram(waveform, device=device)
+
+        tokens = torch.tensor(
+            [
+                *tokenizer.sot_sequence,
+                tokenizer.no_timestamps,
+                *tokenizer.encode(transcript),
+                tokenizer.eot,
+            ],
+            device=device,
+        )
+
+        qks = [None] * model.dims.n_text_layer
+        hooks = [
+            block.cross_attn.register_forward_hook(
+                lambda _, __, outputs, index=i: qks.__setitem__(index, outputs[-1][0])
+            )
+            for i, block in enumerate(model.decoder.blocks)
+        ]
+
+        with torch.no_grad():
+            model(mel.unsqueeze(0), tokens.unsqueeze(0))
+
+        for hook in hooks:
+            hook.remove()
+
+        for layer_idx, tensor in enumerate(qks):
+            if tensor is None:
+                continue
+            tensor = tensor[:, :, : mel.shape[-1] // 2]
+            tensor = tensor.softmax(dim=-1)
+            peak = tensor.max(dim=-1).values  # [heads, tokens]
+            strengths[layer_idx] += peak.mean(dim=-1)
+            zscore = (peak - peak.mean(dim=-1, keepdim=True)) / (
+                peak.std(dim=-1, keepdim=True, unbiased=False) + 1e-6
+            )
+            mask = (zscore > 3).any(dim=-1)
+            votes[layer_idx] += mask.float()
+
+    votes /= len(clips)
+    strengths /= len(clips)
+    return votes, strengths
+
+
+def _select_heads_for_visualization(selection, strengths, top_k):
+    selected = torch.nonzero(selection, as_tuple=False)
+    if selected.numel() == 0:
+        return []
+
+    entries = [
+        (int(layer.item()), int(head.item()), float(strengths[layer, head].item()))
+        for layer, head in selected
+    ]
+    entries.sort(key=lambda item: item[2], reverse=True)
+    return entries[:top_k]
+
+def _extract_heatmaps(
+    model,
+    tokenizer,
+    clip: Tuple[AudioInput, str],
+    heads: Sequence[Tuple[int, int, float]],
+) -> dict:
+    if not heads:
+        return {}
+
+    target_map = {}
+    for layer, head, _ in heads:
+        target_map.setdefault(layer, set()).add(head)
+
+    waveform = pad_or_trim(_waveform_from_source(clip[0]))
+    mel = log_mel_spectrogram(waveform, device=model.device)
+    transcript = clip[1]
+    tokens = torch.tensor(
+        [
+            *tokenizer.sot_sequence,
+            tokenizer.no_timestamps,
+            *tokenizer.encode(transcript),
+            tokenizer.eot,
+        ],
+        device=model.device,
+    )
+
+    QKs = [None] * model.dims.n_text_layer
+    hooks = [
+        block.cross_attn.register_forward_hook(
+            lambda _, __, outputs, index=i: QKs.__setitem__(index, outputs[-1][0])
+        )
+        for i, block in enumerate(model.decoder.blocks)
+    ]
+
+    with torch.no_grad():
+        model(mel.unsqueeze(0), tokens.unsqueeze(0))
+
+    for hook in hooks:
+        hook.remove()
+
+    heatmaps = {}
+    for layer_idx, tensor in enumerate(QKs):
+        if tensor is None or layer_idx not in target_map:
+            continue
+        tensor = tensor[:, :, : mel.shape[-1] // 2]
+        tensor = tensor.softmax(dim=-1).cpu()
+        for head_idx in target_map[layer_idx]:
+            heatmaps[(layer_idx, head_idx)] = tensor[head_idx]
+
+    return heatmaps
+
+
+def _plot_heatmaps(
+    heads, heatmaps, output_path):
+    cols = min(3, len(heads))
+    rows = math.ceil(len(heads) / cols)
+    fig, axes = plt.subplots(rows, cols, figsize=(4 * cols, 3.2 * rows), squeeze=False)
+
+    for idx, (layer, head, score) in enumerate(heads):
+        ax = axes[idx // cols][idx % cols]
+        mat = heatmaps.get((layer, head))
+        if mat is None:
+            ax.axis("off")
+            continue
+        im = ax.imshow(mat.to(torch.float32).numpy(), aspect="auto", origin="lower")
+        ax.set_title(f"L{layer} H{head} · score {score:.2f}")
+        ax.set_xlabel("time")
+        ax.set_ylabel("tokens")
+
+    for j in range(len(heads), rows * cols):
+        axes[j // cols][j % cols].axis("off")
+
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+def _dump_mask(mask: torch.Tensor, output_path: str):
+    payload = mask.numpy().astype(np.bool_)
+    blob = base64.b85encode(gzip.compress(payload.tobytes()))
+    with open(output_path, "wb") as f:
+        f.write(blob)
+
+
+def main():
+    args = _parse_args()
+    model = load_model(args.model, device=args.device)
+    model.eval()
+    tokenizer = get_tokenizer(multilingual=model.is_multilingual)
+    clips = load_clips(args)
+
+    votes, strengths = collect_heads(model, tokenizer, clips, args.threshold)
+    # selection = votes > 0.5
+    selection = strengths > 0.05
+    _dump_mask(selection.cpu(), args.output)
+
+    viz_heads = _select_heads_for_visualization(selection, strengths, args.visualize_top_k)
+    heatmaps = _extract_heatmaps(model, tokenizer, clips[0], viz_heads)
+    _plot_heatmaps(viz_heads, heatmaps, "alignment_heads.png")
+
+if __name__ == "__main__":
+    main()

scripts/sync_extension.py

@@ -0,0 +1,40 @@
+"""Copy core files from web directory to Chrome extension directory."""
+
+import os
+import shutil
+from pathlib import Path
+
+
+def sync_extension_files():
+    
+    web_dir = Path("whisperlivekit/web")
+    extension_dir = Path("chrome-extension")
+    
+    files_to_sync = [
+        "live_transcription.html", "live_transcription.js", "live_transcription.css"
+    ]
+
+    svg_files = [
+        "system_mode.svg",
+        "light_mode.svg", 
+        "dark_mode.svg",
+        "settings.svg"
+    ]
+        
+    for file in files_to_sync:
+        src_path = web_dir / file
+        dest_path = extension_dir / file
+        
+        dest_path.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(src_path, dest_path)
+    
+    for svg_file in svg_files:
+        src_path = web_dir / "src" / svg_file
+        dest_path = extension_dir / "web" / "src" / svg_file
+        dest_path.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(src_path, dest_path)
+    
+
+if __name__ == "__main__":
+
+    sync_extension_files()

test_backend_offline.py

@@ -0,0 +1,783 @@
+#!/usr/bin/env python3
+"""
+Offline test harness and benchmark suite for WhisperLiveKit backends.
+
+Simulates a client-server session by feeding audio files as PCM bytes through
+the full AudioProcessor pipeline (the same path used by the WebSocket server),
+without needing a browser or microphone.
+
+Computes WER (Word Error Rate) and timestamp accuracy when ground truth
+transcript files (.transcript.json) are available alongside audio files.
+
+Usage:
+    # Test with a single audio file:
+    python test_backend_offline.py --backend faster-whisper --audio audio_tests/00_00_07_english_1_speaker.wav
+
+    # Test all files in audio_tests/:
+    python test_backend_offline.py --backend faster-whisper --no-realtime
+
+    # Override streaming policy:
+    python test_backend_offline.py --backend faster-whisper --policy simulstreaming --no-realtime
+
+    # Multi-backend benchmark (auto-detects all installed backends):
+    python test_backend_offline.py --benchmark --no-realtime
+
+    # Export results as JSON:
+    python test_backend_offline.py --benchmark --no-realtime --json results.json
+
+    # Insert silence for testing silence handling:
+    python test_backend_offline.py --backend faster-whisper --insert-silence 3.0 2.0
+"""
+
+import argparse
+import asyncio
+import json
+import logging
+import sys
+import time
+import urllib.request
+from pathlib import Path
+from dataclasses import dataclass, asdict, field
+from typing import List, Optional
+
+import numpy as np
+
+logging.basicConfig(
+    level=logging.WARNING,
+    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
+)
+logger = logging.getLogger("test_offline")
+logger.setLevel(logging.INFO)
+
+SAMPLE_RATE = 16000
+JFK_WAV_URL = "https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav"
+CACHE_DIR = Path(__file__).parent / ".test_cache"
+AUDIO_TESTS_DIR = Path(__file__).parent / "audio_tests"
+AUDIO_EXTENSIONS = {".wav", ".mp3", ".flac", ".ogg", ".m4a"}
+
+
+@dataclass
+class WordTimestamp:
+    """Word with its start/end time."""
+    word: str
+    start: float
+    end: float
+
+
+@dataclass
+class TestResult:
+    """Structured result from a single test run."""
+    audio_file: str
+    audio_duration_s: float
+    backend: str
+    policy: str
+    language: str
+    chunk_ms: int
+    realtime_pacing: bool
+    # Timing
+    processing_time_s: float
+    rtf: float  # real-time factor
+    # Transcription output
+    transcription: str
+    n_lines: int
+    n_responses: int
+    # WER metrics (None if no ground truth)
+    wer: Optional[float] = None
+    wer_details: Optional[dict] = None
+    # Timestamp accuracy (None if no ground truth)
+    timestamp_mae: Optional[float] = None
+    timestamp_max_delta: Optional[float] = None
+    timestamp_median_delta: Optional[float] = None
+    # Word-level timestamps
+    word_timestamps: List[WordTimestamp] = field(default_factory=list)
+    # Raw last response
+    last_response: Optional[dict] = None
+
+
+def download_sample_audio() -> Path:
+    """Download the jfk.wav sample if not cached."""
+    CACHE_DIR.mkdir(exist_ok=True)
+    path = CACHE_DIR / "jfk.wav"
+    if not path.exists():
+        logger.info(f"Downloading sample audio to {path} ...")
+        urllib.request.urlretrieve(JFK_WAV_URL, path)
+        logger.info("Done.")
+    return path
+
+
+def load_audio(path: str) -> np.ndarray:
+    """Load audio file as float32 mono 16kHz numpy array.
+
+    Supports WAV, FLAC (via soundfile) and MP3, OGG, M4A (via librosa).
+    """
+    ext = Path(path).suffix.lower()
+    if ext in (".mp3", ".ogg", ".m4a"):
+        import librosa
+        audio, _ = librosa.load(path, sr=SAMPLE_RATE, mono=True)
+        return audio.astype(np.float32)
+
+    import soundfile as sf
+    audio, sr = sf.read(path, dtype="float32")
+    if audio.ndim > 1:
+        audio = audio.mean(axis=1)
+    if sr != SAMPLE_RATE:
+        import librosa
+        audio = librosa.resample(audio, orig_sr=sr, target_sr=SAMPLE_RATE)
+    return audio
+
+
+def insert_silence(audio: np.ndarray, silence_sec: float, position_sec: float) -> np.ndarray:
+    """Insert silence into audio at a given position.
+
+    Args:
+        audio: Float32 mono audio array at SAMPLE_RATE.
+        silence_sec: Duration of silence to insert in seconds.
+        position_sec: Position in seconds where silence starts.
+    Returns:
+        New audio array with silence inserted.
+    """
+    pos_samples = int(position_sec * SAMPLE_RATE)
+    silence_samples = int(silence_sec * SAMPLE_RATE)
+    pos_samples = min(pos_samples, len(audio))
+    silence = np.zeros(silence_samples, dtype=np.float32)
+    return np.concatenate([audio[:pos_samples], silence, audio[pos_samples:]])
+
+
+def float32_to_s16le_bytes(audio: np.ndarray) -> bytes:
+    """Convert float32 audio to s16le PCM bytes (what the browser sends)."""
+    return (audio * 32768).clip(-32768, 32767).astype(np.int16).tobytes()
+
+
+def create_engine(
+    backend: str, model_size: str, lan: str,
+    diarization: bool = False, vac: bool = True, policy: str = "",
+):
+    """Create a TranscriptionEngine with the given backend config."""
+    import gc
+    from whisperlivekit.core import TranscriptionEngine
+
+    # Reset singleton so we get a fresh instance
+    TranscriptionEngine._instance = None
+    TranscriptionEngine._initialized = False
+    gc.collect()
+
+    kwargs = dict(
+        backend=backend,
+        lan=lan,
+        pcm_input=True,
+        vac=vac,
+        transcription=True,
+        diarization=diarization,
+    )
+    if model_size:
+        kwargs["model_size"] = model_size
+    if policy:
+        kwargs["backend_policy"] = policy
+
+    return TranscriptionEngine(**kwargs)
+
+
+def _extract_text_from_response(response_dict: dict) -> str:
+    """Extract full transcription text from a FrontData dict."""
+    segments = response_dict.get("lines", [])
+    full_text = " ".join(
+        seg.get("text", "").strip()
+        for seg in segments
+        if seg.get("text", "").strip()
+    )
+    buf = response_dict.get("buffer_transcription", "").strip()
+    if buf:
+        full_text = f"{full_text} {buf}".strip() if full_text else buf
+    return full_text
+
+
+async def run_test(
+    engine, audio: np.ndarray, chunk_ms: int, realtime: bool,
+    audio_file: str = "", backend: str = "", policy: str = "", lan: str = "",
+) -> TestResult:
+    """
+    Simulate a client session through the full AudioProcessor pipeline.
+
+    1. Create AudioProcessor (one per "client session")
+    2. Start async pipeline (transcription_processor, results_formatter, etc.)
+    3. Feed audio as PCM bytes in timed chunks
+    4. Collect and display FrontData responses
+    5. Signal EOF and cleanup
+    """
+    from whisperlivekit.audio_processor import AudioProcessor
+
+    chunk_samples = int(SAMPLE_RATE * chunk_ms / 1000)
+    total_samples = len(audio)
+    audio_duration = total_samples / SAMPLE_RATE
+
+    logger.info(
+        f"Audio: {audio_duration:.2f}s | "
+        f"Chunk: {chunk_ms}ms ({chunk_samples} samples) | "
+        f"Steps: {total_samples // chunk_samples + 1} | "
+        f"Realtime: {realtime}"
+    )
+
+    # --- Server side: create processor and start pipeline ---
+    processor = AudioProcessor(transcription_engine=engine)
+    results_generator = await processor.create_tasks()
+
+    # Collect results in background (like handle_websocket_results)
+    all_responses = []
+    response_count = 0
+    last_printed_text = ""
+
+    async def collect_results():
+        nonlocal response_count, last_printed_text
+        async for response in results_generator:
+            all_responses.append(response)
+            response_count += 1
+            d = response.to_dict()
+
+            # Only print when transcription text actually changes
+            current_text = _extract_text_from_response(d)
+            if current_text and current_text != last_printed_text:
+                buf = d.get("buffer_transcription", "").strip()
+                committed = current_text
+                if buf and committed.endswith(buf):
+                    committed = committed[:-len(buf)].strip()
+
+                # Show committed text + buffer separately
+                display = committed
+                if buf:
+                    display = f"{committed} \033[90m{buf}\033[0m" if committed else f"\033[90m{buf}\033[0m"
+                print(f"  > {display}", flush=True)
+                last_printed_text = current_text
+
+    result_task = asyncio.create_task(collect_results())
+
+    # --- Client side: feed audio as PCM bytes ---
+    t_start = time.time()
+
+    for offset in range(0, total_samples, chunk_samples):
+        chunk = audio[offset : offset + chunk_samples]
+        pcm_bytes = float32_to_s16le_bytes(chunk)
+        await processor.process_audio(pcm_bytes)
+        if realtime:
+            await asyncio.sleep(chunk_ms / 1000)
+
+    feed_elapsed = time.time() - t_start
+
+    logger.info(f"Audio fed in {feed_elapsed:.2f}s. Signaling EOF...")
+
+    # Signal end of audio (like client disconnect / empty message)
+    await processor.process_audio(None)
+
+    # Wait for pipeline to drain completely
+    try:
+        await asyncio.wait_for(result_task, timeout=120.0)
+    except asyncio.TimeoutError:
+        logger.warning("Timed out waiting for results. Proceeding with cleanup.")
+        result_task.cancel()
+        try:
+            await result_task
+        except asyncio.CancelledError:
+            pass
+
+    # --- Capture word-level timestamps before cleanup ---
+    word_timestamps = []
+    try:
+        state = await processor.get_current_state()
+        for token in state.tokens:
+            if hasattr(token, 'start') and hasattr(token, 'text') and token.text:
+                word_timestamps.append(WordTimestamp(
+                    word=token.text.strip(),
+                    start=round(token.start, 3),
+                    end=round(token.end, 3),
+                ))
+    except Exception as e:
+        logger.warning(f"Could not capture word timestamps: {e}")
+
+    # Cleanup
+    await processor.cleanup()
+
+    total_elapsed = time.time() - t_start
+
+    # --- Build result ---
+    transcription = ""
+    n_lines = 0
+    last_response_dict = None
+
+    if all_responses:
+        last = all_responses[-1].to_dict()
+        last_response_dict = last
+        n_lines = len(last.get("lines", []))
+        transcription = _extract_text_from_response(last)
+
+    # --- Compute WER and timestamp accuracy against ground truth ---
+    from whisperlivekit.metrics import compute_wer, compute_timestamp_accuracy
+
+    wer_val = None
+    wer_details = None
+    ts_mae = None
+    ts_max_delta = None
+    ts_median_delta = None
+
+    gt_path = Path(audio_file).with_suffix(".transcript.json")
+    if not gt_path.exists():
+        gt_path = AUDIO_TESTS_DIR / gt_path
+    gt = None
+    if gt_path.exists():
+        with open(gt_path) as f:
+            gt = json.load(f)
+
+        # WER
+        gt_text = " ".join(w["word"] for w in gt)
+        wer_result = compute_wer(gt_text, transcription)
+        wer_val = round(wer_result["wer"], 4)
+        wer_details = wer_result
+
+        # Timestamp accuracy
+        if word_timestamps:
+            pred_dicts = [{"word": wt.word, "start": wt.start, "end": wt.end} for wt in word_timestamps]
+            ts_result = compute_timestamp_accuracy(pred_dicts, gt)
+            ts_mae = ts_result["mae_start"]
+            ts_max_delta = ts_result["max_delta_start"]
+            ts_median_delta = ts_result["median_delta_start"]
+
+    result = TestResult(
+        audio_file=audio_file,
+        audio_duration_s=round(audio_duration, 2),
+        backend=backend,
+        policy=policy,
+        language=lan,
+        chunk_ms=chunk_ms,
+        realtime_pacing=realtime,
+        processing_time_s=round(total_elapsed, 2),
+        rtf=round(total_elapsed / audio_duration, 2),
+        transcription=transcription,
+        n_lines=n_lines,
+        n_responses=response_count,
+        wer=wer_val,
+        wer_details=wer_details,
+        timestamp_mae=round(ts_mae, 3) if ts_mae is not None else None,
+        timestamp_max_delta=round(ts_max_delta, 3) if ts_max_delta is not None else None,
+        timestamp_median_delta=round(ts_median_delta, 3) if ts_median_delta is not None else None,
+        word_timestamps=word_timestamps,
+        last_response=last_response_dict,
+    )
+
+    # --- Print summary ---
+    print(f"\n{'=' * 60}")
+    print(f"RESULT: {audio_file}")
+    print(f"{'=' * 60}")
+    print(f"Transcription: {transcription}")
+    print(f"Lines: {n_lines} | Responses: {response_count}")
+    print(f"Audio: {audio_duration:.2f}s | Time: {total_elapsed:.2f}s | RTF: {result.rtf:.2f}x")
+
+    if wer_val is not None:
+        print(f"WER: {wer_val:.2%} (S={wer_details['substitutions']} I={wer_details['insertions']} D={wer_details['deletions']})")
+
+    # Print word timestamps if available
+    if word_timestamps:
+        print(f"\nWord timestamps ({len(word_timestamps)} words):")
+        for wt in word_timestamps:
+            print(f"  [{wt.start:6.2f} - {wt.end:6.2f}] {wt.word}")
+
+        # Detailed comparison with ground truth
+        if gt:
+            print(f"\n  vs Ground truth ({len(gt)} words):")
+            max_words = max(len(word_timestamps), len(gt))
+            for i in range(max_words):
+                pred = word_timestamps[i] if i < len(word_timestamps) else None
+                ref = gt[i] if i < len(gt) else None
+                p_str = f"[{pred.start:5.2f}-{pred.end:5.2f}] {pred.word:<15}" if pred else " " * 30
+                r_str = f"[{ref['start']:5.2f}-{ref['end']:5.2f}] {ref['word']:<15}" if ref else ""
+                delta = ""
+                if pred and ref:
+                    d = pred.start - ref['start']
+                    delta = f"  Δstart={d:+.2f}"
+                print(f"  {p_str}  |  {r_str}{delta}")
+
+        if ts_mae is not None:
+            print(f"\n  Timestamp stats: MAE={ts_mae:.3f}s  max|Δ|={ts_max_delta:.3f}s  median|Δ|={ts_median_delta:.3f}s")
+
+    print(f"{'=' * 60}")
+
+    return result
+
+
+def discover_audio_files(directory: str) -> List[Path]:
+    """Find all supported audio files in directory."""
+    d = Path(directory)
+    files = sorted(
+        p for p in d.iterdir()
+        if p.is_file() and p.suffix.lower() in AUDIO_EXTENSIONS
+    )
+    return files
+
+
+async def run_all_tests(
+    engine, audio_files: List[Path], chunk_ms: int, realtime: bool,
+    backend: str, policy: str, lan: str, max_duration: float = 60.0,
+    silence_insertions: Optional[List[List[float]]] = None,
+) -> List[TestResult]:
+    """Run tests on multiple audio files sequentially."""
+    results = []
+    for audio_path in audio_files:
+        # Detect language from filename if "french" in name
+        file_lan = lan
+        if "french" in audio_path.name.lower() and lan == "en":
+            file_lan = "fr"
+            logger.info(f"Auto-detected language 'fr' from filename")
+
+        audio = load_audio(str(audio_path))
+
+        # Insert silence segments (applied in reverse position order to keep offsets valid)
+        if silence_insertions:
+            for secs, at_sec in sorted(silence_insertions, key=lambda x: x[1], reverse=True):
+                logger.info(f"Inserting {secs:.1f}s silence at {at_sec:.1f}s")
+                audio = insert_silence(audio, secs, at_sec)
+
+        duration = len(audio) / SAMPLE_RATE
+
+        if duration > max_duration:
+            logger.info(f"Skipping {audio_path.name} ({duration:.0f}s > {max_duration:.0f}s max)")
+            continue
+
+        print(f"\n{'#' * 60}")
+        print(f"# Testing: {audio_path.name} ({duration:.1f}s)")
+        print(f"{'#' * 60}")
+
+        result = await run_test(
+            engine, audio, chunk_ms, realtime,
+            audio_file=audio_path.name, backend=backend, policy=policy, lan=file_lan,
+        )
+        results.append(result)
+
+    return results
+
+
+def print_benchmark_summary(results: List[TestResult]):
+    """Print a tabular summary of all test results."""
+    print(f"\n{'=' * 110}")
+    print("BENCHMARK SUMMARY")
+    print(f"{'=' * 110}")
+    print(
+        f"{'File':<40} {'Duration':>8} {'Time':>8} {'RTF':>6} "
+        f"{'WER':>7} {'MAE(s)':>7} {'Lines':>5}"
+    )
+    print(f"{'-' * 110}")
+    for r in results:
+        wer_str = f"{r.wer:.2%}" if r.wer is not None else "  -"
+        mae_str = f"{r.timestamp_mae:.3f}" if r.timestamp_mae is not None else "  -"
+        print(
+            f"{r.audio_file:<40} {r.audio_duration_s:>7.1f}s {r.processing_time_s:>7.1f}s "
+            f"{r.rtf:>5.2f}x {wer_str:>7} {mae_str:>7} {r.n_lines:>5}"
+        )
+    print(f"{'-' * 110}")
+    total_audio = sum(r.audio_duration_s for r in results)
+    total_time = sum(r.processing_time_s for r in results)
+    avg_rtf = total_time / total_audio if total_audio > 0 else 0
+    wer_vals = [r.wer for r in results if r.wer is not None]
+    avg_wer_str = f"{sum(wer_vals)/len(wer_vals):.2%}" if wer_vals else "  -"
+    mae_vals = [r.timestamp_mae for r in results if r.timestamp_mae is not None]
+    avg_mae_str = f"{sum(mae_vals)/len(mae_vals):.3f}" if mae_vals else "  -"
+    print(
+        f"{'TOTAL/AVG':<40} {total_audio:>7.1f}s {total_time:>7.1f}s "
+        f"{avg_rtf:>5.2f}x {avg_wer_str:>7} {avg_mae_str:>7}"
+    )
+    print(f"{'=' * 110}")
+
+    # Print transcription excerpts
+    print(f"\nTRANSCRIPTIONS:")
+    print(f"{'-' * 110}")
+    for r in results:
+        excerpt = r.transcription[:120] + "..." if len(r.transcription) > 120 else r.transcription
+        print(f"  {r.audio_file}:")
+        print(f"    {excerpt}")
+    print(f"{'=' * 110}")
+
+
+def detect_available_backends() -> List[dict]:
+    """Probe which backends can be imported and return (backend, policy) combos.
+
+    Returns list of dicts with keys: backend, policy, description.
+    """
+    combos = []
+
+    # faster-whisper
+    try:
+        import faster_whisper  # noqa: F401
+        combos.append({"backend": "faster-whisper", "policy": "localagreement", "description": "faster-whisper + LocalAgreement"})
+        combos.append({"backend": "faster-whisper", "policy": "simulstreaming", "description": "faster-whisper + SimulStreaming"})
+    except ImportError:
+        pass
+
+    # mlx-whisper (macOS only)
+    try:
+        import mlx_whisper  # noqa: F401
+        combos.append({"backend": "mlx-whisper", "policy": "localagreement", "description": "mlx-whisper + LocalAgreement"})
+        combos.append({"backend": "mlx-whisper", "policy": "simulstreaming", "description": "mlx-whisper + SimulStreaming"})
+    except ImportError:
+        pass
+
+    # openai-whisper
+    try:
+        import whisper  # noqa: F401
+        combos.append({"backend": "whisper", "policy": "localagreement", "description": "openai-whisper + LocalAgreement"})
+        combos.append({"backend": "whisper", "policy": "simulstreaming", "description": "openai-whisper + SimulStreaming"})
+    except ImportError:
+        pass
+
+    # voxtral-mlx
+    try:
+        from whisperlivekit.voxtral_mlx import VoxtralMLXModel  # noqa: F401
+        combos.append({"backend": "voxtral-mlx", "policy": "voxtral", "description": "voxtral-mlx (MLX)"})
+    except ImportError:
+        pass
+
+    # voxtral (HuggingFace)
+    try:
+        from transformers import AutoModelForSpeechSeq2Seq  # noqa: F401
+        combos.append({"backend": "voxtral", "policy": "voxtral", "description": "voxtral (HuggingFace)"})
+    except ImportError:
+        pass
+
+    return combos
+
+
+def print_cross_backend_comparison(all_results: List[TestResult]):
+    """Print a comparison table across backends and policies."""
+    print(f"\n{'=' * 110}")
+    print("CROSS-BACKEND BENCHMARK COMPARISON")
+    print(f"{'=' * 110}")
+    print(
+        f"{'Backend':<18} {'Policy':<16} {'File':<30} "
+        f"{'WER':>7} {'RTF':>6} {'MAE(s)':>7} {'MaxΔ(s)':>8}"
+    )
+    print(f"{'-' * 110}")
+
+    for r in all_results:
+        wer_str = f"{r.wer:.2%}" if r.wer is not None else "  -"
+        rtf_str = f"{r.rtf:.2f}x"
+        mae_str = f"{r.timestamp_mae:.3f}" if r.timestamp_mae is not None else "  -"
+        max_str = f"{r.timestamp_max_delta:.3f}" if r.timestamp_max_delta is not None else "  -"
+        # Truncate filename for readability
+        fname = r.audio_file[:28] + ".." if len(r.audio_file) > 30 else r.audio_file
+        print(
+            f"{r.backend:<18} {r.policy:<16} {fname:<30} "
+            f"{wer_str:>7} {rtf_str:>6} {mae_str:>7} {max_str:>8}"
+        )
+
+    print(f"{'-' * 110}")
+
+    # Per-backend averages
+    from collections import defaultdict
+    by_combo = defaultdict(list)
+    for r in all_results:
+        by_combo[(r.backend, r.policy)].append(r)
+
+    print(f"\n{'Backend':<18} {'Policy':<16} {'Avg WER':>8} {'Avg RTF':>8} {'Avg MAE':>8} {'Files':>6}")
+    print(f"{'-' * 80}")
+    for (backend, policy), group in sorted(by_combo.items()):
+        wer_vals = [r.wer for r in group if r.wer is not None]
+        rtf_vals = [r.rtf for r in group]
+        mae_vals = [r.timestamp_mae for r in group if r.timestamp_mae is not None]
+        avg_wer = f"{sum(wer_vals)/len(wer_vals):.2%}" if wer_vals else "  -"
+        avg_rtf = f"{sum(rtf_vals)/len(rtf_vals):.2f}x"
+        avg_mae = f"{sum(mae_vals)/len(mae_vals):.3f}" if mae_vals else "  -"
+        print(
+            f"{backend:<18} {policy:<16} {avg_wer:>8} {avg_rtf:>8} {avg_mae:>8} {len(group):>6}"
+        )
+    print(f"{'=' * 110}")
+
+
+def _quiet_loggers(verbose: bool):
+    """Set internal module log levels to reduce noise."""
+    if verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+    else:
+        for mod in (
+            "whisperlivekit.audio_processor", "whisperlivekit.simul_whisper",
+            "whisperlivekit.tokens_alignment", "whisperlivekit.simul_whisper.align_att_base",
+            "whisperlivekit.simul_whisper.simul_whisper",
+        ):
+            logging.getLogger(mod).setLevel(logging.WARNING)
+
+
+async def run_benchmark(
+    audio_files: List[Path], chunk_ms: int, realtime: bool,
+    model_size: str, lan: str, max_duration: float, vac: bool,
+    verbose: bool,
+) -> List[TestResult]:
+    """Run benchmark across all available backend+policy combinations."""
+    combos = detect_available_backends()
+    if not combos:
+        logger.error("No backends available. Install at least one ASR backend.")
+        return []
+
+    logger.info(f"Detected {len(combos)} backend+policy combinations:")
+    for c in combos:
+        logger.info(f"  - {c['description']}")
+
+    all_results = []
+    for i, combo in enumerate(combos, 1):
+        backend = combo["backend"]
+        policy = combo["policy"]
+        desc = combo["description"]
+
+        print(f"\n{'*' * 70}")
+        print(f"* BENCHMARK {i}/{len(combos)}: {desc}")
+        print(f"{'*' * 70}")
+
+        try:
+            engine = create_engine(
+                backend, model_size, lan, vac=vac, policy=policy,
+            )
+            _quiet_loggers(verbose)
+
+            results = await run_all_tests(
+                engine, audio_files, chunk_ms, realtime,
+                backend=backend, policy=policy, lan=lan,
+                max_duration=max_duration,
+            )
+            all_results.extend(results)
+        except Exception as e:
+            logger.error(f"Failed to run {desc}: {e}")
+            import traceback
+            traceback.print_exc()
+
+    return all_results
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Offline backend test harness (AudioProcessor-level)"
+    )
+    parser.add_argument(
+        "--backend", default="faster-whisper",
+        help="Backend: voxtral, voxtral-mlx, auto, faster-whisper, mlx-whisper, whisper.",
+    )
+    parser.add_argument(
+        "--policy", default="",
+        help="Override backend policy: localagreement, simulstreaming, voxtral.",
+    )
+    parser.add_argument(
+        "--audio", default=None,
+        help="Path to a single audio file (WAV, MP3, FLAC, etc.).",
+    )
+    parser.add_argument(
+        "--audio-dir", default=None,
+        help="Directory of audio files to test. Defaults to audio_tests/ if neither --audio nor --audio-dir given.",
+    )
+    parser.add_argument(
+        "--chunk-ms", type=int, default=100,
+        help="Chunk size in milliseconds (simulates real-time interval).",
+    )
+    parser.add_argument(
+        "--model", default="", dest="model_size",
+        help="Model size or HF repo ID.",
+    )
+    parser.add_argument("--lan", default="en", help="Language code.")
+    parser.add_argument(
+        "--no-realtime", action="store_true",
+        help="Skip real-time pacing between chunks (faster but less realistic).",
+    )
+    parser.add_argument(
+        "--no-vac", action="store_true",
+        help="Disable Voice Activity Classification (send all audio without silence filtering).",
+    )
+    parser.add_argument(
+        "--diarization", action="store_true",
+        help="Enable speaker diarization.",
+    )
+    parser.add_argument(
+        "--benchmark", action="store_true",
+        help="Run benchmark across all detected backend+policy combinations.",
+    )
+    parser.add_argument(
+        "--json", default=None, dest="json_output",
+        help="Write structured JSON results to this file.",
+    )
+    parser.add_argument(
+        "--max-duration", type=float, default=60.0,
+        help="Skip audio files longer than this many seconds (default: 60).",
+    )
+    parser.add_argument(
+        "--insert-silence", nargs=2, type=float, metavar=("SECS", "AT_SEC"),
+        action="append", default=[],
+        help="Insert SECS of silence at AT_SEC position. Can be repeated. "
+             "E.g.: --insert-silence 3.0 2.0 --insert-silence 5.0 7.0",
+    )
+    parser.add_argument(
+        "-v", "--verbose", action="store_true",
+        help="Show debug-level logs from all components.",
+    )
+    args = parser.parse_args()
+
+    realtime = not args.no_realtime
+    vac = not args.no_vac
+
+    # Resolve audio file(s)
+    if args.audio:
+        audio_files = [Path(args.audio)]
+    elif args.audio_dir:
+        audio_files = discover_audio_files(args.audio_dir)
+    elif AUDIO_TESTS_DIR.is_dir():
+        audio_files = discover_audio_files(str(AUDIO_TESTS_DIR))
+    else:
+        # Fall back to jfk.wav download
+        audio_files = [download_sample_audio()]
+
+    if not audio_files:
+        logger.error("No audio files found.")
+        sys.exit(1)
+
+    logger.info(f"Audio files: {[f.name for f in audio_files]}")
+
+    if args.benchmark:
+        # --- Multi-backend benchmark mode ---
+        all_results = asyncio.run(
+            run_benchmark(
+                audio_files, args.chunk_ms, realtime,
+                args.model_size, args.lan, args.max_duration, vac,
+                args.verbose,
+            )
+        )
+        if all_results:
+            print_cross_backend_comparison(all_results)
+        results = all_results
+    else:
+        # --- Single-backend mode ---
+        policy = args.policy
+        logger.info(f"Creating {args.backend} engine...")
+        engine = create_engine(
+            args.backend, args.model_size, args.lan,
+            diarization=args.diarization, vac=vac, policy=policy,
+        )
+        logger.info("Engine ready.")
+
+        _quiet_loggers(args.verbose)
+
+        results = asyncio.run(
+            run_all_tests(
+                engine, audio_files, args.chunk_ms, realtime,
+                args.backend, policy, args.lan,
+                max_duration=args.max_duration,
+                silence_insertions=args.insert_silence or None,
+            )
+        )
+
+        if len(results) > 1:
+            print_benchmark_summary(results)
+
+    # JSON output
+    if args.json_output and results:
+        json_results = []
+        for r in results:
+            d = asdict(r)
+            d.pop("last_response", None)  # too verbose for summary
+            json_results.append(d)
+        Path(args.json_output).write_text(
+            json.dumps(json_results, indent=2, ensure_ascii=False)
+        )
+        logger.info(f"Results written to {args.json_output}")
+
+
+if __name__ == "__main__":
+    main()

tests/conftest.py

@@ -0,0 +1,58 @@
+"""Shared pytest fixtures for WhisperLiveKit tests."""
+
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+from whisperlivekit.timed_objects import ASRToken, Silence, Transcript
+
+
+AUDIO_TESTS_DIR = Path(__file__).parent.parent / "audio_tests"
+
+
+@pytest.fixture
+def sample_tokens():
+    """A short sequence of ASRToken objects."""
+    return [
+        ASRToken(start=0.0, end=0.5, text="Hello"),
+        ASRToken(start=0.5, end=1.0, text=" world"),
+        ASRToken(start=1.0, end=1.5, text=" test."),
+    ]
+
+
+@pytest.fixture
+def sample_silence():
+    """A completed silence event."""
+    s = Silence(start=1.5, end=3.0, is_starting=False, has_ended=True)
+    s.compute_duration()
+    return s
+
+
+@pytest.fixture
+def mock_args():
+    """Minimal args namespace for AudioProcessor tests."""
+    return SimpleNamespace(
+        diarization=False,
+        transcription=True,
+        target_language="",
+        vac=False,
+        vac_chunk_size=0.04,
+        min_chunk_size=0.1,
+        pcm_input=True,
+        punctuation_split=False,
+        backend="faster-whisper",
+        backend_policy="localagreement",
+        vad=True,
+    )
+
+
+@pytest.fixture
+def ground_truth_en():
+    """Ground truth transcript for the 7s English audio (if available)."""
+    path = AUDIO_TESTS_DIR / "00_00_07_english_1_speaker.transcript.json"
+    if path.exists():
+        with open(path) as f:
+            return json.load(f)
+    return None

tests/test_audio_processor.py

@@ -0,0 +1,209 @@
+"""Tests for AudioProcessor pipeline with mocked ASR backends.
+
+These tests verify the async audio processing pipeline works correctly
+without requiring any real ASR models to be loaded.
+"""
+
+import asyncio
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+from whisperlivekit.timed_objects import ASRToken, Transcript
+
+
+# ---------------------------------------------------------------------------
+# Mock ASR components
+# ---------------------------------------------------------------------------
+
+class MockASR:
+    """Mock ASR model holder."""
+    sep = " "
+    SAMPLING_RATE = 16000
+
+    def __init__(self):
+        self.transcribe_kargs = {}
+        self.original_language = "en"
+        self.backend_choice = "mock"
+
+    def transcribe(self, audio):
+        return None
+
+
+class MockOnlineProcessor:
+    """Mock online processor that returns canned tokens."""
+    SAMPLING_RATE = 16000
+
+    def __init__(self, asr=None):
+        self.asr = asr or MockASR()
+        self.audio_buffer = np.array([], dtype=np.float32)
+        self.end = 0.0
+        self._call_count = 0
+        self._finished = False
+
+    def insert_audio_chunk(self, audio, audio_stream_end_time):
+        self.audio_buffer = np.append(self.audio_buffer, audio)
+        self.end = audio_stream_end_time
+
+    def process_iter(self, is_last=False):
+        self._call_count += 1
+        # Emit a token on every call when we have audio
+        if len(self.audio_buffer) > 0:
+            t = self._call_count * 0.5
+            return [ASRToken(start=t, end=t + 0.5, text=f"word{self._call_count}")], self.end
+        return [], self.end
+
+    def get_buffer(self):
+        return Transcript(start=None, end=None, text="")
+
+    def start_silence(self):
+        return [], self.end
+
+    def end_silence(self, silence_duration, offset):
+        pass
+
+    def new_speaker(self, change_speaker):
+        pass
+
+    def finish(self):
+        self._finished = True
+        return [], self.end
+
+    def warmup(self, audio, init_prompt=""):
+        pass
+
+
+def _make_pcm_bytes(duration_s=0.1, sample_rate=16000):
+    """Generate silent PCM s16le bytes."""
+    n_samples = int(duration_s * sample_rate)
+    audio = np.zeros(n_samples, dtype=np.float32)
+    return (audio * 32768).clip(-32768, 32767).astype(np.int16).tobytes()
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+@pytest.fixture
+def mock_engine():
+    """Create a mock TranscriptionEngine-like object."""
+    engine = SimpleNamespace(
+        asr=MockASR(),
+        diarization_model=None,
+        translation_model=None,
+        args=SimpleNamespace(
+            diarization=False,
+            transcription=True,
+            target_language="",
+            vac=False,
+            vac_chunk_size=0.04,
+            min_chunk_size=0.1,
+            pcm_input=True,
+            punctuation_split=False,
+            backend="mock",
+            backend_policy="localagreement",
+            vad=True,
+            model_size="base",
+            lan="en",
+        ),
+    )
+    return engine
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+class TestPCMConversion:
+    """Test PCM byte conversion without needing the full pipeline."""
+
+    def test_s16le_roundtrip(self):
+        """Convert float32 → s16le → float32 and verify approximate roundtrip."""
+        original = np.array([0.0, 0.5, -0.5, 1.0, -1.0], dtype=np.float32)
+        s16 = (original * 32768).clip(-32768, 32767).astype(np.int16)
+        pcm_bytes = s16.tobytes()
+        # Direct numpy conversion (same logic as AudioProcessor.convert_pcm_to_float)
+        recovered = np.frombuffer(pcm_bytes, dtype=np.int16).astype(np.float32) / 32768.0
+
+        np.testing.assert_allclose(recovered, original, atol=1 / 32768)
+
+
+@pytest.mark.asyncio
+class TestPipelineBasics:
+    async def test_feed_audio_and_get_responses(self, mock_engine):
+        """Feed audio through the pipeline and verify we get responses."""
+        from whisperlivekit.audio_processor import AudioProcessor
+
+        with patch("whisperlivekit.audio_processor.online_factory", return_value=MockOnlineProcessor()):
+            processor = AudioProcessor(transcription_engine=mock_engine)
+            results_gen = await processor.create_tasks()
+
+            responses = []
+
+            async def collect():
+                async for resp in results_gen:
+                    responses.append(resp)
+
+            task = asyncio.create_task(collect())
+
+            # Feed 2 seconds of audio in 100ms chunks
+            for _ in range(20):
+                await processor.process_audio(_make_pcm_bytes(0.1))
+
+            # Signal EOF
+            await processor.process_audio(None)
+
+            await asyncio.wait_for(task, timeout=10.0)
+            await processor.cleanup()
+
+            # We should have gotten at least one response
+            assert len(responses) > 0
+
+    async def test_eof_terminates_pipeline(self, mock_engine):
+        """Sending None (EOF) should cleanly terminate the pipeline."""
+        from whisperlivekit.audio_processor import AudioProcessor
+
+        with patch("whisperlivekit.audio_processor.online_factory", return_value=MockOnlineProcessor()):
+            processor = AudioProcessor(transcription_engine=mock_engine)
+            results_gen = await processor.create_tasks()
+
+            responses = []
+
+            async def collect():
+                async for resp in results_gen:
+                    responses.append(resp)
+
+            task = asyncio.create_task(collect())
+
+            # Send a small amount of audio then EOF
+            await processor.process_audio(_make_pcm_bytes(0.5))
+            await processor.process_audio(None)
+
+            await asyncio.wait_for(task, timeout=10.0)
+            await processor.cleanup()
+
+            # Pipeline should have terminated without error
+            assert task.done()
+
+    async def test_empty_audio_no_crash(self, mock_engine):
+        """Sending EOF immediately (no audio) should not crash."""
+        from whisperlivekit.audio_processor import AudioProcessor
+
+        with patch("whisperlivekit.audio_processor.online_factory", return_value=MockOnlineProcessor()):
+            processor = AudioProcessor(transcription_engine=mock_engine)
+            results_gen = await processor.create_tasks()
+
+            responses = []
+
+            async def collect():
+                async for resp in results_gen:
+                    responses.append(resp)
+
+            task = asyncio.create_task(collect())
+            await processor.process_audio(None)
+
+            await asyncio.wait_for(task, timeout=10.0)
+            await processor.cleanup()
+            assert task.done()

tests/test_config.py

@@ -0,0 +1,99 @@
+"""Tests for WhisperLiveKitConfig."""
+
+import logging
+from types import SimpleNamespace
+
+import pytest
+
+from whisperlivekit.config import WhisperLiveKitConfig
+
+
+class TestDefaults:
+    def test_default_backend(self):
+        c = WhisperLiveKitConfig()
+        assert c.backend == "auto"
+
+    def test_default_policy(self):
+        c = WhisperLiveKitConfig()
+        assert c.backend_policy == "simulstreaming"
+
+    def test_default_language(self):
+        c = WhisperLiveKitConfig()
+        assert c.lan == "auto"
+
+    def test_default_vac(self):
+        c = WhisperLiveKitConfig()
+        assert c.vac is True
+
+    def test_default_model_size(self):
+        c = WhisperLiveKitConfig()
+        assert c.model_size == "base"
+
+    def test_default_transcription(self):
+        c = WhisperLiveKitConfig()
+        assert c.transcription is True
+        assert c.diarization is False
+
+
+class TestPostInit:
+    def test_en_model_forces_english(self):
+        c = WhisperLiveKitConfig(model_size="tiny.en")
+        assert c.lan == "en"
+
+    def test_en_suffix_with_auto_language(self):
+        c = WhisperLiveKitConfig(model_size="base.en", lan="auto")
+        assert c.lan == "en"
+
+    def test_non_en_model_keeps_language(self):
+        c = WhisperLiveKitConfig(model_size="base", lan="fr")
+        assert c.lan == "fr"
+
+    def test_policy_alias_1(self):
+        c = WhisperLiveKitConfig(backend_policy="1")
+        assert c.backend_policy == "simulstreaming"
+
+    def test_policy_alias_2(self):
+        c = WhisperLiveKitConfig(backend_policy="2")
+        assert c.backend_policy == "localagreement"
+
+    def test_policy_no_alias(self):
+        c = WhisperLiveKitConfig(backend_policy="localagreement")
+        assert c.backend_policy == "localagreement"
+
+
+class TestFromNamespace:
+    def test_known_keys(self):
+        ns = SimpleNamespace(backend="faster-whisper", lan="en", model_size="large-v3")
+        c = WhisperLiveKitConfig.from_namespace(ns)
+        assert c.backend == "faster-whisper"
+        assert c.lan == "en"
+        assert c.model_size == "large-v3"
+
+    def test_ignores_unknown_keys(self):
+        ns = SimpleNamespace(backend="auto", unknown_key="value", another="x")
+        c = WhisperLiveKitConfig.from_namespace(ns)
+        assert c.backend == "auto"
+        assert not hasattr(c, "unknown_key")
+
+    def test_preserves_defaults_for_missing(self):
+        ns = SimpleNamespace(backend="voxtral-mlx")
+        c = WhisperLiveKitConfig.from_namespace(ns)
+        assert c.lan == "auto"
+        assert c.vac is True
+
+
+class TestFromKwargs:
+    def test_known_keys(self):
+        c = WhisperLiveKitConfig.from_kwargs(backend="mlx-whisper", lan="fr")
+        assert c.backend == "mlx-whisper"
+        assert c.lan == "fr"
+
+    def test_warns_on_unknown_keys(self, caplog):
+        with caplog.at_level(logging.WARNING, logger="whisperlivekit.config"):
+            c = WhisperLiveKitConfig.from_kwargs(backend="auto", bogus="value")
+        assert c.backend == "auto"
+        assert "bogus" in caplog.text
+
+    def test_post_init_runs(self):
+        c = WhisperLiveKitConfig.from_kwargs(model_size="small.en")
+        assert c.lan == "en"

tests/test_hypothesis_buffer.py

@@ -0,0 +1,172 @@
+"""Tests for HypothesisBuffer — the core of LocalAgreement policy."""
+
+import pytest
+
+from whisperlivekit.timed_objects import ASRToken
+from whisperlivekit.local_agreement.online_asr import HypothesisBuffer
+
+
+def make_tokens(words, start=0.0, step=0.5):
+    """Helper: create ASRToken list from word strings."""
+    tokens = []
+    t = start
+    for w in words:
+        tokens.append(ASRToken(start=t, end=t + step, text=w, probability=0.9))
+        t += step
+    return tokens
+
+
+class TestInsert:
+    def test_basic_insert(self):
+        buf = HypothesisBuffer()
+        tokens = make_tokens(["hello", "world"])
+        buf.insert(tokens, offset=0.0)
+        assert len(buf.new) == 2
+        assert buf.new[0].text == "hello"
+
+    def test_insert_with_offset(self):
+        buf = HypothesisBuffer()
+        tokens = make_tokens(["hello"], start=0.0)
+        buf.insert(tokens, offset=5.0)
+        assert buf.new[0].start == pytest.approx(5.0)
+
+    def test_insert_filters_old_tokens(self):
+        buf = HypothesisBuffer()
+        buf.last_committed_time = 10.0
+        tokens = make_tokens(["old", "new"], start=5.0, step=3.0)
+        buf.insert(tokens, offset=0.0)
+        # "old" at 5.0 is before last_committed_time - 0.1 = 9.9 → filtered
+        # "new" at 8.0 is also before 9.9 → filtered
+        assert len(buf.new) == 0
+
+    def test_insert_deduplicates_committed(self):
+        buf = HypothesisBuffer()
+        # Commit "hello"
+        tokens1 = make_tokens(["hello", "world"])
+        buf.insert(tokens1, offset=0.0)
+        buf.flush()  # commits "hello" (buffer was empty, so nothing matches)
+        # Actually with empty buffer, flush won't commit anything
+        # Let's do it properly: two rounds
+        buf2 = HypothesisBuffer()
+        first = make_tokens(["hello", "world"])
+        buf2.insert(first, offset=0.0)
+        buf2.flush()  # buffer was empty → no commits, buffer = ["hello", "world"]
+
+        second = make_tokens(["hello", "world", "test"])
+        buf2.insert(second, offset=0.0)
+        committed = buf2.flush()
+        # LCP of ["hello", "world"] and ["hello", "world", "test"] = ["hello", "world"]
+        assert len(committed) == 2
+        assert committed[0].text == "hello"
+        assert committed[1].text == "world"
+
+
+class TestFlush:
+    def test_flush_empty(self):
+        buf = HypothesisBuffer()
+        committed = buf.flush()
+        assert committed == []
+
+    def test_flush_lcp_matching(self):
+        buf = HypothesisBuffer()
+        # Round 1: establish buffer
+        buf.insert(make_tokens(["hello", "world"]), offset=0.0)
+        buf.flush()  # buffer = ["hello", "world"], committed = []
+
+        # Round 2: same prefix, new suffix
+        buf.insert(make_tokens(["hello", "world", "test"]), offset=0.0)
+        committed = buf.flush()
+        assert [t.text for t in committed] == ["hello", "world"]
+
+    def test_flush_no_match(self):
+        buf = HypothesisBuffer()
+        # Round 1
+        buf.insert(make_tokens(["hello", "world"]), offset=0.0)
+        buf.flush()
+
+        # Round 2: completely different
+        buf.insert(make_tokens(["foo", "bar"]), offset=0.0)
+        committed = buf.flush()
+        assert committed == []
+
+    def test_flush_partial_match(self):
+        buf = HypothesisBuffer()
+        buf.insert(make_tokens(["hello", "world", "test"]), offset=0.0)
+        buf.flush()
+
+        buf.insert(make_tokens(["hello", "earth", "again"]), offset=0.0)
+        committed = buf.flush()
+        assert len(committed) == 1
+        assert committed[0].text == "hello"
+
+    def test_flush_updates_last_committed(self):
+        buf = HypothesisBuffer()
+        buf.insert(make_tokens(["hello", "world"]), offset=0.0)
+        buf.flush()
+
+        buf.insert(make_tokens(["hello", "world", "test"]), offset=0.0)
+        buf.flush()
+        assert buf.last_committed_word == "world"
+        assert buf.last_committed_time > 0
+
+    def test_flush_with_confidence_validation(self):
+        buf = HypothesisBuffer(confidence_validation=True)
+        high_conf = [
+            ASRToken(start=0.0, end=0.5, text="sure", probability=0.99),
+            ASRToken(start=0.5, end=1.0, text="maybe", probability=0.5),
+        ]
+        buf.insert(high_conf, offset=0.0)
+        committed = buf.flush()
+        # "sure" has p>0.95 → committed immediately
+        assert len(committed) == 1
+        assert committed[0].text == "sure"
+
+
+class TestPopCommitted:
+    def test_pop_removes_old(self):
+        buf = HypothesisBuffer()
+        buf.committed_in_buffer = make_tokens(["a", "b", "c"], start=0.0, step=1.0)
+        # "a": end=1.0, "b": end=2.0, "c": end=3.0
+        # pop_committed removes tokens with end <= time
+        buf.pop_committed(2.0)
+        # "a" (end=1.0) and "b" (end=2.0) removed, "c" (end=3.0) remains
+        assert len(buf.committed_in_buffer) == 1
+        assert buf.committed_in_buffer[0].text == "c"
+
+    def test_pop_nothing(self):
+        buf = HypothesisBuffer()
+        buf.committed_in_buffer = make_tokens(["a", "b"], start=5.0)
+        buf.pop_committed(0.0)
+        assert len(buf.committed_in_buffer) == 2
+
+    def test_pop_all(self):
+        buf = HypothesisBuffer()
+        buf.committed_in_buffer = make_tokens(["a", "b"], start=0.0, step=0.5)
+        buf.pop_committed(100.0)
+        assert len(buf.committed_in_buffer) == 0
+
+
+class TestStreamingSimulation:
+    """Multi-round insert/flush simulating real streaming behavior."""
+
+    def test_three_rounds(self):
+        buf = HypothesisBuffer()
+        all_committed = []
+
+        # Round 1: "this is"
+        buf.insert(make_tokens(["this", "is"]), offset=0.0)
+        all_committed.extend(buf.flush())
+
+        # Round 2: "this is a test"
+        buf.insert(make_tokens(["this", "is", "a", "test"]), offset=0.0)
+        all_committed.extend(buf.flush())
+
+        # Round 3: "this is a test today"
+        buf.insert(make_tokens(["this", "is", "a", "test", "today"]), offset=0.0)
+        all_committed.extend(buf.flush())
+
+        words = [t.text for t in all_committed]
+        assert "this" in words
+        assert "is" in words
+        assert "a" in words
+        assert "test" in words

tests/test_metrics.py

@@ -0,0 +1,183 @@
+"""Tests for whisperlivekit.metrics — WER, timestamp accuracy, normalization."""
+
+import pytest
+
+from whisperlivekit.metrics import compute_wer, compute_timestamp_accuracy, normalize_text
+
+
+class TestNormalizeText:
+    def test_lowercase(self):
+        assert normalize_text("Hello World") == "hello world"
+
+    def test_strip_punctuation(self):
+        assert normalize_text("Hello, world!") == "hello world"
+
+    def test_collapse_whitespace(self):
+        assert normalize_text("  hello   world  ") == "hello world"
+
+    def test_keep_hyphens(self):
+        assert normalize_text("real-time") == "real-time"
+
+    def test_keep_apostrophes(self):
+        assert normalize_text("don't") == "don't"
+
+    def test_unicode_normalized(self):
+        # e + combining accent should be same as precomposed
+        assert normalize_text("caf\u0065\u0301") == normalize_text("caf\u00e9")
+
+    def test_empty(self):
+        assert normalize_text("") == ""
+
+    def test_only_punctuation(self):
+        assert normalize_text("...!?") == ""
+
+
+class TestComputeWER:
+    def test_perfect_match(self):
+        result = compute_wer("hello world", "hello world")
+        assert result["wer"] == 0.0
+        assert result["substitutions"] == 0
+        assert result["insertions"] == 0
+        assert result["deletions"] == 0
+
+    def test_case_insensitive(self):
+        result = compute_wer("Hello World", "hello world")
+        assert result["wer"] == 0.0
+
+    def test_punctuation_ignored(self):
+        result = compute_wer("Hello, world!", "hello world")
+        assert result["wer"] == 0.0
+
+    def test_one_substitution(self):
+        result = compute_wer("hello world", "hello earth")
+        assert result["wer"] == pytest.approx(0.5)
+        assert result["substitutions"] == 1
+
+    def test_one_insertion(self):
+        result = compute_wer("hello world", "hello big world")
+        assert result["wer"] == pytest.approx(0.5)
+        assert result["insertions"] == 1
+
+    def test_one_deletion(self):
+        result = compute_wer("hello big world", "hello world")
+        assert result["wer"] == pytest.approx(1 / 3)
+        assert result["deletions"] == 1
+
+    def test_completely_different(self):
+        result = compute_wer("the cat sat", "a dog ran")
+        assert result["wer"] == pytest.approx(1.0)
+
+    def test_empty_reference(self):
+        result = compute_wer("", "hello")
+        assert result["wer"] == 1.0  # 1 insertion / 0 ref → treated as float(m)
+        assert result["ref_words"] == 0
+
+    def test_empty_hypothesis(self):
+        result = compute_wer("hello world", "")
+        assert result["wer"] == pytest.approx(1.0)
+        assert result["deletions"] == 2
+
+    def test_both_empty(self):
+        result = compute_wer("", "")
+        assert result["wer"] == 0.0
+
+    def test_ref_and_hyp_word_counts(self):
+        result = compute_wer("one two three", "one two three four")
+        assert result["ref_words"] == 3
+        assert result["hyp_words"] == 4
+
+
+class TestComputeTimestampAccuracy:
+    def test_perfect_match(self):
+        words = [
+            {"word": "hello", "start": 0.0, "end": 0.5},
+            {"word": "world", "start": 0.5, "end": 1.0},
+        ]
+        result = compute_timestamp_accuracy(words, words)
+        assert result["mae_start"] == 0.0
+        assert result["max_delta_start"] == 0.0
+        assert result["n_matched"] == 2
+
+    def test_constant_offset(self):
+        ref = [
+            {"word": "hello", "start": 0.0, "end": 0.5},
+            {"word": "world", "start": 0.5, "end": 1.0},
+        ]
+        pred = [
+            {"word": "hello", "start": 0.1, "end": 0.6},
+            {"word": "world", "start": 0.6, "end": 1.1},
+        ]
+        result = compute_timestamp_accuracy(pred, ref)
+        assert result["mae_start"] == pytest.approx(0.1)
+        assert result["max_delta_start"] == pytest.approx(0.1)
+        assert result["n_matched"] == 2
+
+    def test_mismatched_word_counts(self):
+        ref = [
+            {"word": "hello", "start": 0.0, "end": 0.5},
+            {"word": "beautiful", "start": 0.5, "end": 1.0},
+            {"word": "world", "start": 1.0, "end": 1.5},
+        ]
+        pred = [
+            {"word": "hello", "start": 0.0, "end": 0.5},
+            {"word": "world", "start": 1.1, "end": 1.6},
+        ]
+        result = compute_timestamp_accuracy(pred, ref)
+        assert result["n_matched"] == 2
+        assert result["n_ref"] == 3
+        assert result["n_pred"] == 2
+
+    def test_empty_predicted(self):
+        ref = [{"word": "hello", "start": 0.0, "end": 0.5}]
+        result = compute_timestamp_accuracy([], ref)
+        assert result["mae_start"] is None
+        assert result["n_matched"] == 0
+
+    def test_empty_reference(self):
+        pred = [{"word": "hello", "start": 0.0, "end": 0.5}]
+        result = compute_timestamp_accuracy(pred, [])
+        assert result["mae_start"] is None
+        assert result["n_matched"] == 0
+
+    def test_case_insensitive_matching(self):
+        ref = [{"word": "Hello", "start": 0.0, "end": 0.5}]
+        pred = [{"word": "hello", "start": 0.1, "end": 0.6}]
+        result = compute_timestamp_accuracy(pred, ref)
+        assert result["n_matched"] == 1
+        assert result["mae_start"] == pytest.approx(0.1)
+
+    def test_median_even_count(self):
+        """Median with even number of matched words should average the two middle values."""
+        ref = [
+            {"word": "a", "start": 0.0, "end": 0.2},
+            {"word": "b", "start": 0.5, "end": 0.7},
+            {"word": "c", "start": 1.0, "end": 1.2},
+            {"word": "d", "start": 1.5, "end": 1.7},
+        ]
+        pred = [
+            {"word": "a", "start": 0.1, "end": 0.3},   # delta 0.1
+            {"word": "b", "start": 0.7, "end": 0.9},   # delta 0.2
+            {"word": "c", "start": 1.3, "end": 1.5},   # delta 0.3
+            {"word": "d", "start": 1.9, "end": 2.1},   # delta 0.4
+        ]
+        result = compute_timestamp_accuracy(pred, ref)
+        assert result["n_matched"] == 4
+        # sorted abs deltas: [0.1, 0.2, 0.3, 0.4] -> median = (0.2 + 0.3) / 2 = 0.25
+        assert result["median_delta_start"] == pytest.approx(0.25)
+
+    def test_median_odd_count(self):
+        """Median with odd number of matched words takes the middle value."""
+        ref = [
+            {"word": "a", "start": 0.0, "end": 0.2},
+            {"word": "b", "start": 0.5, "end": 0.7},
+            {"word": "c", "start": 1.0, "end": 1.2},
+        ]
+        pred = [
+            {"word": "a", "start": 0.1, "end": 0.3},   # delta 0.1
+            {"word": "b", "start": 0.8, "end": 1.0},   # delta 0.3
+            {"word": "c", "start": 1.2, "end": 1.4},   # delta 0.2
+        ]
+        result = compute_timestamp_accuracy(pred, ref)
+        assert result["n_matched"] == 3
+        # sorted abs deltas: [0.1, 0.2, 0.3] -> median = 0.2
+        assert result["median_delta_start"] == pytest.approx(0.2)

tests/test_silence_handling.py

@@ -0,0 +1,99 @@
+"""Tests for silence handling — state machine and double-counting regression."""
+
+import pytest
+
+from whisperlivekit.timed_objects import Silence
+
+
+class TestSilenceStateMachine:
+    """Test Silence object state transitions."""
+
+    def test_initial_state(self):
+        s = Silence(start=1.0, is_starting=True)
+        assert s.is_starting is True
+        assert s.has_ended is False
+        assert s.duration is None
+        assert s.end is None
+
+    def test_end_silence(self):
+        s = Silence(start=1.0, is_starting=True)
+        s.end = 3.0
+        s.is_starting = False
+        s.has_ended = True
+        s.compute_duration()
+        assert s.duration == pytest.approx(2.0)
+
+    def test_very_short_silence(self):
+        s = Silence(start=1.0, end=1.01, is_starting=False, has_ended=True)
+        s.compute_duration()
+        assert s.duration == pytest.approx(0.01)
+
+    def test_zero_duration_silence(self):
+        s = Silence(start=5.0, end=5.0)
+        s.compute_duration()
+        assert s.duration == pytest.approx(0.0)
+
+
+class TestSilenceDoubleCounting:
+    """Regression tests for the silence double-counting bug.
+
+    The bug: _begin_silence and _end_silence both pushed self.current_silence
+    to the queue. Since they were the same Python object, _end_silence's mutation
+    affected the already-queued start event. The consumer processed both as
+    ended silences, doubling the duration.
+
+    Fix: _begin_silence now pushes a separate Silence object for the start event.
+    """
+
+    def test_start_and_end_are_separate_objects(self):
+        """Simulate the fix: start event and end event must be different objects."""
+        # Simulate _begin_silence: creates start event as separate object
+        current_silence = Silence(start=1.0, is_starting=True)
+        start_event = Silence(start=1.0, is_starting=True)  # separate copy
+
+        # Simulate _end_silence: mutates current_silence
+        current_silence.end = 3.0
+        current_silence.is_starting = False
+        current_silence.has_ended = True
+        current_silence.compute_duration()
+
+        # start_event should NOT be affected by mutations to current_silence
+        assert start_event.is_starting is True
+        assert start_event.has_ended is False
+        assert start_event.end is None
+
+        # current_silence (end event) has the final state
+        assert current_silence.has_ended is True
+        assert current_silence.duration == pytest.approx(2.0)
+
+    def test_single_object_would_cause_double_counting(self):
+        """Demonstrate the bug: if same object is used for both events."""
+        shared = Silence(start=1.0, is_starting=True)
+        queue = [shared]  # start event queued
+
+        # Mutate (simulates _end_silence)
+        shared.end = 3.0
+        shared.is_starting = False
+        shared.has_ended = True
+        shared.compute_duration()
+        queue.append(shared)  # end event queued
+
+        # Both queue items point to the SAME mutated object
+        assert queue[0] is queue[1]  # same reference
+        assert queue[0].has_ended is True  # start event also shows ended!
+
+        # This would cause double-counting: both items have has_ended=True
+        # and duration=2.0, so the consumer adds 2.0 twice = 4.0
+
+
+class TestConsecutiveSilences:
+    def test_multiple_silences(self):
+        """Multiple silence periods should have independent durations."""
+        s1 = Silence(start=1.0, end=2.0)
+        s1.compute_duration()
+        s2 = Silence(start=5.0, end=8.0)
+        s2.compute_duration()
+        assert s1.duration == pytest.approx(1.0)
+        assert s2.duration == pytest.approx(3.0)
+        # Total silence should be sum, not accumulated on single object
+        assert s1.duration + s2.duration == pytest.approx(4.0)

tests/test_timed_objects.py

@@ -0,0 +1,185 @@
+"""Tests for whisperlivekit.timed_objects data classes."""
+
+import pytest
+
+from whisperlivekit.timed_objects import (
+    ASRToken,
+    FrontData,
+    Segment,
+    Silence,
+    TimedText,
+    Transcript,
+    format_time,
+)
+
+
+class TestFormatTime:
+    def test_zero(self):
+        assert format_time(0) == "0:00:00"
+
+    def test_one_minute(self):
+        assert format_time(60) == "0:01:00"
+
+    def test_one_hour(self):
+        assert format_time(3600) == "1:00:00"
+
+    def test_fractional_truncated(self):
+        assert format_time(61.9) == "0:01:01"
+
+
+class TestASRToken:
+    def test_with_offset(self):
+        t = ASRToken(start=1.0, end=2.0, text="hello")
+        shifted = t.with_offset(0.5)
+        assert shifted.start == pytest.approx(1.5)
+        assert shifted.end == pytest.approx(2.5)
+        assert shifted.text == "hello"
+
+    def test_with_offset_preserves_fields(self):
+        t = ASRToken(start=0.0, end=1.0, text="hi", speaker=2, probability=0.95)
+        shifted = t.with_offset(1.0)
+        assert shifted.speaker == 2
+        assert shifted.probability == 0.95
+
+    def test_is_silence_false(self):
+        t = ASRToken(start=0.0, end=1.0, text="hello")
+        assert t.is_silence() is False
+
+    def test_bool_truthy(self):
+        t = ASRToken(start=0.0, end=1.0, text="hello")
+        assert bool(t) is True
+
+    def test_bool_falsy(self):
+        t = ASRToken(start=0.0, end=1.0, text="")
+        assert bool(t) is False
+
+
+class TestTimedText:
+    def test_has_punctuation_period(self):
+        t = TimedText(text="hello.")
+        assert t.has_punctuation() is True
+
+    def test_has_punctuation_exclamation(self):
+        t = TimedText(text="wow!")
+        assert t.has_punctuation() is True
+
+    def test_has_punctuation_question(self):
+        t = TimedText(text="really?")
+        assert t.has_punctuation() is True
+
+    def test_has_punctuation_cjk(self):
+        t = TimedText(text="hello。")
+        assert t.has_punctuation() is True
+
+    def test_no_punctuation(self):
+        t = TimedText(text="hello world")
+        assert t.has_punctuation() is False
+
+    def test_duration(self):
+        t = TimedText(start=1.0, end=3.5)
+        assert t.duration() == pytest.approx(2.5)
+
+    def test_contains_timespan(self):
+        outer = TimedText(start=0.0, end=5.0)
+        inner = TimedText(start=1.0, end=3.0)
+        assert outer.contains_timespan(inner) is True
+        assert inner.contains_timespan(outer) is False
+
+
+class TestSilence:
+    def test_compute_duration(self):
+        s = Silence(start=1.0, end=3.5)
+        d = s.compute_duration()
+        assert d == pytest.approx(2.5)
+        assert s.duration == pytest.approx(2.5)
+
+    def test_compute_duration_none_start(self):
+        s = Silence(start=None, end=3.5)
+        d = s.compute_duration()
+        assert d is None
+
+    def test_compute_duration_none_end(self):
+        s = Silence(start=1.0, end=None)
+        d = s.compute_duration()
+        assert d is None
+
+    def test_is_silence_true(self):
+        s = Silence()
+        assert s.is_silence() is True
+
+
+class TestTranscript:
+    def test_from_tokens(self, sample_tokens):
+        t = Transcript.from_tokens(sample_tokens, sep="")
+        assert t.text == "Hello world test."
+        assert t.start == pytest.approx(0.0)
+        assert t.end == pytest.approx(1.5)
+
+    def test_from_tokens_with_sep(self, sample_tokens):
+        t = Transcript.from_tokens(sample_tokens, sep="|")
+        assert t.text == "Hello| world| test."
+
+    def test_from_empty_tokens(self):
+        t = Transcript.from_tokens([])
+        assert t.text == ""
+        assert t.start is None
+        assert t.end is None
+
+    def test_from_tokens_with_offset(self, sample_tokens):
+        t = Transcript.from_tokens(sample_tokens, offset=10.0)
+        assert t.start == pytest.approx(10.0)
+        assert t.end == pytest.approx(11.5)
+
+
+class TestSegment:
+    def test_from_tokens(self, sample_tokens):
+        seg = Segment.from_tokens(sample_tokens)
+        assert seg is not None
+        assert seg.text == "Hello world test."
+        assert seg.start == pytest.approx(0.0)
+        assert seg.end == pytest.approx(1.5)
+        assert seg.speaker == -1
+
+    def test_from_silence_tokens(self):
+        silences = [
+            Silence(start=1.0, end=2.0),
+            Silence(start=2.0, end=3.0),
+        ]
+        seg = Segment.from_tokens(silences, is_silence=True)
+        assert seg is not None
+        assert seg.speaker == -2
+        assert seg.is_silence() is True
+        assert seg.text is None
+
+    def test_from_empty_tokens(self):
+        seg = Segment.from_tokens([])
+        assert seg is None
+
+    def test_to_dict(self, sample_tokens):
+        seg = Segment.from_tokens(sample_tokens)
+        d = seg.to_dict()
+        assert "text" in d
+        assert "speaker" in d
+        assert "start" in d
+        assert "end" in d
+
+
+class TestFrontData:
+    def test_to_dict_empty(self):
+        fd = FrontData()
+        d = fd.to_dict()
+        assert d["lines"] == []
+        assert d["buffer_transcription"] == ""
+        assert "error" not in d
+
+    def test_to_dict_with_error(self):
+        fd = FrontData(error="something broke")
+        d = fd.to_dict()
+        assert d["error"] == "something broke"
+
+    def test_to_dict_with_lines(self, sample_tokens):
+        seg = Segment.from_tokens(sample_tokens)
+        fd = FrontData(lines=[seg])
+        d = fd.to_dict()
+        assert len(d["lines"]) == 1
+        assert d["lines"][0]["text"] == "Hello world test."

whisperlivekit/__init__.py

@@ -1,7 +1,7 @@
 from .audio_processor import AudioProcessor
 from .core import TranscriptionEngine
 from .parse_args import parse_args
-from .web.web_interface import get_web_interface_html, get_inline_ui_html
+from .web.web_interface import get_inline_ui_html, get_web_interface_html
 
 __all__ = [
     "TranscriptionEngine",

whisperlivekit/backend_support.py

@@ -0,0 +1,47 @@
+import importlib.util
+import logging
+import platform
+
+logger = logging.getLogger(__name__)
+
+
+def module_available(module_name):
+    """Return True if the given module can be imported."""
+    return importlib.util.find_spec(module_name) is not None
+
+
+def mlx_backend_available(warn_on_missing = False):
+    is_macos = platform.system() == "Darwin"
+    is_arm = platform.machine() == "arm64"
+    available = (
+        is_macos
+        and is_arm
+        and module_available("mlx_whisper")
+    )
+    if not available and warn_on_missing and is_macos and is_arm:
+        logger.warning(
+            "=" * 50
+            + "\nMLX Whisper not found but you are on Apple Silicon. "
+              "Consider installing mlx-whisper for better performance: "
+              "`pip install mlx-whisper`\n"
+            + "=" * 50
+        )
+    return available
+
+
+def voxtral_hf_backend_available():
+    """Return True if HF Transformers Voxtral backend is available."""
+    return module_available("transformers")
+
+
+
+def faster_backend_available(warn_on_missing = False):
+    available = module_available("faster_whisper")
+    if not available and warn_on_missing and platform.system() != "Darwin":
+        logger.warning(
+            "=" * 50
+            + "\nFaster-Whisper not found. Consider installing faster-whisper "
+              "for better performance: `pip install faster-whisper`\n"
+            + "=" * 50
+        )
+    return available

whisperlivekit/basic_server.py

@@ -1,37 +1,26 @@
-from contextlib import asynccontextmanager
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
-from fastapi.responses import HTMLResponse
-from fastapi.middleware.cors import CORSMiddleware
-from whisperlivekit import TranscriptionEngine, AudioProcessor, get_inline_ui_html, parse_args
 import asyncio
 import logging
-from starlette.staticfiles import StaticFiles
-import pathlib
-import whisperlivekit.web as webpkg
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
+
+from whisperlivekit import (AudioProcessor, TranscriptionEngine,
+                            get_inline_ui_html, parse_args)
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logging.getLogger().setLevel(logging.WARNING)
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 
-args = parse_args()
+config = parse_args()
 transcription_engine = None
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    
-    #to remove after 0.2.8
-    if args.backend == "simulstreaming" and not args.disable_fast_encoder:
-        logger.warning(f"""
-{'='*50}
-WhisperLiveKit 0.2.8 has introduced a new fast encoder feature using MLX Whisper or Faster Whisper for improved speed. Use --disable-fast-encoder to disable if you encounter issues.
-{'='*50}
-    """)
-    
     global transcription_engine
-    transcription_engine = TranscriptionEngine(
-        **vars(args),
-    )
+    transcription_engine = TranscriptionEngine(config=config)
     yield
 
 app = FastAPI(lifespan=lifespan)
@@ -42,8 +31,6 @@ app.add_middleware(
     allow_methods=["*"],
     allow_headers=["*"],
 )
-web_dir = pathlib.Path(webpkg.__file__).parent
-app.mount("/web", StaticFiles(directory=str(web_dir)), name="web")
 
 @app.get("/")
 async def get():
@@ -54,7 +41,7 @@ async def handle_websocket_results(websocket, results_generator):
     """Consumes results from the audio processor and sends them via WebSocket."""
     try:
         async for response in results_generator:
-            await websocket.send_json(response)
+            await websocket.send_json(response.to_dict())
         # when the results_generator finishes it means all audio has been processed
         logger.info("Results generator finished. Sending 'ready_to_stop' to client.")
         await websocket.send_json({"type": "ready_to_stop"})
@@ -72,6 +59,11 @@ async def websocket_endpoint(websocket: WebSocket):
     )
     await websocket.accept()
     logger.info("WebSocket connection opened.")
+
+    try:
+        await websocket.send_json({"type": "config", "useAudioWorklet": bool(config.pcm_input)})
+    except Exception as e:
+        logger.warning(f"Failed to send config to client: {e}")
             
     results_generator = await audio_processor.create_tasks()
     websocket_task = asyncio.create_task(handle_websocket_results(websocket, results_generator))
@@ -109,24 +101,26 @@ def main():
     
     uvicorn_kwargs = {
         "app": "whisperlivekit.basic_server:app",
-        "host":args.host, 
-        "port":args.port, 
+        "host": config.host,
+        "port": config.port,
         "reload": False,
         "log_level": "info",
         "lifespan": "on",
     }
-    
+
     ssl_kwargs = {}
-    if args.ssl_certfile or args.ssl_keyfile:
-        if not (args.ssl_certfile and args.ssl_keyfile):
+    if config.ssl_certfile or config.ssl_keyfile:
+        if not (config.ssl_certfile and config.ssl_keyfile):
             raise ValueError("Both --ssl-certfile and --ssl-keyfile must be specified together.")
         ssl_kwargs = {
-            "ssl_certfile": args.ssl_certfile,
-            "ssl_keyfile": args.ssl_keyfile
+            "ssl_certfile": config.ssl_certfile,
+            "ssl_keyfile": config.ssl_keyfile,
         }
 
     if ssl_kwargs:
         uvicorn_kwargs = {**uvicorn_kwargs, **ssl_kwargs}
+    if config.forwarded_allow_ips:
+        uvicorn_kwargs = {**uvicorn_kwargs, "forwarded_allow_ips": config.forwarded_allow_ips}
 
     uvicorn.run(**uvicorn_kwargs)
 

whisperlivekit/config.py

@@ -0,0 +1,102 @@
+"""Typed configuration for the WhisperLiveKit pipeline."""
+import logging
+from dataclasses import dataclass, field, fields
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class WhisperLiveKitConfig:
+    """Single source of truth for all WhisperLiveKit configuration.
+
+    Replaces the previous dict-based parameter system in TranscriptionEngine.
+    All fields have defaults matching the prior behaviour.
+    """
+
+    # Server / global
+    host: str = "localhost"
+    port: int = 8000
+    diarization: bool = False
+    punctuation_split: bool = False
+    target_language: str = ""
+    vac: bool = True
+    vac_chunk_size: float = 0.04
+    log_level: str = "DEBUG"
+    ssl_certfile: Optional[str] = None
+    ssl_keyfile: Optional[str] = None
+    forwarded_allow_ips: Optional[str] = None
+    transcription: bool = True
+    vad: bool = True
+    pcm_input: bool = False
+    disable_punctuation_split: bool = False
+    diarization_backend: str = "sortformer"
+    backend_policy: str = "simulstreaming"
+    backend: str = "auto"
+
+    # Transcription common
+    warmup_file: Optional[str] = None
+    min_chunk_size: float = 0.1
+    model_size: str = "base"
+    model_cache_dir: Optional[str] = None
+    model_dir: Optional[str] = None
+    model_path: Optional[str] = None
+    lora_path: Optional[str] = None
+    lan: str = "auto"
+    direct_english_translation: bool = False
+
+    # LocalAgreement-specific
+    buffer_trimming: str = "segment"
+    confidence_validation: bool = False
+    buffer_trimming_sec: float = 15.0
+
+    # SimulStreaming-specific
+    disable_fast_encoder: bool = False
+    custom_alignment_heads: Optional[str] = None
+    frame_threshold: int = 25
+    beams: int = 1
+    decoder_type: Optional[str] = None
+    audio_max_len: float = 20.0
+    audio_min_len: float = 0.0
+    cif_ckpt_path: Optional[str] = None
+    never_fire: bool = False
+    init_prompt: Optional[str] = None
+    static_init_prompt: Optional[str] = None
+    max_context_tokens: Optional[int] = None
+
+    # Diarization (diart)
+    segmentation_model: str = "pyannote/segmentation-3.0"
+    embedding_model: str = "pyannote/embedding"
+
+    # Translation
+    nllb_backend: str = "transformers"
+    nllb_size: str = "600M"
+
+    def __post_init__(self):
+        # .en model suffix forces English
+        if self.model_size and self.model_size.endswith(".en"):
+            self.lan = "en"
+        # Normalize backend_policy aliases
+        if self.backend_policy == "1":
+            self.backend_policy = "simulstreaming"
+        elif self.backend_policy == "2":
+            self.backend_policy = "localagreement"
+
+    # ------------------------------------------------------------------
+    # Factory helpers
+    # ------------------------------------------------------------------
+
+    @classmethod
+    def from_namespace(cls, ns) -> "WhisperLiveKitConfig":
+        """Create config from an argparse Namespace, ignoring unknown keys."""
+        known = {f.name for f in fields(cls)}
+        return cls(**{k: v for k, v in vars(ns).items() if k in known})
+
+    @classmethod
+    def from_kwargs(cls, **kwargs) -> "WhisperLiveKitConfig":
+        """Create config from keyword arguments; warns on unknown keys."""
+        known = {f.name for f in fields(cls)}
+        unknown = set(kwargs.keys()) - known
+        if unknown:
+            logger.warning("Unknown config keys ignored: %s", unknown)
+        return cls(**{k: v for k, v in kwargs.items() if k in known})

whisperlivekit/core.py

@@ -1,169 +1,207 @@
-try:
-    from whisperlivekit.whisper_streaming_custom.whisper_online import backend_factory
-    from whisperlivekit.whisper_streaming_custom.online_asr import OnlineASRProcessor
-except ImportError:
-    from .whisper_streaming_custom.whisper_online import backend_factory
-    from .whisper_streaming_custom.online_asr import OnlineASRProcessor
-from whisperlivekit.warmup import warmup_asr, warmup_online
-from argparse import Namespace
+import logging
 import sys
+import threading
+from argparse import Namespace
+from dataclasses import asdict
+
+from whisperlivekit.config import WhisperLiveKitConfig
+from whisperlivekit.local_agreement.online_asr import OnlineASRProcessor
+from whisperlivekit.local_agreement.whisper_online import backend_factory
+from whisperlivekit.simul_whisper import SimulStreamingASR
+
+logger = logging.getLogger(__name__)
 
 class TranscriptionEngine:
     _instance = None
     _initialized = False
+    _lock = threading.Lock()  # Thread-safe singleton lock
     
     def __new__(cls, *args, **kwargs):
+        # Double-checked locking pattern for thread-safe singleton
         if cls._instance is None:
-            cls._instance = super().__new__(cls)
+            with cls._lock:
+                # Check again inside lock to prevent race condition
+                if cls._instance is None:
+                    cls._instance = super().__new__(cls)
         return cls._instance
     
-    def __init__(self, **kwargs):
-        if TranscriptionEngine._initialized:
-            return
+    def __init__(self, config=None, **kwargs):
+        # Thread-safe initialization check
+        with TranscriptionEngine._lock:
+            if TranscriptionEngine._initialized:
+                return
 
-        defaults = {
-            "host": "localhost",
-            "port": 8000,
-            "warmup_file": None,
-            "diarization": False,
-            "punctuation_split": False,
-            "min_chunk_size": 0.5,
-            "model": "tiny",
-            "model_cache_dir": None,
-            "model_dir": None,
-            "lan": "auto",
-            "task": "transcribe",
-            "backend": "faster-whisper",
-            "vac": True,
-            "vac_chunk_size": 0.04,
-            "log_level": "DEBUG",
-            "ssl_certfile": None,
-            "ssl_keyfile": None,
-            "transcription": True,
-            "vad": True,
-            # whisperstreaming params:
-            "buffer_trimming": "segment",
-            "confidence_validation": False,
-            "buffer_trimming_sec": 15,
-            # simulstreaming params:
-            "disable_fast_encoder": False,
-            "frame_threshold": 25,
-            "beams": 1,
-            "decoder_type": None,
-            "audio_max_len": 20.0,
-            "audio_min_len": 0.0,
-            "cif_ckpt_path": None,
-            "never_fire": False,
-            "init_prompt": None,
-            "static_init_prompt": None,
-            "max_context_tokens": None,
-            "model_path": './base.pt',
-            "diarization_backend": "sortformer",
-            # diart params:
-            "segmentation_model": "pyannote/segmentation-3.0",
-            "embedding_model": "pyannote/embedding",         
-        }
+        try:
+            self._do_init(config, **kwargs)
+        except Exception:
+            # Reset singleton so a retry is possible
+            with TranscriptionEngine._lock:
+                TranscriptionEngine._instance = None
+                TranscriptionEngine._initialized = False
+            raise
 
-        config_dict = {**defaults, **kwargs}
+        with TranscriptionEngine._lock:
+            TranscriptionEngine._initialized = True
 
+    def _do_init(self, config=None, **kwargs):
+        # Handle negated kwargs from programmatic API
         if 'no_transcription' in kwargs:
-            config_dict['transcription'] = not kwargs['no_transcription']
+            kwargs['transcription'] = not kwargs.pop('no_transcription')
         if 'no_vad' in kwargs:
-            config_dict['vad'] = not kwargs['no_vad']
+            kwargs['vad'] = not kwargs.pop('no_vad')
         if 'no_vac' in kwargs:
-            config_dict['vac'] = not kwargs['no_vac']
-        
-        config_dict.pop('no_transcription', None)
-        config_dict.pop('no_vad', None)
+            kwargs['vac'] = not kwargs.pop('no_vac')
 
-        if 'language' in kwargs:
-            config_dict['lan'] = kwargs['language']
-        config_dict.pop('language', None) 
+        if config is None:
+            if isinstance(kwargs.get('config'), WhisperLiveKitConfig):
+                config = kwargs.pop('config')
+            else:
+                config = WhisperLiveKitConfig.from_kwargs(**kwargs)
+        self.config = config
+
+        # Backward compat: expose as self.args (Namespace-like) for AudioProcessor etc.
+        self.args = Namespace(**asdict(config))
 
-        self.args = Namespace(**config_dict)
-        
         self.asr = None
         self.tokenizer = None
         self.diarization = None
-        self.vac_model = None
-        
-        if self.args.vac:
-            import torch
-            self.vac_model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad")            
-        
-        if self.args.transcription:
-            if self.args.backend == "simulstreaming": 
-                from whisperlivekit.simul_whisper import SimulStreamingASR
-                self.tokenizer = None
-                simulstreaming_kwargs = {}
-                for attr in ['frame_threshold', 'beams', 'decoder_type', 'audio_max_len', 'audio_min_len', 
-                            'cif_ckpt_path', 'never_fire', 'init_prompt', 'static_init_prompt', 
-                            'max_context_tokens', 'model_path', 'warmup_file', 'preload_model_count', 'disable_fast_encoder']:
-                    if hasattr(self.args, attr):
-                        simulstreaming_kwargs[attr] = getattr(self.args, attr)
-        
-                # Add segment_length from min_chunk_size
-                simulstreaming_kwargs['segment_length'] = getattr(self.args, 'min_chunk_size', 0.5)
-                simulstreaming_kwargs['task'] = self.args.task
-                
-                size = self.args.model
-                self.asr = SimulStreamingASR(
-                    modelsize=size,
-                    lan=self.args.lan,
-                    cache_dir=getattr(self.args, 'model_cache_dir', None),
-                    model_dir=getattr(self.args, 'model_dir', None),
-                    **simulstreaming_kwargs
+        self.vac_session = None
+
+        if config.vac:
+            from whisperlivekit.silero_vad_iterator import is_onnx_available
+
+            if is_onnx_available():
+                from whisperlivekit.silero_vad_iterator import load_onnx_session
+                self.vac_session = load_onnx_session()
+            else:
+                logger.warning(
+                    "onnxruntime not installed. VAC will use JIT model which is loaded per-session. "
+                    "For multi-user scenarios, install onnxruntime: pip install onnxruntime"
                 )
 
-            else:
-                self.asr, self.tokenizer = backend_factory(self.args)
-            warmup_asr(self.asr, self.args.warmup_file) #for simulstreaming, warmup should be done in the online class not here
+        transcription_common_params = {
+            "warmup_file": config.warmup_file,
+            "min_chunk_size": config.min_chunk_size,
+            "model_size": config.model_size,
+            "model_cache_dir": config.model_cache_dir,
+            "model_dir": config.model_dir,
+            "model_path": config.model_path,
+            "lora_path": config.lora_path,
+            "lan": config.lan,
+            "direct_english_translation": config.direct_english_translation,
+        }
 
-        if self.args.diarization:
-            if self.args.diarization_backend == "diart":
+        if config.transcription:
+            if config.backend == "voxtral-mlx":
+                from whisperlivekit.voxtral_mlx_asr import VoxtralMLXASR
+                self.tokenizer = None
+                self.asr = VoxtralMLXASR(**transcription_common_params)
+                logger.info("Using Voxtral MLX native backend")
+            elif config.backend == "voxtral":
+                from whisperlivekit.voxtral_hf_streaming import VoxtralHFStreamingASR
+                self.tokenizer = None
+                self.asr = VoxtralHFStreamingASR(**transcription_common_params)
+                logger.info("Using Voxtral HF Transformers streaming backend")
+            elif config.backend_policy == "simulstreaming":
+                simulstreaming_params = {
+                    "disable_fast_encoder": config.disable_fast_encoder,
+                    "custom_alignment_heads": config.custom_alignment_heads,
+                    "frame_threshold": config.frame_threshold,
+                    "beams": config.beams,
+                    "decoder_type": config.decoder_type,
+                    "audio_max_len": config.audio_max_len,
+                    "audio_min_len": config.audio_min_len,
+                    "cif_ckpt_path": config.cif_ckpt_path,
+                    "never_fire": config.never_fire,
+                    "init_prompt": config.init_prompt,
+                    "static_init_prompt": config.static_init_prompt,
+                    "max_context_tokens": config.max_context_tokens,
+                }
+
+                self.tokenizer = None
+                self.asr = SimulStreamingASR(
+                    **transcription_common_params,
+                    **simulstreaming_params,
+                    backend=config.backend,
+                )
+                logger.info(
+                    "Using SimulStreaming policy with %s backend",
+                    getattr(self.asr, "encoder_backend", "whisper"),
+                )
+            else:
+                whisperstreaming_params = {
+                    "buffer_trimming": config.buffer_trimming,
+                    "confidence_validation": config.confidence_validation,
+                    "buffer_trimming_sec": config.buffer_trimming_sec,
+                }
+
+                self.asr = backend_factory(
+                    backend=config.backend,
+                    **transcription_common_params,
+                    **whisperstreaming_params,
+                )
+                logger.info(
+                    "Using LocalAgreement policy with %s backend",
+                    getattr(self.asr, "backend_choice", self.asr.__class__.__name__),
+                )
+
+        if config.diarization:
+            if config.diarization_backend == "diart":
                 from whisperlivekit.diarization.diart_backend import DiartDiarization
                 self.diarization_model = DiartDiarization(
-                    block_duration=self.args.min_chunk_size,
-                    segmentation_model_name=self.args.segmentation_model,
-                    embedding_model_name=self.args.embedding_model
+                    block_duration=config.min_chunk_size,
+                    segmentation_model=config.segmentation_model,
+                    embedding_model=config.embedding_model,
                 )
-            elif self.args.diarization_backend == "sortformer":
+            elif config.diarization_backend == "sortformer":
                 from whisperlivekit.diarization.sortformer_backend import SortformerDiarization
                 self.diarization_model = SortformerDiarization()
+
+        self.translation_model = None
+        if config.target_language:
+            if config.lan == 'auto' and config.backend_policy != "simulstreaming":
+                raise ValueError('Translation cannot be set with language auto when transcription backend is not simulstreaming')
             else:
-                raise ValueError(f"Unknown diarization backend: {self.args.diarization_backend}")
-            
-        TranscriptionEngine._initialized = True
+                try:
+                    from nllw import load_model
+                except ImportError:
+                    raise ImportError('To use translation, you must install nllw: `pip install nllw`')
+                self.translation_model = load_model(
+                    [config.lan],
+                    nllb_backend=config.nllb_backend,
+                    nllb_size=config.nllb_size,
+                )
 
 
-
-def online_factory(args, asr, tokenizer, logfile=sys.stderr):
-    if args.backend == "simulstreaming":    
+def online_factory(args, asr):
+    if getattr(args, 'backend', None) == "voxtral-mlx":
+        from whisperlivekit.voxtral_mlx_asr import VoxtralMLXOnlineProcessor
+        return VoxtralMLXOnlineProcessor(asr)
+    if getattr(args, 'backend', None) == "voxtral":
+        from whisperlivekit.voxtral_hf_streaming import VoxtralHFStreamingOnlineProcessor
+        return VoxtralHFStreamingOnlineProcessor(asr)
+    if args.backend_policy == "simulstreaming":
         from whisperlivekit.simul_whisper import SimulStreamingOnlineProcessor
-        online = SimulStreamingOnlineProcessor(
-            asr,
-            logfile=logfile,
-        )
-        # warmup_online(online, args.warmup_file)
-    else:
-        online = OnlineASRProcessor(
-            asr,
-            tokenizer,
-            logfile=logfile,
-            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
-            confidence_validation = args.confidence_validation
-        )
-    return online
+        return SimulStreamingOnlineProcessor(asr)
+    return OnlineASRProcessor(asr)
   
   
 def online_diarization_factory(args, diarization_backend):
     if args.diarization_backend == "diart":
         online = diarization_backend
-        # Not the best here, since several user/instances will share the same backend, but diart is not SOTA anymore and sortformer is recommanded
-    
-    if args.diarization_backend == "sortformer":
-        from whisperlivekit.diarization.sortformer_backend import SortformerDiarizationOnline
+        # Not the best here, since several user/instances will share the same backend, but diart is not SOTA anymore and sortformer is recommended
+    elif args.diarization_backend == "sortformer":
+        from whisperlivekit.diarization.sortformer_backend import \
+            SortformerDiarizationOnline
         online = SortformerDiarizationOnline(shared_model=diarization_backend)
+    else:
+        raise ValueError(f"Unknown diarization backend: {args.diarization_backend}")
     return online
 
-        
+
+def online_translation_factory(args, translation_model):
+    #should be at speaker level in the future:
+    #one shared nllb model for all speaker
+    #one tokenizer per speaker/language
+    from nllw import OnlineTranslation
+    return OnlineTranslation(translation_model, [args.lan], [args.target_language])

whisperlivekit/diarization/diart_backend.py

@@ -1,32 +1,28 @@
 import asyncio
-import re
-import threading
-import numpy as np
 import logging
+import threading
 import time
-from queue import SimpleQueue, Empty
+from queue import Empty, SimpleQueue
+from typing import Any, List, Tuple
 
+import diart.models as m
+import numpy as np
 from diart import SpeakerDiarization, SpeakerDiarizationConfig
 from diart.inference import StreamingInference
-from diart.sources import AudioSource
-from whisperlivekit.timed_objects import SpeakerSegment
-from diart.sources import MicrophoneAudioSource
-from rx.core import Observer
-from typing import Tuple, Any, List
+from diart.sources import AudioSource, MicrophoneAudioSource
 from pyannote.core import Annotation
-import diart.models as m
+from rx.core import Observer
+
+from whisperlivekit.diarization.utils import extract_number
+from whisperlivekit.timed_objects import SpeakerSegment
 
 logger = logging.getLogger(__name__)
 
-def extract_number(s: str) -> int:
-    m = re.search(r'\d+', s)
-    return int(m.group()) if m else None
-
 class DiarizationObserver(Observer):
     """Observer that logs all data emitted by the diarization pipeline and stores speaker segments."""
     
     def __init__(self):
-        self.speaker_segments = []
+        self.diarization_segments = []
         self.processed_time = 0
         self.segment_lock = threading.Lock()
         self.global_time_offset = 0.0
@@ -48,7 +44,7 @@ class DiarizationObserver(Observer):
                 for speaker, label in annotation._labels.items():
                     for start, end in zip(label.segments_boundaries_[:-1], label.segments_boundaries_[1:]):
                         print(f"  {speaker}: {start:.2f}s-{end:.2f}s")
-                        self.speaker_segments.append(SpeakerSegment(
+                        self.diarization_segments.append(SpeakerSegment(
                             speaker=speaker,
                             start=start + self.global_time_offset,
                             end=end + self.global_time_offset
@@ -59,14 +55,14 @@ class DiarizationObserver(Observer):
     def get_segments(self) -> List[SpeakerSegment]:
         """Get a copy of the current speaker segments."""
         with self.segment_lock:
-            return self.speaker_segments.copy()
+            return self.diarization_segments.copy()
     
     def clear_old_segments(self, older_than: float = 30.0):
         """Clear segments older than the specified time."""
         with self.segment_lock:
             current_time = self.processed_time
-            self.speaker_segments = [
-                segment for segment in self.speaker_segments 
+            self.diarization_segments = [
+                segment for segment in self.diarization_segments 
                 if current_time - segment.end < older_than
             ]
     
@@ -178,7 +174,6 @@ class DiartDiarization:
         
         self.pipeline = SpeakerDiarization(config=config)        
         self.observer = DiarizationObserver()
-        self.lag_diart = None
         
         if use_microphone:
             self.source = MicrophoneAudioSource(block_duration=block_duration)
@@ -203,46 +198,20 @@ class DiartDiarization:
     def insert_silence(self, silence_duration):
         self.observer.global_time_offset += silence_duration
 
-    async def diarize(self, pcm_array: np.ndarray):
-        """
-        Process audio data for diarization.
-        Only used when working with WebSocketAudioSource.
-        """
+    def insert_audio_chunk(self, pcm_array: np.ndarray):
+        """Buffer audio for the next diarization step."""
         if self.custom_source:
-            self.custom_source.push_audio(pcm_array)            
-        # self.observer.clear_old_segments()        
+            self.custom_source.push_audio(pcm_array)
+
+    async def diarize(self):
+        """Return the current speaker segments from the diarization pipeline."""
+        return self.observer.get_segments()        
 
     def close(self):
         """Close the audio source."""
         if self.custom_source:
             self.custom_source.close()
 
-    def assign_speakers_to_tokens(self, tokens: list, use_punctuation_split: bool = False) -> float:
-        """
-        Assign speakers to tokens based on timing overlap with speaker segments.
-        Uses the segments collected by the observer.
-        
-        If use_punctuation_split is True, uses punctuation marks to refine speaker boundaries.
-        """
-        segments = self.observer.get_segments()
-        
-        # Debug logging
-        logger.debug(f"assign_speakers_to_tokens called with {len(tokens)} tokens")
-        logger.debug(f"Available segments: {len(segments)}")
-        for i, seg in enumerate(segments[:5]):  # Show first 5 segments
-            logger.debug(f"  Segment {i}: {seg.speaker} [{seg.start:.2f}-{seg.end:.2f}]")
-        
-        if not self.lag_diart and segments and tokens:
-            self.lag_diart = segments[0].start - tokens[0].start
-        
-        if not use_punctuation_split:
-            for token in tokens:
-                for segment in segments:
-                    if not (segment.end <= token.start + self.lag_diart or segment.start >= token.end + self.lag_diart):
-                        token.speaker = extract_number(segment.speaker) + 1
-        else:
-            tokens = add_speaker_to_tokens(segments, tokens)
-        return tokens
         
 def concatenate_speakers(segments):
     segments_concatenated = [{"speaker": 1, "begin": 0.0, "end": 0.0}]

whisperlivekit/diarization/sortformer_backend.py

@@ -1,11 +1,12 @@
-import numpy as np
-import torch
 import logging
 import threading
 import time
 import wave
+from queue import Empty, SimpleQueue
 from typing import List, Optional
-from queue import SimpleQueue, Empty
+
+import numpy as np
+import torch
 
 from whisperlivekit.timed_objects import SpeakerSegment
 
@@ -60,11 +61,15 @@ class SortformerDiarization:
             self.diar_model = SortformerEncLabelModel.from_pretrained(model_name)
             self.diar_model.eval()
 
-            if torch.cuda.is_available():
-                self.diar_model.to(torch.device("cuda"))
-                logger.info("Using CUDA for Sortformer model")
-            else:
-                logger.info("Using CPU for Sortformer model")
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            self.diar_model.to(device)
+            
+            ## to test
+            # for name, param in self.diar_model.named_parameters():
+            #     if param.device != device:
+            #         raise RuntimeError(f"Parameter {name} is on {param.device} but should be on {device}")
+            
+            logger.info(f"Using {device.type.upper()} for Sortformer model")
 
             self.diar_model.sortformer_modules.chunk_len = 10
             self.diar_model.sortformer_modules.subsampling_factor = 10
@@ -90,11 +95,11 @@ class SortformerDiarizationOnline:
             model_name: Pre-trained model name (default: "nvidia/diar_streaming_sortformer_4spk-v2")
         """
         self.sample_rate = sample_rate
-        self.speaker_segments = []
+        self.diarization_segments = []
+        self.diar_segments = []
         self.buffer_audio = np.array([], dtype=np.float32)
         self.segment_lock = threading.Lock()
         self.global_time_offset = 0.0
-        self.processed_time = 0.0
         self.debug = False
                 
         self.diar_model = shared_model.diar_model
@@ -106,6 +111,7 @@ class SortformerDiarizationOnline:
             features=128,
             pad_to=0
         )
+        self.audio2mel.to(self.diar_model.device)
         
         self.chunk_duration_seconds = (
             self.diar_model.sortformer_modules.chunk_len * 
@@ -150,12 +156,10 @@ class SortformerDiarizationOnline:
         )
         self.streaming_state.fifo_lengths = torch.zeros((batch_size,), dtype=torch.long, device=device)
         self.streaming_state.mean_sil_emb = torch.zeros((batch_size, self.diar_model.sortformer_modules.fc_d_model), device=device)
-        self.streaming_state.n_sil_frames = torch.zeros((batch_size,), dtype=torch.long, device=device)
-        
-        # Initialize total predictions tensor
+        self.streaming_state.n_sil_frames = torch.zeros((batch_size,), dtype=torch.long, device=device)        
         self.total_preds = torch.zeros((batch_size, 0, self.diar_model.sortformer_modules.n_spk), device=device)
 
-    def insert_silence(self, silence_duration: float):
+    def insert_silence(self, silence_duration: Optional[float]):
         """
         Insert silence period by adjusting the global time offset.
         
@@ -166,244 +170,111 @@ class SortformerDiarizationOnline:
             self.global_time_offset += silence_duration
         logger.debug(f"Inserted silence of {silence_duration:.2f}s, new offset: {self.global_time_offset:.2f}s")
 
-    async def diarize(self, pcm_array: np.ndarray):
+    def insert_audio_chunk(self, pcm_array: np.ndarray):
+        if self.debug:
+            self.audio_buffer.append(pcm_array.copy())
+        self.buffer_audio = np.concatenate([self.buffer_audio, pcm_array.copy()])
+  
+
+    async def diarize(self):
         """
         Process audio data for diarization in streaming fashion.
         
         Args:
             pcm_array: Audio data as numpy array
         """
-        try:
-            if self.debug:
-                self.audio_buffer.append(pcm_array.copy())
 
-            threshold = int(self.chunk_duration_seconds * self.sample_rate)
+        threshold = int(self.chunk_duration_seconds * self.sample_rate)
+        
+        if not len(self.buffer_audio) >= threshold:
+            return []
+        
+        audio = self.buffer_audio[:threshold]
+        self.buffer_audio = self.buffer_audio[threshold:]
+        
+        device = self.diar_model.device
+        audio_signal_chunk = torch.tensor(audio, device=device).unsqueeze(0)
+        audio_signal_length_chunk = torch.tensor([audio_signal_chunk.shape[1]], device=device)
+        
+        processed_signal_chunk, processed_signal_length_chunk = self.audio2mel.get_features(
+            audio_signal_chunk, audio_signal_length_chunk
+        )
+        processed_signal_chunk = processed_signal_chunk.to(device)
+        processed_signal_length_chunk = processed_signal_length_chunk.to(device)
+        
+        if self._previous_chunk_features is not None:
+            to_add = self._previous_chunk_features[:, :, -99:].to(device)
+            total_features = torch.concat([to_add, processed_signal_chunk], dim=2).to(device)
+        else:
+            total_features = processed_signal_chunk.to(device)
+        
+        self._previous_chunk_features = processed_signal_chunk.to(device)
+        
+        chunk_feat_seq_t = torch.transpose(total_features, 1, 2).to(device)
+        
+        with torch.inference_mode():
+            left_offset = 8 if self._chunk_index > 0 else 0
+            right_offset = 8
             
-            self.buffer_audio = np.concatenate([self.buffer_audio, pcm_array.copy()])
-            if not len(self.buffer_audio) >= threshold:
-                return
-            
-            audio = self.buffer_audio[:threshold]
-            self.buffer_audio = self.buffer_audio[threshold:]
-            
-            audio_signal_chunk = torch.tensor(audio).unsqueeze(0).to(self.diar_model.device)
-            audio_signal_length_chunk = torch.tensor([audio_signal_chunk.shape[1]]).to(self.diar_model.device)
-            
-            processed_signal_chunk, processed_signal_length_chunk = self.audio2mel.get_features(
-                audio_signal_chunk, audio_signal_length_chunk
-            )
-            
-            if self._previous_chunk_features is not None:
-                to_add = self._previous_chunk_features[:, :, -99:]
-                total_features = torch.concat([to_add, processed_signal_chunk], dim=2)
-            else:
-                total_features = processed_signal_chunk
-            
-            self._previous_chunk_features = processed_signal_chunk
-            
-            chunk_feat_seq_t = torch.transpose(total_features, 1, 2)
-            
-            with torch.inference_mode():
-                left_offset = 8 if self._chunk_index > 0 else 0
-                right_offset = 8
-                
-                self.streaming_state, self.total_preds = self.diar_model.forward_streaming_step(
-                    processed_signal=chunk_feat_seq_t,
-                    processed_signal_length=torch.tensor([chunk_feat_seq_t.shape[1]]),
-                    streaming_state=self.streaming_state,
-                    total_preds=self.total_preds,
-                    left_offset=left_offset,
-                    right_offset=right_offset,
-                )
-                
-            # Convert predictions to speaker segments
-            self._process_predictions()
-            
-            self._chunk_index += 1
-            
-        except Exception as e:
-            logger.error(f"Error in diarize: {e}")
-            raise
-            
-        # TODO: Handle case when stream ends with partial buffer (accumulated_duration > 0 but < chunk_duration_seconds)
+            self.streaming_state, self.total_preds = self.diar_model.forward_streaming_step(
+                processed_signal=chunk_feat_seq_t,
+                processed_signal_length=torch.tensor([chunk_feat_seq_t.shape[1]]).to(device),
+                streaming_state=self.streaming_state,
+                total_preds=self.total_preds,
+                left_offset=left_offset,
+                right_offset=right_offset,
+            )                
+        new_segments = self._process_predictions()
+        
+        self._chunk_index += 1
+        return new_segments
 
     def _process_predictions(self):
         """Process model predictions and convert to speaker segments."""
-        try:
-            preds_np = self.total_preds[0].cpu().numpy()
-            active_speakers = np.argmax(preds_np, axis=1)
-            
-            if self._len_prediction is None:
-                self._len_prediction = len(active_speakers)
-            
-            # Get predictions for current chunk
-            frame_duration = self.chunk_duration_seconds / self._len_prediction
-            current_chunk_preds = active_speakers[-self._len_prediction:]
-            
-            with self.segment_lock:
-                # Process predictions into segments
-                base_time = self._chunk_index * self.chunk_duration_seconds + self.global_time_offset
-                
-                for idx, spk in enumerate(current_chunk_preds):
-                    start_time = base_time + idx * frame_duration
-                    end_time = base_time + (idx + 1) * frame_duration
-                    
-                    # Check if this continues the last segment or starts a new one
-                    if (self.speaker_segments and 
-                        self.speaker_segments[-1].speaker == spk and 
-                        abs(self.speaker_segments[-1].end - start_time) < frame_duration * 0.5):
-                        # Continue existing segment
-                        self.speaker_segments[-1].end = end_time
-                    else:
-                        
-                        # Create new segment
-                        self.speaker_segments.append(SpeakerSegment(
-                            speaker=spk,
-                            start=start_time,
-                            end=end_time
-                        ))
-                
-                # Update processed time
-                self.processed_time = max(self.processed_time, base_time + self.chunk_duration_seconds)
-                
-                logger.debug(f"Processed chunk {self._chunk_index}, total segments: {len(self.speaker_segments)}")
-                
-        except Exception as e:
-            logger.error(f"Error processing predictions: {e}")
-
-    def assign_speakers_to_tokens(self, tokens: list, use_punctuation_split: bool = False) -> list:
-        """
-        Assign speakers to tokens based on timing overlap with speaker segments.
+        preds_np = self.total_preds[0].cpu().numpy()
+        active_speakers = np.argmax(preds_np, axis=1)
         
-        Args:
-            tokens: List of tokens with timing information
-            use_punctuation_split: Whether to use punctuation for boundary refinement
-            
-        Returns:
-            List of tokens with speaker assignments
-        """
+        if self._len_prediction is None:
+            self._len_prediction = len(active_speakers) #12
+        
+        frame_duration = self.chunk_duration_seconds / self._len_prediction
+        current_chunk_preds = active_speakers[-self._len_prediction:]
+        
+        new_segments = []
+
         with self.segment_lock:
-            segments = self.speaker_segments.copy()
-        
-        if not segments or not tokens:
-            logger.debug("No segments or tokens available for speaker assignment")
-            return tokens
-        
-        logger.debug(f"Assigning speakers to {len(tokens)} tokens using {len(segments)} segments")
-        use_punctuation_split = False
-        if not use_punctuation_split:
-            # Simple overlap-based assignment
-            for token in tokens:
-                token.speaker = -1  # Default to no speaker
-                for segment in segments:
-                    # Check for timing overlap
-                    if not (segment.end <= token.start or segment.start >= token.end):
-                        token.speaker = segment.speaker + 1  # Convert to 1-based indexing
-                        break
-        else:
-            # Use punctuation-aware assignment (similar to diart_backend)
-            tokens = self._add_speaker_to_tokens_with_punctuation(segments, tokens)
-        
-        return tokens
-
-    def _add_speaker_to_tokens_with_punctuation(self, segments: List[SpeakerSegment], tokens: list) -> list:
-        """
-        Assign speakers to tokens with punctuation-aware boundary adjustment.
-        
-        Args:
-            segments: List of speaker segments
-            tokens: List of tokens to assign speakers to
-            
-        Returns:
-            List of tokens with speaker assignments
-        """
-        punctuation_marks = {'.', '!', '?'}
-        punctuation_tokens = [token for token in tokens if token.text.strip() in punctuation_marks]
-        
-        # Convert segments to concatenated format
-        segments_concatenated = self._concatenate_speakers(segments)
-        
-        # Adjust segment boundaries based on punctuation
-        for ind, segment in enumerate(segments_concatenated):
-            for i, punctuation_token in enumerate(punctuation_tokens):
-                if punctuation_token.start > segment['end']:
-                    after_length = punctuation_token.start - segment['end']
-                    before_length = segment['end'] - punctuation_tokens[i - 1].end if i > 0 else float('inf')
-                    
-                    if before_length > after_length:
-                        segment['end'] = punctuation_token.start
-                        if i < len(punctuation_tokens) - 1 and ind + 1 < len(segments_concatenated):
-                            segments_concatenated[ind + 1]['begin'] = punctuation_token.start
-                    else:
-                        segment['end'] = punctuation_tokens[i - 1].end if i > 0 else segment['end']
-                        if i < len(punctuation_tokens) - 1 and ind - 1 >= 0:
-                            segments_concatenated[ind - 1]['begin'] = punctuation_tokens[i - 1].end
-                    break
-        
-        # Ensure non-overlapping tokens
-        last_end = 0.0
-        for token in tokens:
-            start = max(last_end + 0.01, token.start)
-            token.start = start
-            token.end = max(start, token.end)
-            last_end = token.end
-        
-        # Assign speakers based on adjusted segments
-        ind_last_speaker = 0
-        for segment in segments_concatenated:
-            for i, token in enumerate(tokens[ind_last_speaker:]):
-                if token.end <= segment['end']:
-                    token.speaker = segment['speaker']
-                    ind_last_speaker = i + 1
-                elif token.start > segment['end']:
-                    break
-        
-        return tokens
-
-    def _concatenate_speakers(self, segments: List[SpeakerSegment]) -> List[dict]:
-        """
-        Concatenate consecutive segments from the same speaker.
-        
-        Args:
-            segments: List of speaker segments
-            
-        Returns:
-            List of concatenated speaker segments
-        """
-        if not segments:
-            return []
-            
-        segments_concatenated = [{"speaker": segments[0].speaker + 1, "begin": segments[0].start, "end": segments[0].end}]
-        
-        for segment in segments[1:]:
-            speaker = segment.speaker + 1
-            if segments_concatenated[-1]['speaker'] != speaker:
-                segments_concatenated.append({"speaker": speaker, "begin": segment.start, "end": segment.end})
-            else:
-                segments_concatenated[-1]['end'] = segment.end
+            base_time = self._chunk_index * self.chunk_duration_seconds + self.global_time_offset
+            current_spk = current_chunk_preds[0]
+            start_time = round(base_time, 2)
+            for idx, spk in enumerate(current_chunk_preds):
+                current_time = round(base_time + idx * frame_duration, 2)
+                if spk != current_spk:
+                    new_segments.append(SpeakerSegment(
+                        speaker=current_spk,
+                        start=start_time,
+                        end=current_time
+                    ))
+                    start_time = current_time
+                    current_spk = spk
+            new_segments.append(
+                SpeakerSegment(
+                        speaker=current_spk,
+                        start=start_time,
+                        end=current_time
+            )
+            )
+        return new_segments
                 
-        return segments_concatenated
-
     def get_segments(self) -> List[SpeakerSegment]:
         """Get a copy of the current speaker segments."""
         with self.segment_lock:
-            return self.speaker_segments.copy()
-
-    def clear_old_segments(self, older_than: float = 30.0):
-        """Clear segments older than the specified time."""
-        with self.segment_lock:
-            current_time = self.processed_time
-            self.speaker_segments = [
-                segment for segment in self.speaker_segments 
-                if current_time - segment.end < older_than
-            ]
-            logger.debug(f"Cleared old segments, remaining: {len(self.speaker_segments)}")
+            return self.diarization_segments.copy()
 
     def close(self):
         """Close the diarization system and clean up resources."""
         logger.info("Closing SortformerDiarization")
         with self.segment_lock:
-            self.speaker_segments.clear()
+            self.diarization_segments.clear()
         
         if self.debug:
             concatenated_audio = np.concatenate(self.audio_buffer)
@@ -416,20 +287,17 @@ class SortformerDiarizationOnline:
             logger.info(f"Saved {len(concatenated_audio)} samples to diarization_audio.wav")
 
 
-def extract_number(s: str) -> int:
-    """Extract number from speaker string (compatibility function)."""
-    import re
-    m = re.search(r'\d+', s)
-    return int(m.group()) if m else 0
+from whisperlivekit.diarization.utils import extract_number
 
 
 if __name__ == '__main__':
     import asyncio
+
     import librosa
     
     async def main():
         """TEST ONLY."""
-        an4_audio = 'audio_test.mp3'
+        an4_audio = 'diarization_audio.wav'
         signal, sr = librosa.load(an4_audio, sr=16000)
         signal = signal[:16000*30]
 
@@ -441,13 +309,15 @@ if __name__ == '__main__':
         print("Speaker 0: 0:25 - 0:30")
         print("=" * 50)
         
-        diarization = SortformerDiarization(sample_rate=16000)        
+        diarization_backend = SortformerDiarization()
+        diarization = SortformerDiarizationOnline(shared_model = diarization_backend)     
         chunk_size = 1600
         
         for i in range(0, len(signal), chunk_size):
             chunk = signal[i:i+chunk_size]
-            await diarization.diarize(chunk)
+            new_segments = await diarization.diarize(chunk)
             print(f"Processed chunk {i // chunk_size + 1}")
+            print(new_segments)
         
         segments = diarization.get_segments()
         print("\nDiarization results:")

whisperlivekit/diarization/sortformer_backend_offline.py

@@ -1,205 +0,0 @@
-import numpy as np
-import torch
-import logging
-
-from nemo.collections.asr.models import SortformerEncLabelModel
-from nemo.collections.asr.modules import AudioToMelSpectrogramPreprocessor
-import librosa
-
-logger = logging.getLogger(__name__)
-
-def load_model():
-
-    diar_model = SortformerEncLabelModel.from_pretrained("nvidia/diar_streaming_sortformer_4spk-v2")
-    diar_model.eval()
-
-    if torch.cuda.is_available():
-        diar_model.to(torch.device("cuda"))
-
-    #we target 1 second lag for the moment. chunk_len could be reduced.
-    diar_model.sortformer_modules.chunk_len = 10
-    diar_model.sortformer_modules.subsampling_factor = 10 #8 would be better ideally
-
-    diar_model.sortformer_modules.chunk_right_context = 0 #no.
-    diar_model.sortformer_modules.chunk_left_context = 10 #big so it compensiate the problem with no padding later.
-
-    diar_model.sortformer_modules.spkcache_len = 188
-    diar_model.sortformer_modules.fifo_len = 188
-    diar_model.sortformer_modules.spkcache_update_period = 144
-    diar_model.sortformer_modules.log = False
-    diar_model.sortformer_modules._check_streaming_parameters()
-
-
-    audio2mel = AudioToMelSpectrogramPreprocessor(
-            window_size= 0.025, 
-            normalize="NA",
-            n_fft=512,
-            features=128,
-            pad_to=0) #pad_to 16 works better than 0. On test audio, we detect a third speaker for 1 second with pad_to=0. To solve that : increase left context to 10.
-
-    return diar_model, audio2mel
-
-diar_model, audio2mel = load_model()
-
-class StreamingSortformerState:
-    """
-    This class creates a class instance that will be used to store the state of the
-    streaming Sortformer model.
-
-    Attributes:
-        spkcache (torch.Tensor): Speaker cache to store embeddings from start
-        spkcache_lengths (torch.Tensor): Lengths of the speaker cache
-        spkcache_preds (torch.Tensor): The speaker predictions for the speaker cache parts
-        fifo (torch.Tensor): FIFO queue to save the embedding from the latest chunks
-        fifo_lengths (torch.Tensor): Lengths of the FIFO queue
-        fifo_preds (torch.Tensor): The speaker predictions for the FIFO queue parts
-        spk_perm (torch.Tensor): Speaker permutation information for the speaker cache
-        mean_sil_emb (torch.Tensor): Mean silence embedding
-        n_sil_frames (torch.Tensor): Number of silence frames
-    """
-
-    spkcache = None  # Speaker cache to store embeddings from start
-    spkcache_lengths = None  #
-    spkcache_preds = None  # speaker cache predictions
-    fifo = None  # to save the embedding from the latest chunks
-    fifo_lengths = None
-    fifo_preds = None
-    spk_perm = None
-    mean_sil_emb = None
-    n_sil_frames = None
-
-
-def init_streaming_state(self, batch_size: int = 1, async_streaming: bool = False, device: torch.device = None):
-    """
-    Initializes StreamingSortformerState with empty tensors or zero-valued tensors.
-
-    Args:
-        batch_size (int): Batch size for tensors in streaming state
-        async_streaming (bool): True for asynchronous update, False for synchronous update
-        device (torch.device): Device for tensors in streaming state
-
-    Returns:
-        streaming_state (SortformerStreamingState): initialized streaming state
-    """
-    streaming_state = StreamingSortformerState()
-    if async_streaming:
-        streaming_state.spkcache = torch.zeros((batch_size, self.spkcache_len, self.fc_d_model), device=device)
-        streaming_state.spkcache_preds = torch.zeros((batch_size, self.spkcache_len, self.n_spk), device=device)
-        streaming_state.spkcache_lengths = torch.zeros((batch_size,), dtype=torch.long, device=device)
-        streaming_state.fifo = torch.zeros((batch_size, self.fifo_len, self.fc_d_model), device=device)
-        streaming_state.fifo_lengths = torch.zeros((batch_size,), dtype=torch.long, device=device)
-    else:
-        streaming_state.spkcache = torch.zeros((batch_size, 0, self.fc_d_model), device=device)
-        streaming_state.fifo = torch.zeros((batch_size, 0, self.fc_d_model), device=device)
-    streaming_state.mean_sil_emb = torch.zeros((batch_size, self.fc_d_model), device=device)
-    streaming_state.n_sil_frames = torch.zeros((batch_size,), dtype=torch.long, device=device)
-    return streaming_state
-
-
-def process_diarization(chunks):
-    """ 
-    what it does:
-    1. Preprocessing: Applies dithering and pre-emphasis (high-pass filter) if enabled
-    2. STFT: Computes the Short-Time Fourier Transform using:
-        - the window of window_size=0.025 --> size of a window : 400 samples
-        - the hop parameter : n_window_stride = 0.01 -> every 160 samples, a new window
-    3. Magnitude Calculation: Converts complex STFT output to magnitude spectrogram
-    4. Mel Conversion: Applies Mel filterbanks (128 filters in this case) to get Mel spectrogram
-    5. Logarithm: Takes the log of the Mel spectrogram (if `log=True`)
-    6. Normalization: Skips normalization since `normalize="NA"`
-    7. Padding: Pads the time dimension to a multiple of `pad_to` (default 16)    
-    """
-    previous_chunk = None
-    l_chunk_feat_seq_t = []
-    for chunk in chunks:
-        audio_signal_chunk = torch.tensor(chunk).unsqueeze(0).to(diar_model.device)
-        audio_signal_length_chunk = torch.tensor([audio_signal_chunk.shape[1]]).to(diar_model.device)
-        processed_signal_chunk, processed_signal_length_chunk = audio2mel.get_features(audio_signal_chunk, audio_signal_length_chunk)
-        if previous_chunk is not None:
-            to_add = previous_chunk[:, :, -99:]
-            total = torch.concat([to_add, processed_signal_chunk], dim=2)
-        else:
-            total = processed_signal_chunk
-        previous_chunk = processed_signal_chunk
-        l_chunk_feat_seq_t.append(torch.transpose(total, 1, 2))
-
-    batch_size = 1
-    streaming_state = init_streaming_state(diar_model.sortformer_modules,
-        batch_size = batch_size,
-        async_streaming = True,
-        device = diar_model.device
-    )
-    total_preds = torch.zeros((batch_size, 0, diar_model.sortformer_modules.n_spk), device=diar_model.device)
-
-    chunk_duration_seconds = diar_model.sortformer_modules.chunk_len * diar_model.sortformer_modules.subsampling_factor * diar_model.preprocessor._cfg.window_stride
-
-    l_speakers = [
-        {'start_time': 0,
-        'end_time': 0,
-        'speaker': 0
-        }
-    ]
-    len_prediction = None
-    left_offset = 0
-    right_offset = 8
-    for i, chunk_feat_seq_t in enumerate(l_chunk_feat_seq_t):
-        with torch.inference_mode():
-                streaming_state, total_preds = diar_model.forward_streaming_step(
-                    processed_signal=chunk_feat_seq_t,
-                    processed_signal_length=torch.tensor([chunk_feat_seq_t.shape[1]]),
-                    streaming_state=streaming_state,
-                    total_preds=total_preds,
-                    left_offset=left_offset,
-                    right_offset=right_offset,
-                )
-                left_offset = 8
-                preds_np = total_preds[0].cpu().numpy()
-                active_speakers = np.argmax(preds_np, axis=1)
-                if len_prediction is None:
-                    len_prediction = len(active_speakers) # we want to get the len of 1 prediction
-                frame_duration = chunk_duration_seconds / len_prediction
-                active_speakers = active_speakers[-len_prediction:]
-                for idx, spk in enumerate(active_speakers):
-                    if spk != l_speakers[-1]['speaker']:
-                        l_speakers.append(
-                            {'start_time': (i * chunk_duration_seconds + idx * frame_duration),
-                            'end_time': (i * chunk_duration_seconds + (idx + 1) * frame_duration),
-                            'speaker': spk
-                        })                    
-                    else:
-                        l_speakers[-1]['end_time'] = i * chunk_duration_seconds + (idx + 1) * frame_duration
-                    
-        
-        """
-        Should print
-        [{'start_time': 0, 'end_time': 8.72, 'speaker': 0}, 
-        {'start_time': 8.72, 'end_time': 18.88, 'speaker': 1},
-        {'start_time': 18.88, 'end_time': 24.96, 'speaker': 2},
-        {'start_time': 24.96, 'end_time': 31.68, 'speaker': 0}]
-        """
-    for speaker in l_speakers:
-        print(f"Speaker {speaker['speaker']}: {speaker['start_time']:.2f}s - {speaker['end_time']:.2f}s")    
-    
-
-if __name__ == '__main__':
-    
-    an4_audio = 'audio_test.mp3'
-    signal, sr = librosa.load(an4_audio, sr=16000)
-    signal = signal[:16000*30]
-    # signal = signal[:-(len(signal)%16000)]
-
-    print("\n" + "=" * 50)
-    print("Expected ground truth:")
-    print("Speaker 0: 0:00 - 0:09")
-    print("Speaker 1: 0:09 - 0:19") 
-    print("Speaker 2: 0:19 - 0:25")
-    print("Speaker 0: 0:25 - 0:30")
-    print("=" * 50)
-
-    chunk_size = 16000  # 1 second
-    chunks = []
-    for i in range(0, len(signal), chunk_size):
-        chunk = signal[i:i+chunk_size]
-        chunks.append(chunk)
-    
-    process_diarization(chunks)

whisperlivekit/diarization/utils.py

@@ -0,0 +1,7 @@
+import re
+
+
+def extract_number(s: str) -> int:
+    """Extract the first integer from a string, e.g. 'speaker_2' -> 2."""
+    m = re.search(r'\d+', s)
+    return int(m.group()) if m else 0

whisperlivekit/ffmpeg_manager.py

@@ -1,17 +1,18 @@
 import asyncio
+import contextlib
 import logging
 from enum import Enum
-from typing import Optional, Callable
-import contextlib
+from typing import Callable, Optional
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
 
-ERROR_INSTALL_INSTRUCTIONS = """
+ERROR_INSTALL_INSTRUCTIONS = f"""
+{'='*50}
 FFmpeg is not installed or not found in your system's PATH.
-Please install FFmpeg to enable audio processing.
+Alternative Solution: You can still use WhisperLiveKit without FFmpeg by adding the --pcm-input parameter. Note that when using this option, audio will not be compressed between the frontend and backend, which may result in higher bandwidth usage.
 
-Installation instructions:
+If you want to install FFmpeg:
 
 # Ubuntu/Debian:
 sudo apt update && sudo apt install ffmpeg
@@ -25,6 +26,7 @@ brew install ffmpeg
 # 3. Add the 'bin' directory (e.g., C:\\FFmpeg\\bin) to your system's PATH environment variable.
 
 After installation, please restart the application.
+{'='*50}
 """
 
 class FFmpegState(Enum):
@@ -183,6 +185,8 @@ class FFmpegManager:
     async def _drain_stderr(self):
         try:
             while True:
+                if not self.process or not self.process.stderr:
+                    break
                 line = await self.process.stderr.readline()
                 if not line:
                     break
@@ -190,4 +194,4 @@ class FFmpegManager:
         except asyncio.CancelledError:
             logger.info("FFmpeg stderr drain task cancelled.")
         except Exception as e:
-            logger.error(f"Error draining FFmpeg stderr: {e}")
+            logger.error(f"Error draining FFmpeg stderr: {e}")

whisperlivekit/local_agreement/backends.py

@@ -1,33 +1,32 @@
-import sys
-import logging
 import io
-import soundfile as sf
+import logging
 import math
+import sys
 from typing import List
+
 import numpy as np
+import soundfile as sf
+
+from whisperlivekit.model_paths import detect_model_format, resolve_model_path
 from whisperlivekit.timed_objects import ASRToken
+from whisperlivekit.whisper.transcribe import transcribe as whisper_transcribe
+
 logger = logging.getLogger(__name__)
 class ASRBase:
     sep = " "  # join transcribe words with this character (" " for whisper_timestamped,
               # "" for faster-whisper because it emits the spaces when needed)
 
-    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr):
+    def __init__(self, lan, model_size=None, cache_dir=None, model_dir=None, lora_path=None, logfile=sys.stderr):
         self.logfile = logfile
         self.transcribe_kargs = {}
+        self.lora_path = lora_path
         if lan == "auto":
             self.original_language = None
         else:
             self.original_language = lan
-        self.model = self.load_model(modelsize, cache_dir, model_dir)
+        self.model = self.load_model(model_size, cache_dir, model_dir)
 
-    def with_offset(self, offset: float) -> ASRToken:
-        # This method is kept for compatibility (typically you will use ASRToken.with_offset)
-        return ASRToken(self.start + offset, self.end + offset, self.text)
-
-    def __repr__(self):
-        return f"ASRToken(start={self.start:.2f}, end={self.end:.2f}, text={self.text!r})"
-
-    def load_model(self, modelsize, cache_dir, model_dir):
+    def load_model(self, model_size, cache_dir, model_dir):
         raise NotImplementedError("must be implemented in the child class")
 
     def transcribe(self, audio, init_prompt=""):
@@ -37,40 +36,59 @@ class ASRBase:
         raise NotImplementedError("must be implemented in the child class")
 
 
-class WhisperTimestampedASR(ASRBase):
-    """Uses whisper_timestamped as the backend."""
+class WhisperASR(ASRBase):
+    """Uses WhisperLiveKit's built-in Whisper implementation."""
     sep = " "
 
-    def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
-        import whisper
-        import whisper_timestamped
-        from whisper_timestamped import transcribe_timestamped
+    def load_model(self, model_size=None, cache_dir=None, model_dir=None):
+        from whisperlivekit.whisper import load_model as load_whisper_model
 
-        self.transcribe_timestamped = transcribe_timestamped
         if model_dir is not None:
-            logger.debug("ignoring model_dir, not implemented")
-        return whisper.load_model(modelsize, download_root=cache_dir)
+            resolved_path = resolve_model_path(model_dir)            
+            if resolved_path.is_dir():
+                model_info = detect_model_format(resolved_path)
+                if not model_info.has_pytorch:
+                    raise FileNotFoundError(
+                        f"No supported PyTorch checkpoint found under {resolved_path}"
+                    )            
+            logger.debug(f"Loading Whisper model from custom path {resolved_path}")
+            return load_whisper_model(str(resolved_path), lora_path=self.lora_path)
+
+        if model_size is None:
+            raise ValueError("Either model_size or model_dir must be set for WhisperASR")
+
+        return load_whisper_model(model_size, download_root=cache_dir, lora_path=self.lora_path)
 
     def transcribe(self, audio, init_prompt=""):
-        result = self.transcribe_timestamped(
+        options = dict(self.transcribe_kargs)
+        options.pop("vad", None)
+        options.pop("vad_filter", None)
+        language = self.original_language if self.original_language else None
+
+        result = whisper_transcribe(
             self.model,
             audio,
-            language=self.original_language,
+            language=language,
             initial_prompt=init_prompt,
-            verbose=None,
             condition_on_previous_text=True,
-            **self.transcribe_kargs,
+            word_timestamps=True,
+            **options,
         )
         return result
 
     def ts_words(self, r) -> List[ASRToken]:
         """
-        Converts the whisper_timestamped result to a list of ASRToken objects.
+        Converts the Whisper result to a list of ASRToken objects.
         """
         tokens = []
         for segment in r["segments"]:
             for word in segment["words"]:
-                token = ASRToken(word["start"], word["end"], word["text"])
+                token = ASRToken(
+                    word["start"],
+                    word["end"],
+                    word["word"],
+                    probability=word.get("probability"),
+                )
                 tokens.append(token)
         return tokens
 
@@ -78,27 +96,24 @@ class WhisperTimestampedASR(ASRBase):
         return [segment["end"] for segment in res["segments"]]
 
     def use_vad(self):
-        self.transcribe_kargs["vad"] = True
-
-    def set_translate_task(self):
-        self.transcribe_kargs["task"] = "translate"
-
+        logger.warning("VAD is not currently supported for WhisperASR backend and will be ignored.")
 
 class FasterWhisperASR(ASRBase):
     """Uses faster-whisper as the backend."""
     sep = ""
 
-    def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
+    def load_model(self, model_size=None, cache_dir=None, model_dir=None):
         from faster_whisper import WhisperModel
 
         if model_dir is not None:
-            logger.debug(f"Loading whisper model from model_dir {model_dir}. "
-                         f"modelsize and cache_dir parameters are not used.")
-            model_size_or_path = model_dir
-        elif modelsize is not None:
-            model_size_or_path = modelsize
+            resolved_path = resolve_model_path(model_dir)
+            logger.debug(f"Loading faster-whisper model from {resolved_path}. "
+                         f"model_size and cache_dir parameters are not used.")
+            model_size_or_path = str(resolved_path)
+        elif model_size is not None:
+            model_size_or_path = model_size
         else:
-            raise ValueError("Either modelsize or model_dir must be set")
+            raise ValueError("Either model_size or model_dir must be set")
         device = "auto" # Allow CTranslate2 to decide available device
         compute_type = "auto" # Allow CTranslate2 to decide faster compute type
                               
@@ -139,28 +154,25 @@ class FasterWhisperASR(ASRBase):
     def use_vad(self):
         self.transcribe_kargs["vad_filter"] = True
 
-    def set_translate_task(self):
-        self.transcribe_kargs["task"] = "translate"
-
-
 class MLXWhisper(ASRBase):
     """
     Uses MLX Whisper optimized for Apple Silicon.
     """
     sep = ""
 
-    def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
-        from mlx_whisper.transcribe import ModelHolder, transcribe
+    def load_model(self, model_size=None, cache_dir=None, model_dir=None):
         import mlx.core as mx
+        from mlx_whisper.transcribe import ModelHolder, transcribe
 
         if model_dir is not None:
-            logger.debug(f"Loading whisper model from model_dir {model_dir}. modelsize parameter is not used.")
-            model_size_or_path = model_dir
-        elif modelsize is not None:
-            model_size_or_path = self.translate_model_name(modelsize)
-            logger.debug(f"Loading whisper model {modelsize}. You use mlx whisper, so {model_size_or_path} will be used.")
+            resolved_path = resolve_model_path(model_dir)
+            logger.debug(f"Loading MLX Whisper model from {resolved_path}. model_size parameter is not used.")
+            model_size_or_path = str(resolved_path)
+        elif model_size is not None:
+            model_size_or_path = self.translate_model_name(model_size)
+            logger.debug(f"Loading whisper model {model_size}. You use mlx whisper, so {model_size_or_path} will be used.")
         else:
-            raise ValueError("Either modelsize or model_dir must be set")
+            raise ValueError("Either model_size or model_dir must be set")
 
         self.model_size_or_path = model_size_or_path
         dtype = mx.float16
@@ -168,22 +180,8 @@ class MLXWhisper(ASRBase):
         return transcribe
 
     def translate_model_name(self, model_name):
-        model_mapping = {
-            "tiny.en": "mlx-community/whisper-tiny.en-mlx",
-            "tiny": "mlx-community/whisper-tiny-mlx",
-            "base.en": "mlx-community/whisper-base.en-mlx",
-            "base": "mlx-community/whisper-base-mlx",
-            "small.en": "mlx-community/whisper-small.en-mlx",
-            "small": "mlx-community/whisper-small-mlx",
-            "medium.en": "mlx-community/whisper-medium.en-mlx",
-            "medium": "mlx-community/whisper-medium-mlx",
-            "large-v1": "mlx-community/whisper-large-v1-mlx",
-            "large-v2": "mlx-community/whisper-large-v2-mlx",
-            "large-v3": "mlx-community/whisper-large-v3-mlx",
-            "large-v3-turbo": "mlx-community/whisper-large-v3-turbo",
-            "large": "mlx-community/whisper-large-mlx",
-        }
-        mlx_model_path = model_mapping.get(model_name)
+        from whisperlivekit.model_mapping import MLX_MODEL_MAPPING
+        mlx_model_path = MLX_MODEL_MAPPING.get(model_name)
         if mlx_model_path:
             return mlx_model_path
         else:
@@ -208,7 +206,7 @@ class MLXWhisper(ASRBase):
             if segment.get("no_speech_prob", 0) > 0.9:
                 continue
             for word in segment.get("words", []):
-                token = ASRToken(word["start"], word["end"], word["word"], probability=word["probability"])
+                token = ASRToken(word["start"], word["end"], word["word"])
                 tokens.append(token)
         return tokens
 
@@ -218,9 +216,6 @@ class MLXWhisper(ASRBase):
     def use_vad(self):
         self.transcribe_kargs["vad_filter"] = True
 
-    def set_translate_task(self):
-        self.transcribe_kargs["task"] = "translate"
-
 
 class OpenaiApiASR(ASRBase):
     """Uses OpenAI's Whisper API for transcription."""
@@ -232,6 +227,7 @@ class OpenaiApiASR(ASRBase):
         self.temperature = temperature
         self.load_model()
         self.use_vad_opt = False
+        self.direct_english_translation = False
         self.task = "transcribe"
 
     def load_model(self, *args, **kwargs):
@@ -274,17 +270,15 @@ class OpenaiApiASR(ASRBase):
             "temperature": self.temperature,
             "timestamp_granularities": ["word", "segment"],
         }
-        if self.task != "translate" and self.original_language:
+        if not self.direct_english_translation and self.original_language:
             params["language"] = self.original_language
         if prompt:
             params["prompt"] = prompt
-        proc = self.client.audio.translations if self.task == "translate" else self.client.audio.transcriptions
+        task = self.transcribe_kargs.get("task", self.task)
+        proc = self.client.audio.translations if task == "translate" else self.client.audio.transcriptions
         transcript = proc.create(**params)
         logger.debug(f"OpenAI API processed accumulated {self.transcribed_seconds} seconds")
         return transcript
 
     def use_vad(self):
         self.use_vad_opt = True
-
-    def set_translate_task(self):
-        self.task = "translate"

whisperlivekit/local_agreement/online_asr.py

@@ -1,7 +1,9 @@
-import sys
-import numpy as np
 import logging
-from typing import List, Tuple, Optional
+import sys
+from typing import List, Optional, Tuple
+
+import numpy as np
+
 from whisperlivekit.timed_objects import ASRToken, Sentence, Transcript
 
 logger = logging.getLogger(__name__)
@@ -106,9 +108,6 @@ class OnlineASRProcessor:
     def __init__(
         self,
         asr,
-        tokenize_method: Optional[callable] = None,
-        buffer_trimming: Tuple[str, float] = ("segment", 15),
-        confidence_validation = False,
         logfile=sys.stderr,
     ):
         """
@@ -119,13 +118,14 @@ class OnlineASRProcessor:
         buffer_trimming: A tuple (option, seconds), where option is either "sentence" or "segment".
         """
         self.asr = asr
-        self.tokenize = tokenize_method
+        self.tokenize = asr.tokenizer
         self.logfile = logfile
-        self.confidence_validation = confidence_validation
+        self.confidence_validation = asr.confidence_validation
         self.global_time_offset = 0.0
         self.init()
 
-        self.buffer_trimming_way, self.buffer_trimming_sec = buffer_trimming
+        self.buffer_trimming_way = asr.buffer_trimming
+        self.buffer_trimming_sec = asr.buffer_trimming_sec
 
         if self.buffer_trimming_way not in ["sentence", "segment"]:
             raise ValueError("buffer_trimming must be either 'sentence' or 'segment'")
@@ -136,6 +136,11 @@ class OnlineASRProcessor:
                 f"buffer_trimming_sec is set to {self.buffer_trimming_sec}, which is very long. It may cause OOM."
             )
 
+    def new_speaker(self, change_speaker):
+        """Handle speaker change event."""
+        self.process_iter()
+        self.init(offset=change_speaker.start)
+
     def init(self, offset: Optional[float] = None):
         """Initialize or reset the processing buffers."""
         self.audio_buffer = np.array([], dtype=np.float32)
@@ -153,21 +158,32 @@ class OnlineASRProcessor:
         """Append an audio chunk (a numpy array) to the current audio buffer."""
         self.audio_buffer = np.append(self.audio_buffer, audio)
 
-    def insert_silence(self, silence_duration, offset):
-        """
-        If silences are > 5s, we do a complete context clear. Otherwise, we just insert a small silence and shift the last_attend_frame
-        """
-        # if self.transcript_buffer.buffer:
-        #     self.committed.extend(self.transcript_buffer.buffer)
-        #     self.transcript_buffer.buffer = []
-            
-        if True: #silence_duration < 3: #we want the last audio to be treated to not have a gap. could also be handled in the future in ends_with_silence.
-            gap_silence = np.zeros(int(16000 * silence_duration), dtype=np.int16)
-            self.insert_audio_chunk(gap_silence)
+    def start_silence(self):
+        if self.audio_buffer.size == 0:
+            return [], self.get_audio_buffer_end_time()
+        return self.process_iter()
+
+    def end_silence(self, silence_duration: Optional[float], offset: float):
+        if not silence_duration or silence_duration <= 0:
+            return
+
+        long_silence = silence_duration >= 5
+        if not long_silence:
+            gap_samples = int(self.SAMPLING_RATE * silence_duration)
+            if gap_samples > 0:
+                gap_silence = np.zeros(gap_samples, dtype=np.float32)
+                self.insert_audio_chunk(gap_silence)
         else:
             self.init(offset=silence_duration + offset)
+
         self.global_time_offset += silence_duration
 
+    def insert_silence(self, silence_duration, offset):
+        """
+        Backwards compatibility shim for legacy callers that still use insert_silence.
+        """
+        self.end_silence(silence_duration, offset)
+
     def prompt(self) -> Tuple[str, str]:
         """
         Returns a tuple: (prompt, context), where:
@@ -402,11 +418,11 @@ class OnlineASRProcessor:
     ) -> Transcript:
         sep = sep if sep is not None else self.asr.sep
         text = sep.join(token.text for token in tokens)
-        probability = sum(token.probability for token in tokens if token.probability) / len(tokens) if tokens else None
+        # probability = sum(token.probability for token in tokens if token.probability) / len(tokens) if tokens else None
         if tokens:
             start = offset + tokens[0].start
             end = offset + tokens[-1].end
         else:
             start = None
             end = None
-        return Transcript(start, end, text, probability=probability)
+        return Transcript(start, end, text)

whisperlivekit/local_agreement/whisper_online.py

@@ -0,0 +1,202 @@
+#!/usr/bin/env python3
+import logging
+import platform
+import time
+
+from whisperlivekit.backend_support import (faster_backend_available,
+                                            mlx_backend_available)
+from whisperlivekit.model_paths import detect_model_format, resolve_model_path
+from whisperlivekit.warmup import warmup_asr
+
+from .backends import FasterWhisperASR, MLXWhisper, OpenaiApiASR, WhisperASR
+
+logger = logging.getLogger(__name__)
+
+
+WHISPER_LANG_CODES = "af,am,ar,as,az,ba,be,bg,bn,bo,br,bs,ca,cs,cy,da,de,el,en,es,et,eu,fa,fi,fo,fr,gl,gu,ha,haw,he,hi,hr,ht,hu,hy,id,is,it,ja,jw,ka,kk,km,kn,ko,la,lb,ln,lo,lt,lv,mg,mi,mk,ml,mn,mr,ms,mt,my,ne,nl,nn,no,oc,pa,pl,ps,pt,ro,ru,sa,sd,si,sk,sl,sn,so,sq,sr,su,sv,sw,ta,te,tg,th,tk,tl,tr,tt,uk,ur,uz,vi,yi,yo,zh".split(
+    ","
+)
+
+
+def create_tokenizer(lan):
+    """returns an object that has split function that works like the one of MosesTokenizer"""
+
+    assert (
+        lan in WHISPER_LANG_CODES
+    ), "language must be Whisper's supported lang code: " + " ".join(WHISPER_LANG_CODES)
+
+    if lan == "uk":
+        import tokenize_uk
+
+        class UkrainianTokenizer:
+            def split(self, text):
+                return tokenize_uk.tokenize_sents(text)
+
+        return UkrainianTokenizer()
+
+    # supported by fast-mosestokenizer
+    if (
+        lan
+        in "as bn ca cs de el en es et fi fr ga gu hi hu is it kn lt lv ml mni mr nl or pa pl pt ro ru sk sl sv ta te yue zh".split()
+    ):
+        from mosestokenizer import MosesSentenceSplitter        
+
+        return MosesSentenceSplitter(lan)
+
+    # the following languages are in Whisper, but not in wtpsplit:
+    if (
+        lan
+        in "as ba bo br bs fo haw hr ht jw lb ln lo mi nn oc sa sd sn so su sw tk tl tt".split()
+    ):
+        logger.debug(
+            f"{lan} code is not supported by wtpsplit. Going to use None lang_code option."
+        )
+        lan = None
+
+    from wtpsplit import WtP
+
+    # downloads the model from huggingface on the first use
+    wtp = WtP("wtp-canine-s-12l-no-adapters")
+
+    class WtPtok:
+        def split(self, sent):
+            return wtp.split(sent, lang_code=lan)
+
+    return WtPtok()
+
+
+def backend_factory(
+            backend,
+            lan,
+            model_size,
+            model_cache_dir,
+            model_dir,
+            model_path,
+            lora_path,
+            direct_english_translation,
+            buffer_trimming,
+            buffer_trimming_sec,
+            confidence_validation,
+            warmup_file=None,
+            min_chunk_size=None,
+        ):
+    backend_choice = backend
+    custom_reference = model_path or model_dir
+    resolved_root = None
+    has_mlx_weights = False
+    has_fw_weights = False
+    has_pytorch = False
+
+    if custom_reference:
+        resolved_root = resolve_model_path(custom_reference)
+        if resolved_root.is_dir():
+            model_info = detect_model_format(resolved_root)
+            has_mlx_weights = model_info.compatible_whisper_mlx
+            has_fw_weights = model_info.compatible_faster_whisper
+            has_pytorch = model_info.has_pytorch
+        else:
+            # Single file provided
+            has_pytorch = True
+
+    if backend_choice == "openai-api":
+        logger.debug("Using OpenAI API.")
+        asr = OpenaiApiASR(lan=lan)
+    else:
+        backend_choice = _normalize_backend_choice(
+            backend_choice,
+            resolved_root,
+            has_mlx_weights,
+            has_fw_weights,
+        )
+
+        if backend_choice == "faster-whisper":
+            asr_cls = FasterWhisperASR
+            if resolved_root is not None and not resolved_root.is_dir():
+                raise ValueError("Faster-Whisper backend expects a directory with CTranslate2 weights.")
+            model_override = str(resolved_root) if resolved_root is not None else None
+        elif backend_choice == "mlx-whisper":
+            asr_cls = MLXWhisper
+            if resolved_root is not None and not resolved_root.is_dir():
+                raise ValueError("MLX Whisper backend expects a directory containing MLX weights.")
+            model_override = str(resolved_root) if resolved_root is not None else None
+        else:
+            asr_cls = WhisperASR
+            model_override = str(resolved_root) if resolved_root is not None else None
+            if custom_reference and not has_pytorch:
+                raise FileNotFoundError(
+                    f"No PyTorch checkpoint found under {resolved_root or custom_reference}"
+                )
+
+        t = time.time()
+        logger.info(f"Loading Whisper {model_size} model for language {lan} using backend {backend_choice}...")
+        asr = asr_cls(
+            model_size=model_size,
+            lan=lan,
+            cache_dir=model_cache_dir,
+            model_dir=model_override,
+            lora_path=lora_path if backend_choice == "whisper" else None,
+        )
+        e = time.time()
+        logger.info(f"done. It took {round(e-t,2)} seconds.")
+
+    if direct_english_translation:
+        tgt_language = "en"  # Whisper translates into English
+        asr.transcribe_kargs["task"] = "translate"
+    else:
+        tgt_language = lan  # Whisper transcribes in this language
+
+    # Create the tokenizer
+    if buffer_trimming == "sentence":
+        tokenizer = create_tokenizer(tgt_language)
+    else:
+        tokenizer = None
+
+    warmup_asr(asr, warmup_file)
+
+    asr.confidence_validation = confidence_validation
+    asr.tokenizer = tokenizer
+    asr.buffer_trimming = buffer_trimming
+    asr.buffer_trimming_sec = buffer_trimming_sec
+    asr.backend_choice = backend_choice
+    return asr
+
+
+def _normalize_backend_choice(
+    preferred_backend,
+    resolved_root,
+    has_mlx_weights,
+    has_fw_weights,
+):
+    backend_choice = preferred_backend
+
+    if backend_choice == "auto":
+        if mlx_backend_available(warn_on_missing=True) and (resolved_root is None or has_mlx_weights):
+            return "mlx-whisper"
+        if faster_backend_available(warn_on_missing=True) and (resolved_root is None or has_fw_weights):
+            return "faster-whisper"
+        return "whisper"
+
+    if backend_choice == "mlx-whisper":
+        if not mlx_backend_available():
+            raise RuntimeError("mlx-whisper backend requested but mlx-whisper is not installed.")
+        if resolved_root is not None and not has_mlx_weights:
+            raise FileNotFoundError(
+                f"mlx-whisper backend requested but no MLX weights were found under {resolved_root}"
+            )
+        if platform.system() != "Darwin":
+            logger.warning("mlx-whisper backend requested on a non-macOS system; this may fail.")
+        return backend_choice
+
+    if backend_choice == "faster-whisper":
+        if not faster_backend_available():
+            raise RuntimeError("faster-whisper backend requested but faster-whisper is not installed.")
+        if resolved_root is not None and not has_fw_weights:
+            raise FileNotFoundError(
+                f"faster-whisper backend requested but no Faster-Whisper weights were found under {resolved_root}"
+            )
+        return backend_choice
+
+    if backend_choice == "whisper":
+        return backend_choice
+
+    raise ValueError(f"Unknown backend '{preferred_backend}' for LocalAgreement.")

whisperlivekit/metrics.py

@@ -0,0 +1,156 @@
+"""Lightweight ASR evaluation metrics — no external dependencies.
+
+Provides WER (Word Error Rate) computation via word-level Levenshtein distance,
+text normalization, and word-level timestamp accuracy metrics with greedy alignment.
+"""
+
+import re
+import unicodedata
+from typing import Dict, List, Optional
+
+
+def normalize_text(text: str) -> str:
+    """Normalize text for WER comparison: lowercase, strip punctuation, collapse whitespace."""
+    text = text.lower()
+    # Normalize unicode (e.g., accented chars to composed form)
+    text = unicodedata.normalize("NFC", text)
+    # Remove punctuation (keep letters, numbers, spaces, hyphens within words)
+    text = re.sub(r"[^\w\s\-']", " ", text)
+    # Collapse whitespace
+    text = re.sub(r"\s+", " ", text).strip()
+    return text
+
+
+def compute_wer(reference: str, hypothesis: str) -> Dict:
+    """Compute Word Error Rate using word-level Levenshtein edit distance.
+
+    Args:
+        reference: Ground truth transcription.
+        hypothesis: Predicted transcription.
+
+    Returns:
+        Dict with keys: wer, substitutions, insertions, deletions, ref_words, hyp_words.
+        WER can exceed 1.0 if there are more errors than reference words.
+    """
+    ref_words = normalize_text(reference).split()
+    hyp_words = normalize_text(hypothesis).split()
+
+    n = len(ref_words)
+    m = len(hyp_words)
+
+    if n == 0:
+        return {
+            "wer": 0.0 if m == 0 else float(m),
+            "substitutions": 0,
+            "insertions": m,
+            "deletions": 0,
+            "ref_words": 0,
+            "hyp_words": m,
+        }
+
+    # DP table: dp[i][j] = (edit_distance, substitutions, insertions, deletions)
+    dp = [[(0, 0, 0, 0) for _ in range(m + 1)] for _ in range(n + 1)]
+
+    for i in range(1, n + 1):
+        dp[i][0] = (i, 0, 0, i)
+    for j in range(1, m + 1):
+        dp[0][j] = (j, 0, j, 0)
+
+    for i in range(1, n + 1):
+        for j in range(1, m + 1):
+            if ref_words[i - 1] == hyp_words[j - 1]:
+                dp[i][j] = dp[i - 1][j - 1]
+            else:
+                sub = dp[i - 1][j - 1]
+                ins = dp[i][j - 1]
+                dele = dp[i - 1][j]
+
+                sub_cost = (sub[0] + 1, sub[1] + 1, sub[2], sub[3])
+                ins_cost = (ins[0] + 1, ins[1], ins[2] + 1, ins[3])
+                del_cost = (dele[0] + 1, dele[1], dele[2], dele[3] + 1)
+
+                dp[i][j] = min(sub_cost, del_cost, ins_cost, key=lambda x: x[0])
+
+    dist, subs, ins, dels = dp[n][m]
+    return {
+        "wer": dist / n,
+        "substitutions": subs,
+        "insertions": ins,
+        "deletions": dels,
+        "ref_words": n,
+        "hyp_words": m,
+    }
+
+
+def compute_timestamp_accuracy(
+    predicted: List[Dict],
+    reference: List[Dict],
+) -> Dict:
+    """Compute timestamp accuracy by aligning predicted words to reference words.
+
+    Uses greedy left-to-right alignment on normalized text. For each matched pair,
+    computes the start-time delta (predicted - reference).
+
+    Args:
+        predicted: List of dicts with keys: word, start, end.
+        reference: List of dicts with keys: word, start, end.
+
+    Returns:
+        Dict with keys: mae_start, max_delta_start, median_delta_start,
+        n_matched, n_ref, n_pred. Returns None values if no matches found.
+    """
+    if not predicted or not reference:
+        return {
+            "mae_start": None,
+            "max_delta_start": None,
+            "median_delta_start": None,
+            "n_matched": 0,
+            "n_ref": len(reference),
+            "n_pred": len(predicted),
+        }
+
+    # Normalize words for matching
+    pred_norm = [normalize_text(p["word"]) for p in predicted]
+    ref_norm = [normalize_text(r["word"]) for r in reference]
+
+    # Greedy left-to-right alignment
+    deltas_start = []
+    ref_idx = 0
+    for p_idx, p_word in enumerate(pred_norm):
+        if not p_word:
+            continue
+        # Scan forward in reference to find a match (allow small skips)
+        search_limit = min(ref_idx + 3, len(ref_norm))
+        for r_idx in range(ref_idx, search_limit):
+            if ref_norm[r_idx] == p_word:
+                delta = predicted[p_idx]["start"] - reference[r_idx]["start"]
+                deltas_start.append(delta)
+                ref_idx = r_idx + 1
+                break
+
+    if not deltas_start:
+        return {
+            "mae_start": None,
+            "max_delta_start": None,
+            "median_delta_start": None,
+            "n_matched": 0,
+            "n_ref": len(reference),
+            "n_pred": len(predicted),
+        }
+
+    abs_deltas = [abs(d) for d in deltas_start]
+    sorted_abs = sorted(abs_deltas)
+    n = len(sorted_abs)
+    if n % 2 == 1:
+        median = sorted_abs[n // 2]
+    else:
+        median = (sorted_abs[n // 2 - 1] + sorted_abs[n // 2]) / 2
+
+    return {
+        "mae_start": sum(abs_deltas) / len(abs_deltas),
+        "max_delta_start": max(abs_deltas),
+        "median_delta_start": median,
+        "n_matched": len(deltas_start),
+        "n_ref": len(reference),
+        "n_pred": len(predicted),
+    }

whisperlivekit/metrics_collector.py

@@ -0,0 +1,84 @@
+"""Lightweight runtime metrics for AudioProcessor sessions.
+
+Zero external dependencies. Negligible overhead when not queried —
+just integer increments and list appends during normal operation.
+"""
+
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Dict, List
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SessionMetrics:
+    """Per-session metrics collected by AudioProcessor."""
+
+    session_start: float = 0.0
+    total_audio_duration_s: float = 0.0
+    total_processing_time_s: float = 0.0
+
+    # Chunk / call counters
+    n_chunks_received: int = 0
+    n_transcription_calls: int = 0
+    n_tokens_produced: int = 0
+    n_responses_sent: int = 0
+
+    # Per-call ASR latency (seconds)
+    transcription_durations: List[float] = field(default_factory=list)
+
+    # Silence
+    n_silence_events: int = 0
+    total_silence_duration_s: float = 0.0
+
+    # --- Computed properties ---
+
+    @property
+    def rtf(self) -> float:
+        """Real-time factor: processing_time / audio_duration."""
+        if self.total_audio_duration_s <= 0:
+            return 0.0
+        return self.total_processing_time_s / self.total_audio_duration_s
+
+    @property
+    def avg_latency_ms(self) -> float:
+        """Average per-call ASR latency in milliseconds."""
+        if not self.transcription_durations:
+            return 0.0
+        return (sum(self.transcription_durations) / len(self.transcription_durations)) * 1000
+
+    @property
+    def p95_latency_ms(self) -> float:
+        """95th percentile per-call ASR latency in milliseconds."""
+        if not self.transcription_durations:
+            return 0.0
+        sorted_d = sorted(self.transcription_durations)
+        idx = int(len(sorted_d) * 0.95)
+        idx = min(idx, len(sorted_d) - 1)
+        return sorted_d[idx] * 1000
+
+    def to_dict(self) -> Dict:
+        """Serialize to a plain dict (JSON-safe)."""
+        return {
+            "session_start": self.session_start,
+            "total_audio_duration_s": round(self.total_audio_duration_s, 3),
+            "total_processing_time_s": round(self.total_processing_time_s, 3),
+            "rtf": round(self.rtf, 3),
+            "n_chunks_received": self.n_chunks_received,
+            "n_transcription_calls": self.n_transcription_calls,
+            "n_tokens_produced": self.n_tokens_produced,
+            "n_responses_sent": self.n_responses_sent,
+            "avg_latency_ms": round(self.avg_latency_ms, 2),
+            "p95_latency_ms": round(self.p95_latency_ms, 2),
+            "n_silence_events": self.n_silence_events,
+            "total_silence_duration_s": round(self.total_silence_duration_s, 3),
+        }
+
+    def log_summary(self) -> None:
+        """Emit a structured log line summarising the session."""
+        self.total_processing_time_s = sum(self.transcription_durations)
+        d = self.to_dict()
+        d["session_elapsed_s"] = round(time.time() - self.session_start, 3) if self.session_start else 0
+        logger.info(f"SESSION_METRICS {d}")

whisperlivekit/model_mapping.py

@@ -0,0 +1,17 @@
+"""Shared MLX model name mapping used by both SimulStreaming and LocalAgreement backends."""
+
+MLX_MODEL_MAPPING = {
+    "tiny.en": "mlx-community/whisper-tiny.en-mlx",
+    "tiny": "mlx-community/whisper-tiny-mlx",
+    "base.en": "mlx-community/whisper-base.en-mlx",
+    "base": "mlx-community/whisper-base-mlx",
+    "small.en": "mlx-community/whisper-small.en-mlx",
+    "small": "mlx-community/whisper-small-mlx",
+    "medium.en": "mlx-community/whisper-medium.en-mlx",
+    "medium": "mlx-community/whisper-medium-mlx",
+    "large-v1": "mlx-community/whisper-large-v1-mlx",
+    "large-v2": "mlx-community/whisper-large-v2-mlx",
+    "large-v3": "mlx-community/whisper-large-v3-mlx",
+    "large-v3-turbo": "mlx-community/whisper-large-v3-turbo",
+    "large": "mlx-community/whisper-large-mlx",
+}

whisperlivekit/model_paths.py

@@ -0,0 +1,215 @@
+import json
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import List, Optional, Tuple, Union
+
+
+@dataclass
+class ModelInfo:
+    """Information about detected model format and files in a directory."""    
+    path: Optional[Path] = None    
+    pytorch_files: List[Path] = field(default_factory=list)
+    compatible_whisper_mlx: bool = False
+    compatible_faster_whisper: bool = False
+    
+    @property
+    def has_pytorch(self) -> bool:
+        return len(self.pytorch_files) > 0
+    
+    @property
+    def is_sharded(self) -> bool:
+        return len(self.pytorch_files) > 1
+    
+    @property
+    def primary_pytorch_file(self) -> Optional[Path]:
+        """Return the primary PyTorch file (or first shard for sharded models)."""
+        if not self.pytorch_files:
+            return None
+        return self.pytorch_files[0]
+
+
+#regex pattern for sharded model files such as: model-00001-of-00002.safetensors or pytorch_model-00001-of-00002.bin
+SHARDED_PATTERN = re.compile(r"^(.+)-(\d{5})-of-(\d{5})\.(safetensors|bin)$")
+
+FASTER_WHISPER_MARKERS = {"model.bin", "encoder.bin", "decoder.bin"}
+MLX_WHISPER_MARKERS = {"weights.npz", "weights.safetensors"}
+CT2_INDICATOR_FILES = {"vocabulary.json", "vocabulary.txt", "shared_vocabulary.json"}
+
+
+def _is_ct2_model_bin(directory: Path, filename: str) -> bool:
+    """
+    Determine if model.bin/encoder.bin/decoder.bin is a CTranslate2 model.
+    
+    CTranslate2 models have specific companion files that distinguish them
+    from PyTorch .bin files.
+    """
+    n_indicators = 0
+    for indicator in CT2_INDICATOR_FILES: #test 1
+        if (directory / indicator).exists(): 
+            n_indicators += 1
+        
+    if n_indicators == 0:
+        return False
+
+    config_path = directory / "config.json" #test 2
+    if config_path.exists():
+        try:
+            with open(config_path, "r", encoding="utf-8") as f:
+                config = json.load(f)
+            if config.get("model_type") == "whisper": #test 2
+                return False
+        except (json.JSONDecodeError, IOError):
+            pass
+    
+    return True
+
+
+def _collect_pytorch_files(directory: Path) -> List[Path]:
+    """
+    Collect all PyTorch checkpoint files from a directory.
+    
+    Handles:
+    - Single files: model.safetensors, pytorch_model.bin, *.pt
+    - Sharded files: model-00001-of-00002.safetensors, pytorch_model-00001-of-00002.bin
+    - Index-based sharded models (reads index file to find shards)
+    
+    Returns files sorted appropriately (shards in order, or single file).
+    """
+    for index_name in ["model.safetensors.index.json", "pytorch_model.bin.index.json"]:
+        index_path = directory / index_name
+        if index_path.exists():
+            try:
+                with open(index_path, "r", encoding="utf-8") as f:
+                    index_data = json.load(f)
+                weight_map = index_data.get("weight_map", {})
+                if weight_map:
+                    shard_names = sorted(set(weight_map.values()))
+                    shards = [directory / name for name in shard_names if (directory / name).exists()]
+                    if shards:
+                        return shards
+            except (json.JSONDecodeError, IOError):
+                pass
+    
+    sharded_groups = {}
+    single_files = {}
+    
+    for file in directory.iterdir():
+        if not file.is_file():
+            continue
+        
+        filename = file.name
+        suffix = file.suffix.lower()
+        
+        if filename.startswith("adapter_"):
+            continue
+        
+        match = SHARDED_PATTERN.match(filename)
+        if match:
+            base_name, shard_idx, total_shards, ext = match.groups()
+            key = (base_name, ext, int(total_shards))
+            if key not in sharded_groups:
+                sharded_groups[key] = []
+            sharded_groups[key].append((int(shard_idx), file))
+            continue
+        
+        if filename == "model.safetensors":
+            single_files[0] = file  # Highest priority
+        elif filename == "pytorch_model.bin":
+            single_files[1] = file
+        elif suffix == ".pt":
+            single_files[2] = file
+        elif suffix == ".safetensors" and not filename.startswith("adapter"):
+            single_files[3] = file
+    
+    for (base_name, ext, total_shards), shards in sharded_groups.items():
+        if len(shards) == total_shards:
+            return [path for _, path in sorted(shards)]
+    
+    for priority in sorted(single_files.keys()):
+        return [single_files[priority]]
+    
+    return []
+
+
+def detect_model_format(model_path: Union[str, Path]) -> ModelInfo:
+    """
+    Detect the model format in a given path.
+    
+    This function analyzes a file or directory to determine:
+    - What PyTorch checkpoint files are available (including sharded models)
+    - Whether the directory contains MLX Whisper weights
+    - Whether the directory contains Faster-Whisper (CTranslate2) weights
+    
+    Args:
+        model_path: Path to a model file or directory
+        
+    Returns:
+        ModelInfo with detected format information
+    """
+    path = Path(model_path)
+    info = ModelInfo(path=path)
+    
+    if path.is_file():
+        suffix = path.suffix.lower()
+        if suffix in {".pt", ".safetensors", ".bin"}:
+            info.pytorch_files = [path]
+        return info
+    
+    if not path.is_dir():
+        return info
+    
+    for file in path.iterdir():
+        if not file.is_file():
+            continue
+        
+        filename = file.name.lower()
+        
+        if filename in MLX_WHISPER_MARKERS:
+            info.compatible_whisper_mlx = True
+        
+        if filename in FASTER_WHISPER_MARKERS:
+            if _is_ct2_model_bin(path, filename):
+                info.compatible_faster_whisper = True
+    
+    info.pytorch_files = _collect_pytorch_files(path)
+    
+    return info
+
+
+def model_path_and_type(model_path: Union[str, Path]) -> Tuple[Optional[Path], bool, bool]:
+    """
+    Inspect the provided path and determine which model formats are available.
+    
+    This is a compatibility wrapper around detect_model_format().
+    
+    Returns:
+        pytorch_path: Path to a PyTorch checkpoint (first shard for sharded models, or None).
+        compatible_whisper_mlx: True if MLX weights exist in this folder.
+        compatible_faster_whisper: True if Faster-Whisper (CTranslate2) weights exist.
+    """
+    info = detect_model_format(model_path)
+    return info.primary_pytorch_file, info.compatible_whisper_mlx, info.compatible_faster_whisper
+
+
+def resolve_model_path(model_path: Union[str, Path]) -> Path:
+    """
+    Return a local path for the provided model reference.
+
+    If the path does not exist locally, it is treated as a Hugging Face repo id
+    and downloaded via snapshot_download.
+    """
+    path = Path(model_path).expanduser()
+    if path.exists():
+        return path
+
+    try:
+        from huggingface_hub import snapshot_download
+    except ImportError as exc:
+        raise FileNotFoundError(
+            f"Model path '{model_path}' does not exist locally and huggingface_hub "
+            "is not installed to download it."
+        ) from exc
+
+    downloaded_path = Path(snapshot_download(repo_id=str(model_path)))
+    return downloaded_path

whisperlivekit/parse_args.py

@@ -1,6 +1,7 @@
 
 from argparse import ArgumentParser
 
+
 def parse_args():
     parser = ArgumentParser(description="Whisper FastAPI Online Server")
     parser.add_argument(
@@ -20,7 +21,7 @@ def parse_args():
         help="""
         The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast.
         If not set, uses https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav.
-        If False, no warmup is performed.
+        If empty, no warmup is performed.
         """,
     )
 
@@ -72,17 +73,24 @@ def parse_args():
         help="Disable transcription to only see live diarization results.",
     )
     
+    parser.add_argument(
+        "--disable-punctuation-split",
+        action="store_true",
+        help="Disable the split parameter.",
+    )
+    
     parser.add_argument(
         "--min-chunk-size",
         type=float,
-        default=0.5,
+        default=0.1,
         help="Minimum audio chunk size in seconds. It waits up to this time to do processing. If the processing takes shorter time, it waits, otherwise it processes the whole segment that was received by this time.",
     )
     
     parser.add_argument(
         "--model",
         type=str,
-        default="small",
+        default="base",
+        dest='model_size',
         help="Name size of the Whisper model to use (default: tiny). Suggested values: tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large-v3,large,large-v3-turbo. The model is automatically downloaded from the model hub if not present in model cache dir.",
     )
     
@@ -98,26 +106,49 @@ def parse_args():
         default=None,
         help="Dir where Whisper model.bin and other files are saved. This option overrides --model and --model_cache_dir parameter.",
     )
+    parser.add_argument(
+        "--lora-path",
+        type=str,
+        default=None,
+        dest="lora_path",
+        help="Path or Hugging Face repo ID for LoRA adapter weights (e.g., QuentinFuxa/whisper-base-french-lora). Only works with native Whisper backend.",
+    )
     parser.add_argument(
         "--lan",
         "--language",
         type=str,
         default="auto",
+        dest='lan',
         help="Source language code, e.g. en,de,cs, or 'auto' for language detection.",
     )
     parser.add_argument(
-        "--task",
+        "--direct-english-translation",
+        action="store_true",
+        default=False,
+        help="Use Whisper to directly translate to english.",
+    )
+    
+    parser.add_argument(
+        "--target-language",
         type=str,
-        default="transcribe",
-        choices=["transcribe", "translate"],
-        help="Transcribe or translate.",
+        default="",
+        dest="target_language",
+        help="Target language for translation. Not functional yet.",
+    )    
+
+    parser.add_argument(
+        "--backend-policy",
+        type=str,
+        default="simulstreaming",
+        choices=["1", "2", "simulstreaming", "localagreement"],
+        help="Select the streaming policy: 1 or 'simulstreaming' for AlignAtt, 2 or 'localagreement' for LocalAgreement.",
     )
     parser.add_argument(
         "--backend",
         type=str,
-        default="simulstreaming",
-        choices=["faster-whisper", "whisper_timestamped", "mlx-whisper", "openai-api", "simulstreaming"],
-        help="Load only this backend for Whisper processing.",
+        default="auto",
+        choices=["auto", "mlx-whisper", "faster-whisper", "whisper", "openai-api", "voxtral", "voxtral-mlx"],
+        help="Select the ASR backend implementation (auto: prefer MLX on macOS, otherwise Faster-Whisper, else Whisper). Use 'voxtral' for HF Transformers Voxtral (CUDA/CPU/MPS). Use 'voxtral-mlx' for native MLX Voxtral on Apple Silicon.",
     )
     parser.add_argument(
         "--no-vac",
@@ -158,7 +189,13 @@ def parse_args():
     )
     parser.add_argument("--ssl-certfile", type=str, help="Path to the SSL certificate file.", default=None)
     parser.add_argument("--ssl-keyfile", type=str, help="Path to the SSL private key file.", default=None)
-
+    parser.add_argument("--forwarded-allow-ips", type=str, help="Allowed ips for reverse proxying.", default=None)
+    parser.add_argument(
+        "--pcm-input",
+        action="store_true",
+        default=False,
+        help="If set, raw PCM (s16le) data is expected as input and FFmpeg will be bypassed. Frontend will use AudioWorklet instead of MediaRecorder."
+    )
     # SimulStreaming-specific arguments
     simulstreaming_group = parser.add_argument_group('SimulStreaming arguments (only used with --backend simulstreaming)')
 
@@ -169,6 +206,13 @@ def parse_args():
         dest="disable_fast_encoder",
         help="Disable Faster Whisper or MLX Whisper backends for encoding (if installed). Slower but helpful when GPU memory is limited",
     )
+
+    simulstreaming_group.add_argument(
+        "--custom-alignment-heads",
+        type=str,
+        default=None,
+        help="Use your own alignment heads, useful when `--model-dir` is used",
+    )
     
     simulstreaming_group.add_argument(
         "--frame-threshold",
@@ -260,18 +304,26 @@ def parse_args():
     )
     
     simulstreaming_group.add_argument(
-        "--preloaded_model_count",
-        type=int,
-        default=1,
-        dest="preloaded_model_count",
-        help="Optional. Number of models to preload in memory to speed up loading (set up to the expected number of concurrent instances).",
+        "--nllb-backend",
+        type=str,
+        default="transformers",
+        help="transformers or ctranslate2",
+    )
+    
+    simulstreaming_group.add_argument(
+        "--nllb-size",
+        type=str,
+        default="600M",
+        help="600M or 1.3B",
     )
 
     args = parser.parse_args()
-    
     args.transcription = not args.no_transcription
-    args.vad = not args.no_vad    
+    args.vad = not args.no_vad
+    args.vac = not args.no_vac
     delattr(args, 'no_transcription')
     delattr(args, 'no_vad')
-    
-    return args
+    delattr(args, 'no_vac')
+
+    from whisperlivekit.config import WhisperLiveKitConfig
+    return WhisperLiveKitConfig.from_namespace(args)

whisperlivekit/remove_silences.py

@@ -1,110 +0,0 @@
-from whisperlivekit.timed_objects import ASRToken
-import re
-
-MIN_SILENCE_DURATION = 4 #in seconds
-END_SILENCE_DURATION = 8 #in seconds. you should keep it important to not have false positive when the model lag is important
-END_SILENCE_DURATION_VAC = 3 #VAC is good at detecting silences, but we want to skip the smallest silences
-
-def blank_to_silence(tokens):
-    full_string = ''.join([t.text for t in tokens])
-    patterns = [re.compile(r'(?:\s*\[BLANK_AUDIO\]\s*)+'), re.compile(r'(?:\s*\[typing\]\s*)+')]             
-    matches = []
-    for pattern in patterns:
-        for m in pattern.finditer(full_string):
-            matches.append({
-                'start': m.start(),
-                'end': m.end()
-            })
-    if matches:
-        # cleaned = pattern.sub(' ', full_string).strip()
-        # print("Cleaned:", cleaned)
-        cumulated_len = 0
-        silence_token = None
-        cleaned_tokens = []
-        for token in tokens:
-            if matches:
-                start = cumulated_len
-                end = cumulated_len + len(token.text)
-                cumulated_len = end
-                if start >= matches[0]['start'] and end <= matches[0]['end']:
-                    if silence_token: #previous token was already silence
-                        silence_token.start = min(silence_token.start, token.start)
-                        silence_token.end = max(silence_token.end, token.end)
-                    else: #new silence
-                        silence_token = ASRToken(
-                            start=token.start,
-                            end=token.end,
-                            speaker=-2,
-                            probability=0.95
-                        )
-                else:
-                    if silence_token: #there was silence but no more
-                        if silence_token.end - silence_token.start >= MIN_SILENCE_DURATION:
-                            cleaned_tokens.append(
-                                silence_token
-                            )
-                        silence_token = None
-                        matches.pop(0)
-                    cleaned_tokens.append(token)
-        # print(cleaned_tokens)    
-        return cleaned_tokens
-    return tokens
-
-def no_token_to_silence(tokens):
-    new_tokens = []
-    silence_token = None
-    for token in tokens:
-        if token.speaker == -2:
-            if new_tokens and new_tokens[-1].speaker == -2: #if token is silence and previous one too
-                new_tokens[-1].end = token.end
-            else:
-                new_tokens.append(token)
-            
-        last_end = new_tokens[-1].end if new_tokens else 0.0
-        if token.start - last_end >= MIN_SILENCE_DURATION: #if token is not silence but important gap
-            if new_tokens and new_tokens[-1].speaker == -2:
-                new_tokens[-1].end = token.start
-            else:
-                silence_token = ASRToken(
-                    start=last_end,
-                    end=token.start,
-                    speaker=-2,
-                    probability=0.95
-                    )
-                new_tokens.append(silence_token)
-        
-        if token.speaker != -2:
-            new_tokens.append(token)
-    return new_tokens
-            
-def ends_with_silence(tokens, buffer_transcription, buffer_diarization, current_time, vac_detected_silence):
-    if not tokens:
-        return [], buffer_transcription, buffer_diarization
-    last_token = tokens[-1]
-    if tokens and current_time and (
-        current_time - last_token.end >= END_SILENCE_DURATION 
-        or 
-        (current_time - last_token.end >= 3 and vac_detected_silence)
-        ):
-        if last_token.speaker == -2:
-            last_token.end = current_time
-        else:
-            tokens.append(
-                ASRToken(
-                    start=tokens[-1].end,
-                    end=current_time,
-                    speaker=-2,
-                    probability=0.95
-                )
-            )
-        buffer_transcription = "" # for whisperstreaming backend, we should probably validate the buffer has because of the silence
-        buffer_diarization  = ""
-    return tokens, buffer_transcription, buffer_diarization
-    
-
-def handle_silences(tokens, buffer_transcription, buffer_diarization, current_time, vac_detected_silence):
-    tokens = blank_to_silence(tokens) #useful for simulstreaming backend which tends to generate [BLANK_AUDIO] text
-    tokens = no_token_to_silence(tokens)
-    tokens, buffer_transcription, buffer_diarization = ends_with_silence(tokens, buffer_transcription, buffer_diarization, current_time, vac_detected_silence)
-    return tokens, buffer_transcription, buffer_diarization
-     

whisperlivekit/results_formater.py

@@ -1,138 +0,0 @@
-
-import logging
-from datetime import timedelta
-from whisperlivekit.remove_silences import handle_silences
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
-PUNCTUATION_MARKS = {'.', '!', '?'}
-CHECK_AROUND = 4
-
-def format_time(seconds: float) -> str:
-    """Format seconds as HH:MM:SS."""
-    return str(timedelta(seconds=int(seconds)))
-
-
-def is_punctuation(token):
-    if token.text.strip() in PUNCTUATION_MARKS:
-        return True
-    return False
-
-def next_punctuation_change(i, tokens):
-    for ind in range(i+1, min(len(tokens), i+CHECK_AROUND+1)):
-        if is_punctuation(tokens[ind]):
-            return ind        
-    return None
-
-def next_speaker_change(i, tokens, speaker):
-    for ind in range(i-1, max(0, i-CHECK_AROUND)-1, -1):
-        token = tokens[ind]
-        if is_punctuation(token):
-            break
-        if token.speaker != speaker:
-            return ind, token.speaker
-    return None, speaker
-    
-
-def new_line(
-    token,
-    speaker,
-    last_end_diarized,
-    debug_info = ""
-):
-    return {
-            "speaker": int(speaker),
-            "text": token.text + debug_info,
-            "beg": format_time(token.start),
-            "end": format_time(token.end),
-            "diff": round(token.end - last_end_diarized, 2)
-    }
-
-
-def append_token_to_last_line(lines, sep, token, debug_info, last_end_diarized):
-    if token.text:
-        lines[-1]["text"] += sep + token.text + debug_info
-        lines[-1]["end"] = format_time(token.end)
-        lines[-1]["diff"] = round(token.end - last_end_diarized, 2)
-            
-
-def format_output(state, silence, current_time, diarization, debug):
-    tokens = state["tokens"]
-    buffer_transcription = state["buffer_transcription"]
-    buffer_diarization = state["buffer_diarization"]
-    end_attributed_speaker = state["end_attributed_speaker"]
-    sep = state["sep"]
-    
-    previous_speaker = -1
-    lines = []
-    last_end_diarized = 0
-    undiarized_text = []
-    tokens, buffer_transcription, buffer_diarization = handle_silences(tokens, buffer_transcription, buffer_diarization, current_time, silence)
-    last_punctuation = None
-    for i, token in enumerate(tokens):
-        speaker = token.speaker
-        
-        if not diarization and speaker == -1: #Speaker -1 means no attributed by diarization. In the frontend, it should appear under 'Speaker 1'
-            speaker = 1
-        if diarization and not tokens[-1].speaker == -2:
-            if (speaker in [-1, 0]) and token.end >= end_attributed_speaker:
-                undiarized_text.append(token.text)
-                continue
-            elif (speaker in [-1, 0]) and token.end < end_attributed_speaker:
-                speaker = previous_speaker
-            if speaker not in [-1, 0]:
-                last_end_diarized = max(token.end, last_end_diarized)
-
-        debug_info = ""
-        if debug:
-            debug_info = f"[{format_time(token.start)} : {format_time(token.end)}]"
-            
-        if not lines:
-            lines.append(new_line(token, speaker, last_end_diarized, debug_info = ""))
-            continue
-        else:
-            previous_speaker = lines[-1]['speaker']
-        
-        if is_punctuation(token):
-            last_punctuation = i
-            
-        
-        if last_punctuation == i-1:
-            if speaker != previous_speaker:
-                # perfect, diarization perfectly aligned
-                lines.append(new_line(token, speaker, last_end_diarized, debug_info = ""))
-                last_punctuation, next_punctuation = None, None
-                continue
-            
-            speaker_change_pos, new_speaker = next_speaker_change(i, tokens, speaker)
-            if speaker_change_pos:
-                # Corrects delay:
-                # That was the idea. Okay haha |SPLIT SPEAKER| that's a good one 
-                # should become:
-                # That was the idea. |SPLIT SPEAKER| Okay haha that's a good one 
-                lines.append(new_line(token, new_speaker, last_end_diarized, debug_info = ""))
-            else:
-                # No speaker change to come
-                append_token_to_last_line(lines, sep, token, debug_info, last_end_diarized)
-            continue
-        
-
-        if speaker != previous_speaker:
-            if speaker == -2 or previous_speaker == -2: #silences can happen anytime
-                lines.append(new_line(token, speaker, last_end_diarized, debug_info = ""))
-                continue
-            elif next_punctuation_change(i, tokens):
-                # Corrects advance:
-                # Are you |SPLIT SPEAKER| okay? yeah, sure. Absolutely 
-                # should become:
-                # Are you okay? |SPLIT SPEAKER| yeah, sure. Absolutely 
-                append_token_to_last_line(lines, sep, token, debug_info, last_end_diarized)
-                continue
-            else: #we create a new speaker, but that's no ideal. We are not sure about the split. We prefer to append to previous line
-                # lines.append(new_line(token, speaker, last_end_diarized, debug_info = ""))
-                pass
-            
-        append_token_to_last_line(lines, sep, token, debug_info, last_end_diarized)
-    return lines, undiarized_text, buffer_transcription, '' 
-

whisperlivekit/silero_vad_iterator.py

@@ -1,27 +1,211 @@
+import warnings
+from pathlib import Path
+
+import numpy as np
 import torch
 
-# This is copied from silero-vad's vad_utils.py:
-# https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/utils_vad.py#L340
-# (except changed defaults)
+"""
+Code is adapted from silero-vad v6: https://github.com/snakers4/silero-vad
+"""
 
-# Their licence is MIT, same as ours: https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/LICENSE
+def is_onnx_available() -> bool:
+    """Check if onnxruntime is installed."""
+    try:
+        import onnxruntime
+        return True
+    except ImportError:
+        return False
+
+
+def init_jit_model(model_path: str, device=torch.device('cpu')):
+    """Load a JIT model from file."""
+    model = torch.jit.load(model_path, map_location=device)
+    model.eval()
+    return model
+
+
+class OnnxSession():
+    """
+    Shared ONNX session for Silero VAD model (stateless).
+    """
+
+    def __init__(self, path, force_onnx_cpu=False):
+        import onnxruntime
+
+        opts = onnxruntime.SessionOptions()
+        opts.inter_op_num_threads = 1
+        opts.intra_op_num_threads = 1
+
+        if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
+            self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts)
+        else:
+            self.session = onnxruntime.InferenceSession(path, sess_options=opts)
+
+        self.path = path
+        if '16k' in path:
+            warnings.warn('This model support only 16000 sampling rate!')
+            self.sample_rates = [16000]
+        else:
+            self.sample_rates = [8000, 16000]
+
+
+class OnnxWrapper():
+    """
+    ONNX Runtime wrapper for Silero VAD model with per-instance state.
+    """
+
+    def __init__(self, session: OnnxSession, force_onnx_cpu=False):
+        self._shared_session = session
+        self.sample_rates = session.sample_rates
+        self.reset_states()
+
+    @property
+    def session(self):
+        return self._shared_session.session
+
+    def _validate_input(self, x, sr: int):
+        if x.dim() == 1:
+            x = x.unsqueeze(0)
+        if x.dim() > 2:
+            raise ValueError(f"Too many dimensions for input audio chunk {x.dim()}")
+
+        if sr != 16000 and (sr % 16000 == 0):
+            step = sr // 16000
+            x = x[:,::step]
+            sr = 16000
+
+        if sr not in self.sample_rates:
+            raise ValueError(f"Supported sampling rates: {self.sample_rates} (or multiply of 16000)")
+        if sr / x.shape[1] > 31.25:
+            raise ValueError("Input audio chunk is too short")
+
+        return x, sr
+
+    def reset_states(self, batch_size=1):
+        self._state = torch.zeros((2, batch_size, 128)).float()
+        self._context = torch.zeros(0)
+        self._last_sr = 0
+        self._last_batch_size = 0
+
+    def __call__(self, x, sr: int):
+
+        x, sr = self._validate_input(x, sr)
+        num_samples = 512 if sr == 16000 else 256
+
+        if x.shape[-1] != num_samples:
+            raise ValueError(f"Provided number of samples is {x.shape[-1]} (Supported values: 256 for 8000 sample rate, 512 for 16000)")
+
+        batch_size = x.shape[0]
+        context_size = 64 if sr == 16000 else 32
+
+        if not self._last_batch_size:
+            self.reset_states(batch_size)
+        if (self._last_sr) and (self._last_sr != sr):
+            self.reset_states(batch_size)
+        if (self._last_batch_size) and (self._last_batch_size != batch_size):
+            self.reset_states(batch_size)
+
+        if not len(self._context):
+            self._context = torch.zeros(batch_size, context_size)
+
+        x = torch.cat([self._context, x], dim=1)
+        if sr in [8000, 16000]:
+            ort_inputs = {'input': x.numpy(), 'state': self._state.numpy(), 'sr': np.array(sr, dtype='int64')}
+            ort_outs = self.session.run(None, ort_inputs)
+            out, state = ort_outs
+            self._state = torch.from_numpy(state)
+        else:
+            raise ValueError(f"Unsupported sampling rate {sr}. Supported: {self.sample_rates} (with sample sizes 256 for 8000, 512 for 16000)")
+
+        self._context = x[..., -context_size:]
+        self._last_sr = sr
+        self._last_batch_size = batch_size
+
+        out = torch.from_numpy(out)
+        return out
+
+
+def _get_onnx_model_path(model_path: str = None, opset_version: int = 16) -> Path:
+    """Get the path to the ONNX model file."""
+    available_ops = [15, 16]
+    if opset_version not in available_ops:
+        raise ValueError(f'Unsupported ONNX opset_version: {opset_version}. Available: {available_ops}')
+    
+    if model_path is None:
+        current_dir = Path(__file__).parent
+        data_dir = current_dir / 'silero_vad_models'
+        
+        if opset_version == 16:
+            model_name = 'silero_vad.onnx'
+        else:
+            model_name = f'silero_vad_16k_op{opset_version}.onnx'
+        
+        model_path = data_dir / model_name
+        
+        if not model_path.exists():
+            raise FileNotFoundError(
+                f"Model file not found: {model_path}\n"
+                f"Please ensure the whisperlivekit/silero_vad_models/ directory contains the model files."
+            )
+    else:
+        model_path = Path(model_path)
+    
+    return model_path
+
+
+def load_onnx_session(model_path: str = None, opset_version: int = 16, force_onnx_cpu: bool = True) -> OnnxSession:
+    """
+    Load a shared ONNX session for Silero VAD.
+    """
+    path = _get_onnx_model_path(model_path, opset_version)
+    return OnnxSession(str(path), force_onnx_cpu=force_onnx_cpu)
+
+
+def load_jit_vad(model_path: str = None):
+    """
+    Load Silero VAD model in JIT format.
+    """
+    if model_path is None:
+        current_dir = Path(__file__).parent
+        data_dir = current_dir / 'silero_vad_models'
+        model_name = 'silero_vad.jit'
+        
+        model_path = data_dir / model_name
+        
+        if not model_path.exists():
+            raise FileNotFoundError(
+                f"Model file not found: {model_path}\n"
+                f"Please ensure the whisperlivekit/silero_vad_models/ directory contains the model files."
+            )
+    else:
+        model_path = Path(model_path)
+
+    model = init_jit_model(str(model_path))
+ 
+    return model
 
 
 class VADIterator:
-    def __init__(
-        self,
-        model,
-        threshold: float = 0.5,
-        sampling_rate: int = 16000,
-        min_silence_duration_ms: int = 500,  # makes sense on one recording that I checked
-        speech_pad_ms: int = 100,  # same
-    ):
+    """
+    Voice Activity Detection iterator for streaming audio.
+    
+    This is the Silero VAD v6 implementation.
+    """
+    
+    def __init__(self,
+                 model,
+                 threshold: float = 0.5,
+                 sampling_rate: int = 16000,
+                 min_silence_duration_ms: int = 100,
+                 speech_pad_ms: int = 30
+                 ):
+
         """
         Class for stream imitation
 
         Parameters
         ----------
-        model: preloaded .jit silero VAD model
+        model: preloaded .jit/.onnx silero VAD model
 
         threshold: float (default - 0.5)
             Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH.
@@ -42,9 +226,7 @@ class VADIterator:
         self.sampling_rate = sampling_rate
 
         if sampling_rate not in [8000, 16000]:
-            raise ValueError(
-                "VADIterator does not support sampling rates other than [8000, 16000]"
-            )
+            raise ValueError('VADIterator does not support sampling rates other than [8000, 16000]')
 
         self.min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
         self.speech_pad_samples = sampling_rate * speech_pad_ms / 1000
@@ -57,20 +239,24 @@ class VADIterator:
         self.temp_end = 0
         self.current_sample = 0
 
-    def __call__(self, x, return_seconds=False):
+    @torch.no_grad()
+    def __call__(self, x, return_seconds=False, time_resolution: int = 1):
         """
         x: torch.Tensor
             audio chunk (see examples in repo)
 
         return_seconds: bool (default - False)
             whether return timestamps in seconds (default - samples)
+
+        time_resolution: int (default - 1)
+            time resolution of speech coordinates when requested as seconds
         """
 
         if not torch.is_tensor(x):
             try:
                 x = torch.Tensor(x)
-            except:
-                raise TypeError("Audio cannot be casted to tensor. Cast it manually")
+            except (ValueError, TypeError, RuntimeError) as exc:
+                raise TypeError("Audio cannot be cast to tensor. Cast it manually") from exc
 
         window_size_samples = len(x[0]) if x.dim() == 2 else len(x)
         self.current_sample += window_size_samples
@@ -82,14 +268,8 @@ class VADIterator:
 
         if (speech_prob >= self.threshold) and not self.triggered:
             self.triggered = True
-            speech_start = self.current_sample - self.speech_pad_samples
-            return {
-                "start": (
-                    int(speech_start)
-                    if not return_seconds
-                    else round(speech_start / self.sampling_rate, 1)
-                )
-            }
+            speech_start = max(0, self.current_sample - self.speech_pad_samples - window_size_samples)
+            return {'start': int(speech_start) if not return_seconds else round(speech_start / self.sampling_rate, time_resolution)}
 
         if (speech_prob < self.threshold - 0.15) and self.triggered:
             if not self.temp_end:
@@ -97,30 +277,17 @@ class VADIterator:
             if self.current_sample - self.temp_end < self.min_silence_samples:
                 return None
             else:
-                speech_end = self.temp_end + self.speech_pad_samples
+                speech_end = self.temp_end + self.speech_pad_samples - window_size_samples
                 self.temp_end = 0
                 self.triggered = False
-                return {
-                    "end": (
-                        int(speech_end)
-                        if not return_seconds
-                        else round(speech_end / self.sampling_rate, 1)
-                    )
-                }
+                return {'end': int(speech_end) if not return_seconds else round(speech_end / self.sampling_rate, time_resolution)}
 
         return None
 
 
-#######################
-# because Silero now requires exactly 512-sized audio chunks
-
-import numpy as np
-
-
 class FixedVADIterator(VADIterator):
-    """It fixes VADIterator by allowing to process any audio length, not only exactly 512 frames at once.
-    If audio to be processed at once is long and multiple voiced segments detected,
-    then __call__ returns the start of the first segment, and end (or middle, which means no end) of the last segment.
+    """
+    Fixed VAD Iterator that handles variable-length audio chunks, not only exactly 512 frames at once.
     """
 
     def reset_states(self):
@@ -137,27 +304,23 @@ class FixedVADIterator(VADIterator):
                 ret = r
             elif r is not None:
                 if "end" in r:
-                    ret["end"] = r["end"]  # the latter end
-                if "start" in r and "end" in ret:  # there is an earlier start.
-                    # Remove end, merging this segment with the previous one.
-                    del ret["end"]
+                    ret["end"] = r["end"]
+                if "start" in r:
+                    ret["start"] = r["start"]
+                    if "end" in ret:
+                        del ret["end"]
         return ret if ret != {} else None
 
 
 if __name__ == "__main__":
-    # test/demonstrate the need for FixedVADIterator:
+    # vad = FixedVADIterator(load_jit_vad())
+    vad = FixedVADIterator(OnnxWrapper(session=load_onnx_session()))
 
-    import torch
-
-    model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad")
-    vac = FixedVADIterator(model)
-    #   vac = VADIterator(model)  # the second case crashes with this
-
-    # this works: for both
-    audio_buffer = np.array([0] * (512), dtype=np.float32)
-    vac(audio_buffer)
-
-    # this crashes on the non FixedVADIterator with
-    # ops.prim.RaiseException("Input audio chunk is too short", "builtins.ValueError")
-    audio_buffer = np.array([0] * (512 - 1), dtype=np.float32)
-    vac(audio_buffer)
+    audio_buffer = np.array([0] * 512, dtype=np.float32)
+    result = vad(audio_buffer)
+    print(f"   512 samples: {result}")
+    
+    # test with 511 samples
+    audio_buffer = np.array([0] * 511, dtype=np.float32)
+    result = vad(audio_buffer)
+    print(f"   511 samples: {result}")

whisperlivekit/simul_whisper/align_att_base.py

@@ -0,0 +1,483 @@
+"""Abstract base class for AlignAtt streaming decoders (PyTorch & MLX)."""
+import logging
+from abc import ABC, abstractmethod
+from typing import Any, List, Optional, Tuple
+
+from whisperlivekit.timed_objects import ASRToken
+from whisperlivekit.whisper import DecodingOptions, tokenizer
+
+from .config import AlignAttConfig
+
+DEC_PAD = 50257
+logger = logging.getLogger(__name__)
+
+
+class AlignAttBase(ABC):
+    """
+    Abstract base class for AlignAtt streaming decoders.
+
+    Provides shared logic for both PyTorch and MLX implementations:
+    - Properties (speaker, global_time_offset)
+    - Pure-Python methods (warmup, trim_context, refresh_segment, etc.)
+    - Template infer() with abstract hooks for tensor-specific operations
+    - Post-decode logic (token splitting, timestamped word building)
+
+    Subclasses must implement ~20 abstract methods for tensor-specific ops.
+    """
+
+    # === Properties ===
+
+    @property
+    def speaker(self):
+        return self.state.speaker
+
+    @speaker.setter
+    def speaker(self, value):
+        self.state.speaker = value
+
+    @property
+    def global_time_offset(self):
+        return self.state.global_time_offset
+
+    @global_time_offset.setter
+    def global_time_offset(self, value):
+        self.state.global_time_offset = value
+
+    # === Constructor helpers ===
+
+    def _base_init(self, cfg: AlignAttConfig, model):
+        """Common initialization — call from subclass __init__."""
+        self.model = model
+        self.cfg = cfg
+        self.decode_options = DecodingOptions(
+            language=cfg.language,
+            without_timestamps=True,
+            task=cfg.task,
+        )
+        self.tokenizer_is_multilingual = cfg.tokenizer_is_multilingual
+        self.max_text_len = model.dims.n_text_ctx
+        self.num_decoder_layers = len(model.decoder.blocks)
+        if cfg.max_context_tokens is None:
+            self.max_context_tokens = self.max_text_len
+        else:
+            self.max_context_tokens = cfg.max_context_tokens
+
+    def _init_state_common(self, cfg: AlignAttConfig):
+        """Common state initialization — call from subclass _init_state."""
+        self.create_tokenizer(cfg.language if cfg.language != "auto" else None)
+        self.state.tokenizer = self.tokenizer
+        self.state.detected_language = cfg.language if cfg.language != "auto" else None
+        self.state.global_time_offset = 0.0
+        self.state.last_attend_frame = -cfg.rewind_threshold
+        self.state.speaker = -1
+
+    # === Shared concrete methods ===
+
+    def warmup(self, audio):
+        try:
+            self.insert_audio(audio)
+            self.infer(is_last=True)
+            self.refresh_segment(complete=True)
+            logger.info("Model warmed up successfully")
+        except Exception as e:
+            logger.exception(f"Model warmup failed: {e}")
+
+    def create_tokenizer(self, language=None):
+        self.tokenizer = tokenizer.get_tokenizer(
+            multilingual=self.tokenizer_is_multilingual,
+            language=language,
+            num_languages=self.model.num_languages,
+            task=self.decode_options.task,
+        )
+        self.state.tokenizer = self.tokenizer
+
+    def trim_context(self):
+        logger.info("Trimming context")
+        c = len(self.state.context.as_token_ids()) - len(self.state.context.prefix_token_ids)
+        logger.info(f"Context text: {self.state.context.as_text()}")
+        l = sum(t.shape[1] for t in self.state.tokens) + c
+        after = 0 if self.cfg.static_init_prompt is None else len(self.cfg.static_init_prompt)
+        while c > self.max_context_tokens or l > self.max_text_len - 20:
+            t = self.state.context.trim_words(after=after)
+            l -= t
+            c -= t
+            logger.debug(f"len {l}, c {c}, max_context_tokens {self.max_context_tokens}")
+            if t == 0:
+                break
+        logger.info(f"Context after trim: {self.state.context.text} (len: {l})")
+
+    def refresh_segment(self, complete=False):
+        logger.debug("Refreshing segment:")
+        self.init_tokens()
+        self.state.last_attend_frame = -self.cfg.rewind_threshold
+        self.state.cumulative_time_offset = 0.0
+        self.init_context()
+        logger.debug(f"Context: {self.state.context}")
+        if not complete and len(self.state.segments) > 2:
+            self.state.segments = self.state.segments[-2:]
+        else:
+            logger.debug("removing all segments.")
+            self.state.segments = []
+        self.state.log_segments += 1
+        self.state.pending_incomplete_tokens = []
+
+    def segments_len(self):
+        return sum(s.shape[0] for s in self.state.segments) / 16000
+
+    def _apply_minseglen(self):
+        segments_len = self.segments_len()
+        if segments_len < self.cfg.audio_min_len:
+            logger.debug("waiting for next segment")
+            return False
+        return True
+
+    def _clean_cache(self):
+        self.state.clean_cache()
+
+    def debug_print_tokens(self, tokens):
+        for i in range(min(self.cfg.beam_size, tokens.shape[0])):
+            logger.debug(self.tokenizer.decode_with_timestamps(tokens[i].tolist()))
+
+    # === Language detection ===
+
+    def _detect_language_if_needed(self, encoder_feature):
+        if (
+            self.cfg.language == "auto"
+            and self.state.detected_language is None
+            and self.state.first_timestamp
+        ):
+            seconds_since_start = self.segments_len() - self.state.first_timestamp
+            if seconds_since_start >= 2.0:
+                language_tokens, language_probs = self.lang_id(encoder_feature)
+                top_lan, p = max(language_probs[0].items(), key=lambda x: x[1])
+                print(f"Detected language: {top_lan} with p={p:.4f}")
+                self.create_tokenizer(top_lan)
+                self.state.last_attend_frame = -self.cfg.rewind_threshold
+                self.state.cumulative_time_offset = 0.0
+                self.init_tokens()
+                self.init_context()
+                self.state.detected_language = top_lan
+                logger.info(f"Tokenizer language: {self.tokenizer.language}")
+
+    # === Template infer() ===
+
+    def infer(self, is_last=False):
+        """Main inference — template method calling abstract hooks for tensor ops."""
+        new_segment = True
+
+        if len(self.state.segments) == 0:
+            logger.debug("No segments, nothing to do")
+            return []
+        if not self._apply_minseglen():
+            logger.debug(f"applied minseglen {self.cfg.audio_min_len} > {self.segments_len()}.")
+            return []
+
+        input_segments = self._concat_segments()
+        encoder_feature, content_mel_len = self._encode(input_segments)
+        self._evaluate(encoder_feature)
+
+        self._detect_language_if_needed(encoder_feature)
+        self.trim_context()
+        current_tokens = self._current_tokens()
+
+        fire_detected = self.fire_at_boundary(encoder_feature[:, :content_mel_len, :])
+
+        sum_logprobs = self._init_sum_logprobs()
+        completed = False
+        token_len_before = current_tokens.shape[1]
+        l_absolute_timestamps = []
+        accumulated_cross_attns = []
+
+        audio_duration_s = self.segments_len()
+        max_tokens = max(50, int(audio_duration_s * 15 * 1.5))
+        tokens_produced = 0
+        most_attended_frame = None
+
+        while not completed and current_tokens.shape[1] < self.max_text_len:
+            tokens_produced += 1
+            if tokens_produced > max_tokens:
+                logger.warning(
+                    f"[Loop Detection] Too many tokens ({tokens_produced}) "
+                    f"for {audio_duration_s:.2f}s audio. Breaking."
+                )
+                current_tokens = current_tokens[:, :token_len_before]
+                break
+
+            tokens_for_logits = current_tokens if new_segment else current_tokens[:, -1:]
+            logits, cross_attns = self._get_logits_and_cross_attn(
+                tokens_for_logits, encoder_feature
+            )
+            self._evaluate(logits)
+
+            accumulated_cross_attns.append(cross_attns)
+            if len(accumulated_cross_attns) > 16:
+                accumulated_cross_attns = accumulated_cross_attns[-16:]
+
+            if new_segment and self._check_no_speech(logits):
+                break
+
+            logits = logits[:, -1, :]
+
+            if new_segment:
+                logits = self._suppress_blank_tokens(logits)
+            new_segment = False
+
+            logits = self._apply_token_suppression(logits)
+            current_tokens, completed = self._update_tokens(
+                current_tokens, logits, sum_logprobs
+            )
+            self._evaluate(current_tokens)
+
+            logger.debug(f"Decoding completed: {completed}")
+            self.debug_print_tokens(current_tokens)
+
+            attn = self._process_cross_attention(accumulated_cross_attns, content_mel_len)
+            frames_list, most_attended_frame = self._get_attended_frames(attn)
+
+            absolute_timestamps = [
+                (frame * 0.02 + self.state.cumulative_time_offset)
+                for frame in frames_list
+            ]
+            l_absolute_timestamps.append(absolute_timestamps[0])
+            logger.debug(f"Absolute timestamps: {absolute_timestamps}")
+
+            if completed:
+                current_tokens = current_tokens[:, :-1]
+                break
+
+            # Rewind check
+            if (
+                not is_last
+                and self.state.last_attend_frame - most_attended_frame
+                > self.cfg.rewind_threshold
+            ):
+                if current_tokens.shape[1] > 1 and self._is_special_token(current_tokens):
+                    logger.debug("omit rewinding from special tokens")
+                    self.state.last_attend_frame = most_attended_frame
+                else:
+                    logger.debug(
+                        f"[rewind detected] current: {most_attended_frame}, "
+                        f"last: {self.state.last_attend_frame}"
+                    )
+                    self.state.last_attend_frame = -self.cfg.rewind_threshold
+                    current_tokens = self._rewind_tokens()
+                    break
+            else:
+                self.state.last_attend_frame = most_attended_frame
+
+            if content_mel_len - most_attended_frame <= (
+                4 if is_last else self.cfg.frame_threshold
+            ):
+                logger.debug(
+                    f"attention reaches the end: {most_attended_frame}/{content_mel_len}"
+                )
+                current_tokens = current_tokens[:, :-1]
+                break
+
+        # Post-decode: split tokens and build timestamped words
+        tokens_to_split = self._tokens_to_list(current_tokens, token_len_before)
+        if self.state.pending_incomplete_tokens:
+            logger.debug(
+                f"[UTF-8 Fix] Prepending {len(self.state.pending_incomplete_tokens)} "
+                f"pending tokens: {self.state.pending_incomplete_tokens}"
+            )
+            tokens_to_split = self.state.pending_incomplete_tokens + tokens_to_split
+
+        new_hypothesis, split_words, split_tokens = self._split_tokens(
+            tokens_to_split, fire_detected, is_last
+        )
+
+        new_tokens_tensor = self._make_new_tokens_tensor(new_hypothesis)
+        self.state.tokens.append(new_tokens_tensor)
+        logger.info(f"Output: {self.tokenizer.decode(new_hypothesis)}")
+
+        self._clean_cache()
+
+        if len(l_absolute_timestamps) >= 2 and self.state.first_timestamp is None:
+            self.state.first_timestamp = l_absolute_timestamps[0]
+
+        timestamped_words = self._build_timestamped_words(
+            split_words, split_tokens, l_absolute_timestamps
+        )
+        self._handle_pending_tokens(split_words, split_tokens)
+
+        return timestamped_words
+
+    # === Post-decode shared helpers ===
+
+    def _split_tokens(self, tokens_list, fire_detected, is_last):
+        """Split token list into words. Returns (hypothesis, split_words, split_tokens)."""
+        if fire_detected or is_last:
+            new_hypothesis = tokens_list
+            split_words, split_tokens = self.tokenizer.split_to_word_tokens(new_hypothesis)
+        else:
+            split_words, split_tokens = self.tokenizer.split_to_word_tokens(tokens_list)
+            if len(split_words) > 1:
+                new_hypothesis = [i for sublist in split_tokens[:-1] for i in sublist]
+            else:
+                new_hypothesis = []
+        return new_hypothesis, split_words, split_tokens
+
+    def _build_timestamped_words(self, split_words, split_tokens, l_absolute_timestamps):
+        """Build list of timestamped ASRToken from split words."""
+        timestamped_words = []
+        timestamp_idx = 0
+        replacement_char = "\ufffd"
+
+        for word, word_tokens in zip(split_words, split_tokens):
+            if replacement_char in word:
+                logger.warning(f"[UTF-8 Filter] Skipping: {repr(word)}")
+                timestamp_idx += len(word_tokens)
+                continue
+
+            try:
+                current_timestamp = l_absolute_timestamps[timestamp_idx]
+            except IndexError:
+                logger.warning(
+                    f"Timestamp index {timestamp_idx} out of range, using last timestamp"
+                )
+                current_timestamp = (
+                    l_absolute_timestamps[-1] if l_absolute_timestamps else 0.0
+                )
+            timestamp_idx += len(word_tokens)
+
+            timestamp_entry = ASRToken(
+                start=round(current_timestamp, 2),
+                end=round(current_timestamp + 0.1, 2),
+                text=word,
+                speaker=self.state.speaker,
+                detected_language=self.state.detected_language,
+            ).with_offset(self.state.global_time_offset)
+            timestamped_words.append(timestamp_entry)
+
+        return timestamped_words
+
+    def _handle_pending_tokens(self, split_words, split_tokens):
+        """Handle incomplete UTF-8 tokens for next chunk."""
+        self.state.pending_incomplete_tokens = []
+        MAX_PENDING_TOKENS = 10
+        replacement_char = "\ufffd"
+        if split_words and replacement_char in split_words[-1]:
+            if len(split_tokens[-1]) <= MAX_PENDING_TOKENS:
+                self.state.pending_incomplete_tokens = split_tokens[-1]
+                logger.debug(
+                    f"[UTF-8 Fix] Holding {len(self.state.pending_incomplete_tokens)} "
+                    f"incomplete tokens for next chunk"
+                )
+            else:
+                logger.warning(
+                    f"[UTF-8 Fix] Skipping {len(split_tokens[-1])} tokens "
+                    f"(exceeds limit of {MAX_PENDING_TOKENS}, likely hallucination)"
+                )
+
+    # === Abstract methods — subclass must implement ===
+
+    @abstractmethod
+    def _init_state(self, cfg: AlignAttConfig):
+        """Initialize per-session decoder state."""
+        ...
+
+    @abstractmethod
+    def init_tokens(self):
+        """Initialize token sequence with framework-specific tensors."""
+        ...
+
+    @abstractmethod
+    def init_context(self):
+        """Initialize context buffer with framework-specific TokenBuffer."""
+        ...
+
+    @abstractmethod
+    def insert_audio(self, segment=None):
+        """Insert audio segment into buffer."""
+        ...
+
+    @abstractmethod
+    def _current_tokens(self):
+        """Build current token tensor for decoding."""
+        ...
+
+    @abstractmethod
+    def fire_at_boundary(self, feature):
+        """Check if we should fire at word boundary."""
+        ...
+
+    @abstractmethod
+    def lang_id(self, encoder_features):
+        """Language detection from encoder features. Returns (tokens, probs)."""
+        ...
+
+    @abstractmethod
+    def _concat_segments(self):
+        """Concatenate audio segments into single array/tensor."""
+        ...
+
+    @abstractmethod
+    def _encode(self, input_segments):
+        """Encode audio. Returns (encoder_feature, content_mel_len)."""
+        ...
+
+    @abstractmethod
+    def _init_sum_logprobs(self):
+        """Create zero sum_logprobs tensor for beam search."""
+        ...
+
+    @abstractmethod
+    def _get_logits_and_cross_attn(self, tokens, encoder_feature):
+        """Get logits and cross-attention from decoder. Returns (logits, cross_attns)."""
+        ...
+
+    @abstractmethod
+    def _check_no_speech(self, logits):
+        """Check no_speech probability at start of segment. Returns True to break."""
+        ...
+
+    @abstractmethod
+    def _suppress_blank_tokens(self, logits):
+        """Suppress blank/EOT tokens at segment start. Returns modified logits."""
+        ...
+
+    @abstractmethod
+    def _apply_token_suppression(self, logits):
+        """Apply general token suppression. Returns modified logits."""
+        ...
+
+    @abstractmethod
+    def _update_tokens(self, current_tokens, logits, sum_logprobs):
+        """Update tokens via decoder. Returns (current_tokens, completed)."""
+        ...
+
+    @abstractmethod
+    def _process_cross_attention(self, accumulated_cross_attns, content_mel_len):
+        """Process cross-attention for alignment. Returns attention tensor."""
+        ...
+
+    @abstractmethod
+    def _get_attended_frames(self, attn):
+        """Get most attended frames. Returns (frames_as_python_list, first_frame_int)."""
+        ...
+
+    @abstractmethod
+    def _is_special_token(self, current_tokens):
+        """Check if second-to-last token is a special token (>= DEC_PAD)."""
+        ...
+
+    @abstractmethod
+    def _rewind_tokens(self):
+        """Concatenate state tokens for rewind. Returns token tensor."""
+        ...
+
+    @abstractmethod
+    def _tokens_to_list(self, current_tokens, start_col):
+        """Extract tokens as Python list from start_col onwards."""
+        ...
+
+    @abstractmethod
+    def _make_new_tokens_tensor(self, hypothesis):
+        """Create tensor from hypothesis token list, repeated for beam search."""
+        ...
+
+    @abstractmethod
+    def _evaluate(self, tensor):
+        """Evaluate lazy tensor (mx.eval for MLX, no-op for PyTorch)."""
+        ...

whisperlivekit/simul_whisper/backend.py

@@ -1,151 +1,108 @@
-import sys
-import numpy as np
-import logging
-from typing import List, Tuple, Optional
-import logging
-from whisperlivekit.timed_objects import ASRToken, Transcript
-from whisperlivekit.warmup import load_file
-from whisperlivekit.simul_whisper.license_simulstreaming import SIMULSTREAMING_LICENSE
-from .whisper import load_model, tokenizer
-from .whisper.audio import TOKENS_PER_SECOND
-
-import os
 import gc
+import logging
+import os
+import platform
+import sys
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+
+from whisperlivekit.backend_support import (faster_backend_available,
+                                            mlx_backend_available)
+from whisperlivekit.model_paths import detect_model_format, resolve_model_path
+from whisperlivekit.simul_whisper.config import AlignAttConfig
+from whisperlivekit.simul_whisper.simul_whisper import AlignAtt
+from whisperlivekit.timed_objects import ASRToken, ChangeSpeaker, Transcript
+from whisperlivekit.warmup import load_file
+from whisperlivekit.whisper import load_model, tokenizer
+from whisperlivekit.whisper.audio import TOKENS_PER_SECOND
+
 logger = logging.getLogger(__name__)
 
-import torch
-from whisperlivekit.simul_whisper.config import AlignAttConfig
-from whisperlivekit.simul_whisper.simul_whisper import PaddedAlignAttWhisper
-from whisperlivekit.simul_whisper.whisper import tokenizer
 
-try:
-    from .mlx_encoder import mlx_model_mapping, load_mlx_encoder
-    HAS_MLX_WHISPER = True
-except ImportError:
-    HAS_MLX_WHISPER = False
+HAS_MLX_WHISPER = mlx_backend_available(warn_on_missing=True)
 if HAS_MLX_WHISPER:
-    HAS_FASTER_WHISPER = False
+    from .mlx_encoder import load_mlx_encoder, load_mlx_model, mlx_model_mapping
+    from .mlx import MLXAlignAtt
 else:
-    try:
-        from faster_whisper import WhisperModel
-        HAS_FASTER_WHISPER = True
-    except ImportError:
-        HAS_FASTER_WHISPER = False
+    mlx_model_mapping = {}
+    MLXAlignAtt = None
+HAS_FASTER_WHISPER = faster_backend_available(warn_on_missing=not HAS_MLX_WHISPER)
+if HAS_FASTER_WHISPER:
+    from faster_whisper import WhisperModel
+else:
+    WhisperModel = None
 
-
-# TOO_MANY_REPETITIONS = 3
+MIN_DURATION_REAL_SILENCE = 5
 
 class SimulStreamingOnlineProcessor:
+    """Online processor for SimulStreaming ASR."""
     SAMPLING_RATE = 16000
 
-    def __init__(
-        self,
-        asr,
-        logfile=sys.stderr,
-        warmup_file=None
-    ):        
+    def __init__(self, asr, logfile=sys.stderr):
         self.asr = asr
         self.logfile = logfile
         self.end = 0.0
-        self.global_time_offset = 0.0
+        self.buffer = []
+        self.model = self._create_alignatt()
         
-        self.committed: List[ASRToken] = []
-        self.last_result_tokens: List[ASRToken] = []
-        self.load_new_backend()
-        
-        #can be moved
         if asr.tokenizer:
             self.model.tokenizer = asr.tokenizer
+            self.model.state.tokenizer = asr.tokenizer
 
-    def load_new_backend(self):
-        model = self.asr.get_new_model_instance()
-        self.model = PaddedAlignAttWhisper(
-            cfg=self.asr.cfg,
-            loaded_model=model,
-            mlx_encoder=self.asr.mlx_encoder,
-            fw_encoder=self.asr.fw_encoder,
+    def _create_alignatt(self):
+        """Create the AlignAtt decoder instance based on ASR mode."""
+        if self.asr.use_full_mlx and HAS_MLX_WHISPER:
+            return MLXAlignAtt(cfg=self.asr.cfg, mlx_model=self.asr.mlx_model)
+        else:
+            return AlignAtt(
+                cfg=self.asr.cfg,
+                loaded_model=self.asr.shared_model,
+                mlx_encoder=self.asr.mlx_encoder,
+                fw_encoder=self.asr.fw_encoder,
             )
 
-    def insert_silence(self, silence_duration, offset):
-        """
-        If silences are > 5s, we do a complete context clear. Otherwise, we just insert a small silence and shift the last_attend_frame
-        """
-        if silence_duration < 5:
-            gap_silence = torch.zeros(int(16000*silence_duration))
-            self.model.insert_audio(gap_silence)
-            # self.global_time_offset += silence_duration
-        else:
-            self.process_iter(is_last=True) #we want to totally process what remains in the buffer.
+    def start_silence(self):
+        tokens, processed_upto = self.process_iter(is_last=True)
+        return tokens, processed_upto
+
+    def end_silence(self, silence_duration, offset):
+        """Handle silence period."""
+        self.end += silence_duration
+        long_silence = silence_duration >= MIN_DURATION_REAL_SILENCE
+        if not long_silence:
+            gap_len = int(16000 * silence_duration)
+            if gap_len > 0:
+                if self.asr.use_full_mlx:
+                    gap_silence = np.zeros(gap_len, dtype=np.float32)
+                else:
+                    gap_silence = torch.zeros(gap_len)
+                self.model.insert_audio(gap_silence)
+        if long_silence:
             self.model.refresh_segment(complete=True)
-            self.global_time_offset += silence_duration + offset
+            self.model.global_time_offset = silence_duration + offset
 
-
-        
     def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time):
         """Append an audio chunk to be processed by SimulStreaming."""
-            
-        # Convert numpy array to torch tensor
-        audio_tensor = torch.from_numpy(audio).float()
-        self.end = audio_stream_end_time #Only to be aligned with what happens in whisperstreaming backend.
-        self.model.insert_audio(audio_tensor)
-
-    def get_buffer(self):
-        return Transcript(
-            start=None, 
-            end=None, 
-            text='', 
-            probability=None
-        )
-
-    def timestamped_text(self, tokens, generation):
-        """
-        generate timestamped text from tokens and generation data.
-        
-        args:
-            tokens: List of tokens to process
-            generation: Dictionary containing generation progress and optionally results
-            
-        returns:
-            List of tuples containing (start_time, end_time, word) for each word
-        """
-        FRAME_DURATION = 0.02    
-        if "result" in generation:
-            split_words = generation["result"]["split_words"]
-            split_tokens = generation["result"]["split_tokens"]
+        self.end = audio_stream_end_time
+        if self.asr.use_full_mlx:
+            self.model.insert_audio(audio)
         else:
-            split_words, split_tokens = self.model.tokenizer.split_to_word_tokens(tokens)
-        progress = generation["progress"]
-        frames = [p["most_attended_frames"][0] for p in progress]
-        absolute_timestamps = [p["absolute_timestamps"][0] for p in progress]
-        tokens_queue = tokens.copy()
-        timestamped_words = []
-        
-        for word, word_tokens in zip(split_words, split_tokens):
-            # start_frame = None
-            # end_frame = None
-            for expected_token in word_tokens:
-                if not tokens_queue or not frames:
-                    raise ValueError(f"Insufficient tokens or frames for word '{word}'")
-                    
-                actual_token = tokens_queue.pop(0)
-                current_frame = frames.pop(0)
-                current_timestamp = absolute_timestamps.pop(0)
-                if actual_token != expected_token:
-                    raise ValueError(
-                        f"Token mismatch: expected '{expected_token}', "
-                        f"got '{actual_token}' at frame {current_frame}"
-                    )
-                # if start_frame is None:
-                #     start_frame = current_frame
-                # end_frame = current_frame
-            # start_time = start_frame * FRAME_DURATION
-            # end_time = end_frame * FRAME_DURATION
-            start_time = current_timestamp
-            end_time = current_timestamp + 0.1
-            timestamp_entry = (start_time, end_time, word)
-            timestamped_words.append(timestamp_entry)
-            logger.debug(f"TS-WORD:\t{start_time:.2f}\t{end_time:.2f}\t{word}")
-        return timestamped_words
+            audio_tensor = torch.from_numpy(audio).float()
+            self.model.insert_audio(audio_tensor)
+
+    def new_speaker(self, change_speaker: ChangeSpeaker):
+        """Handle speaker change event."""
+        self.process_iter(is_last=True)
+        self.model.refresh_segment(complete=True)
+        self.model.speaker = change_speaker.speaker
+        self.model.global_time_offset = change_speaker.start
+            
+    def get_buffer(self):
+        concat_buffer = Transcript.from_tokens(tokens= self.buffer, sep='')
+        return concat_buffer
 
     def process_iter(self, is_last=False) -> Tuple[List[ASRToken], float]:
         """
@@ -154,49 +111,17 @@ class SimulStreamingOnlineProcessor:
         Returns a tuple: (list of committed ASRToken objects, float representing the audio processed up to time).
         """
         try:
-            tokens, generation_progress = self.model.infer(is_last=is_last)
-            ts_words = self.timestamped_text(tokens, generation_progress)
+            timestamped_words = self.model.infer(is_last=is_last)
             
-            new_tokens = []
-            for ts_word in ts_words:
-                
-                start, end, word = ts_word
-                token = ASRToken(
-                    start=start,
-                    end=end,
-                    text=word,
-                    probability=0.95  # fake prob. Maybe we can extract it from the model?
-                ).with_offset(
-                    self.global_time_offset
-                )
-                new_tokens.append(token)
-                
-            # identical_tokens = 0
-            # n_new_tokens = len(new_tokens)
-            # if n_new_tokens:
+            if not timestamped_words:
+                return [], self.end
             
-            self.committed.extend(new_tokens)
-            
-            # if token in self.committed:
-            #     pos = len(self.committed) - 1 - self.committed[::-1].index(token)
-            # if pos:
-            #     for i in range(len(self.committed) - n_new_tokens, -1, -n_new_tokens):
-            #         commited_segment = self.committed[i:i+n_new_tokens]
-            #         if commited_segment == new_tokens:
-            #             identical_segments +=1
-            #             if identical_tokens >= TOO_MANY_REPETITIONS:
-            #                 logger.warning('Too many repetition, model is stuck. Load a new one')
-            #                 self.committed = self.committed[:i]
-            #                 self.load_new_backend()
-            #                 return [], self.end
-
-            # pos = self.committed.rindex(token)
-
-            
-            
-            return new_tokens, self.end
-
+            if self.model.cfg.language == "auto" and timestamped_words[0].detected_language is None:
+                self.buffer.extend(timestamped_words)
+                return [], self.end
             
+            self.buffer = []
+            return timestamped_words, self.end
         except Exception as e:
             logger.exception(f"SimulStreaming processing error: {e}")
             return [], self.end
@@ -204,6 +129,10 @@ class SimulStreamingOnlineProcessor:
     def warmup(self, audio, init_prompt=""):
         """Warmup the SimulStreaming model."""
         try:
+            if self.asr.use_full_mlx:
+                # MLX mode: ensure numpy array
+                if hasattr(audio, 'numpy'):
+                    audio = audio.numpy()
             self.model.insert_audio(audio)
             self.model.infer(True)
             self.model.refresh_segment(complete=True)
@@ -212,70 +141,80 @@ class SimulStreamingOnlineProcessor:
             logger.exception(f"SimulStreaming warmup failed: {e}")
 
     def __del__(self):
-        # free the model and add a new model to stack.
-        # del self.model
         gc.collect()
-        torch.cuda.empty_cache()
-        # self.asr.new_model_to_stack()
-        self.model.remove_hooks()
+        if not getattr(self.asr, 'use_full_mlx', True) and torch is not None:
+            try:
+                torch.cuda.empty_cache()
+            except Exception:
+                pass
 
-class SimulStreamingASR():
+
+class SimulStreamingASR:
     """SimulStreaming backend with AlignAtt policy."""
     sep = ""
 
-    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr, **kwargs):
-        logger.warning(SIMULSTREAMING_LICENSE)
+    def __init__(self, logfile=sys.stderr, **kwargs):
         self.logfile = logfile
         self.transcribe_kargs = {}
-        self.original_language = lan
         
-        self.model_path = kwargs.get('model_path', './large-v3.pt')
-        self.frame_threshold = kwargs.get('frame_threshold', 25)
-        self.audio_max_len = kwargs.get('audio_max_len', 20.0)
-        self.audio_min_len = kwargs.get('audio_min_len', 0.0)
-        self.segment_length = kwargs.get('segment_length', 0.5)
-        self.beams = kwargs.get('beams', 1)
-        self.decoder_type = kwargs.get('decoder_type', 'greedy' if self.beams == 1 else 'beam')
-        self.task = kwargs.get('task', 'transcribe')
-        self.cif_ckpt_path = kwargs.get('cif_ckpt_path', None)
-        self.never_fire = kwargs.get('never_fire', False)
-        self.init_prompt = kwargs.get('init_prompt', None)
-        self.static_init_prompt = kwargs.get('static_init_prompt', None)
-        self.max_context_tokens = kwargs.get('max_context_tokens', None)
-        self.warmup_file = kwargs.get('warmup_file', None)
-        self.preload_model_count = kwargs.get('preload_model_count', 1)
-        self.disable_fast_encoder = kwargs.get('disable_fast_encoder', False)
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+        if self.decoder_type is None:
+            self.decoder_type = 'greedy' if self.beams == 1 else 'beam'
+
         self.fast_encoder = False
-        if model_dir is not None:
-            self.model_path = model_dir
-        elif modelsize is not None:
-            model_mapping = {
-                'tiny': './tiny.pt',
-                'base': './base.pt',
-                'small': './small.pt',
-                'medium': './medium.pt',
-                'medium.en': './medium.en.pt',
-                'large-v1': './large-v1.pt',
-                'base.en': './base.en.pt',
-                'small.en': './small.en.pt',
-                'tiny.en': './tiny.en.pt',
-                'large-v2': './large-v2.pt',
-                'large-v3': './large-v3.pt',
-                'large': './large-v3.pt'
-            }
-            self.model_path = model_mapping.get(modelsize, f'./{modelsize}.pt')
+        self._resolved_model_path = None
+        self.encoder_backend = "whisper"
+        self.use_full_mlx = getattr(self, "use_full_mlx", False)
+        preferred_backend = getattr(self, "backend", "auto")
+        compatible_whisper_mlx, compatible_faster_whisper = True, True
         
+        if self.model_path:
+            resolved_model_path = resolve_model_path(self.model_path)
+            self._resolved_model_path = resolved_model_path
+            self.model_path = str(resolved_model_path)
+            
+            model_info = detect_model_format(resolved_model_path)
+            compatible_whisper_mlx = model_info.compatible_whisper_mlx
+            compatible_faster_whisper = model_info.compatible_faster_whisper
+            
+            if not self.use_full_mlx and not model_info.has_pytorch:
+                raise FileNotFoundError(
+                    f"No PyTorch checkpoint (.pt/.bin/.safetensors) found under {self.model_path}"
+                )            
+            self.model_name = resolved_model_path.name if resolved_model_path.is_dir() else resolved_model_path.stem
+        elif self.model_size is not None:
+            self.model_name = self.model_size
+        else:
+            raise ValueError("Either model_size or model_path must be specified for SimulStreaming.")
+
+        is_multilingual = not self.model_name.endswith(".en")
+
+        self.encoder_backend = self._resolve_encoder_backend(
+            preferred_backend,
+            compatible_whisper_mlx,
+            compatible_faster_whisper,
+        )
+        self.fast_encoder = self.encoder_backend in ("mlx-whisper", "faster-whisper")
+        if self.encoder_backend == "whisper":
+            self.disable_fast_encoder = True
+        
+        if self.encoder_backend == "mlx-whisper" and platform.system() == "Darwin":
+            if not hasattr(self, '_full_mlx_disabled'):
+                self.use_full_mlx = True
+                    
         self.cfg = AlignAttConfig(
-                model_path=self.model_path,
-                segment_length=self.segment_length,
+                tokenizer_is_multilingual= is_multilingual,
+                segment_length=self.min_chunk_size,
                 frame_threshold=self.frame_threshold,
-                language=self.original_language,
+                language=self.lan,
                 audio_max_len=self.audio_max_len,
                 audio_min_len=self.audio_min_len,
                 cif_ckpt_path=self.cif_ckpt_path,
                 decoder_type="beam",
                 beam_size=self.beams,
-                task=self.task,
+                task="translate" if self.direct_english_translation else "transcribe",
                 never_fire=self.never_fire,
                 init_prompt=self.init_prompt,
                 max_context_tokens=self.max_context_tokens,
@@ -283,72 +222,135 @@ class SimulStreamingASR():
         )  
         
         # Set up tokenizer for translation if needed
-        if self.task == "translate":
+        if self.direct_english_translation:
             self.tokenizer = self.set_translate_task()
         else:
             self.tokenizer = None
+
+        self.mlx_encoder, self.fw_encoder, self.mlx_model = None, None, None
+        self.shared_model = None
         
-        self.model_name = os.path.basename(self.cfg.model_path).replace(".pt", "")
-        self.model_path = os.path.dirname(os.path.abspath(self.cfg.model_path))
-    
-        self.mlx_encoder, self.fw_encoder = None, None
-        if not self.disable_fast_encoder:
-            if HAS_MLX_WHISPER:
-                print('Simulstreaming will use MLX whisper for a faster encoder.')
-                mlx_model_name = mlx_model_mapping[self.model_name]
-                self.mlx_encoder = load_mlx_encoder(path_or_hf_repo=mlx_model_name)
-                self.fast_encoder = True
-            elif HAS_FASTER_WHISPER:
-                print('Simulstreaming will use Faster Whisper for the encoder.')
-                self.fw_encoder = WhisperModel(
-                    self.model_name,
-                    device='auto',
-                    compute_type='auto',
+        if self.use_full_mlx and HAS_MLX_WHISPER:
+            logger.info('MLX Whisper backend used.')
+            if self._resolved_model_path is not None:
+                mlx_model_path = str(self._resolved_model_path)
+            else:
+                mlx_model_path = mlx_model_mapping.get(self.model_name)
+            if not mlx_model_path:
+                raise FileNotFoundError(
+                    f"MLX Whisper backend requested but no compatible weights found for model '{self.model_name}'."
                 )
-                self.fast_encoder = True
+            self.mlx_model = load_mlx_model(path_or_hf_repo=mlx_model_path)
+            self._warmup_mlx_model()
+        elif self.encoder_backend == "mlx-whisper":
+            # hybrid mode: mlx encoder + pytorch decoder
+            logger.info('SimulStreaming will use MLX Whisper encoder with PyTorch decoder.')
+            if self._resolved_model_path is not None:
+                mlx_model_path = str(self._resolved_model_path)
+            else:
+                mlx_model_path = mlx_model_mapping.get(self.model_name)
+            if not mlx_model_path:
+                raise FileNotFoundError(
+                    f"MLX Whisper backend requested but no compatible weights found for model '{self.model_name}'."
+                )
+            self.mlx_encoder = load_mlx_encoder(path_or_hf_repo=mlx_model_path)
+            self.shared_model = self.load_model()
+        elif self.encoder_backend == "faster-whisper":
+            print('SimulStreaming will use Faster Whisper for the encoder.')
+            if self._resolved_model_path is not None:
+                fw_model = str(self._resolved_model_path)
+            else:
+                fw_model = self.model_name
+            self.fw_encoder = WhisperModel(
+                fw_model,
+                device='auto',
+                compute_type='auto',
+            )
+            self.shared_model = self.load_model()
+        else:
+            self.shared_model = self.load_model()
+    
+    def _warmup_mlx_model(self):
+        """Warmup the full MLX model."""
+        warmup_audio = load_file(self.warmup_file)
+        if warmup_audio is not None:
+            temp_model = MLXAlignAtt(
+                cfg=self.cfg,
+                mlx_model=self.mlx_model,
+            )
+            temp_model.warmup(warmup_audio)
+            logger.info("Full MLX model warmed up successfully")
 
-        self.models = [self.load_model() for i in range(self.preload_model_count)]
 
+    def _resolve_encoder_backend(self, preferred_backend, compatible_whisper_mlx, compatible_faster_whisper):
+        choice = preferred_backend or "auto"
+        if self.disable_fast_encoder:
+            return "whisper"
+        if choice == "whisper":
+            return "whisper"
+        if choice == "mlx-whisper":
+            if not self._can_use_mlx(compatible_whisper_mlx):
+                raise RuntimeError("mlx-whisper backend requested but MLX Whisper is unavailable or incompatible with the provided model.")
+            return "mlx-whisper"
+        if choice == "faster-whisper":
+            if not self._can_use_faster(compatible_faster_whisper):
+                raise RuntimeError("faster-whisper backend requested but Faster-Whisper is unavailable or incompatible with the provided model.")
+            return "faster-whisper"
+        if choice == "openai-api":
+            raise ValueError("openai-api backend is only supported with the LocalAgreement policy.")
+        # auto mode
+        if platform.system() == "Darwin" and self._can_use_mlx(compatible_whisper_mlx):
+            return "mlx-whisper"
+        if self._can_use_faster(compatible_faster_whisper):
+            return "faster-whisper"
+        return "whisper"
+
+    def _has_custom_model_path(self):
+        return self._resolved_model_path is not None
+
+    def _can_use_mlx(self, compatible_whisper_mlx):
+        if not HAS_MLX_WHISPER:
+            return False
+        if self._has_custom_model_path():
+            return compatible_whisper_mlx
+        return self.model_name in mlx_model_mapping
+
+    def _can_use_faster(self, compatible_faster_whisper):
+        if not HAS_FASTER_WHISPER:
+            return False
+        if self._has_custom_model_path():
+            return compatible_faster_whisper
+        return True
 
     def load_model(self):
-        whisper_model = load_model(name=self.model_name, download_root=self.model_path, decoder_only=self.fast_encoder)
+        model_ref = str(self._resolved_model_path) if self._resolved_model_path else self.model_name
+        lora_path = getattr(self, 'lora_path', None)
+        whisper_model = load_model(
+            name=model_ref,
+            download_root=getattr(self, 'model_cache_dir', None),
+            decoder_only=self.fast_encoder,
+            custom_alignment_heads=self.custom_alignment_heads,
+            lora_path=lora_path,
+        )
         warmup_audio = load_file(self.warmup_file)
         if warmup_audio is not None:
             warmup_audio = torch.from_numpy(warmup_audio).float()
-            if self.fast_encoder:                
-                temp_model = PaddedAlignAttWhisper(
+            if self.fast_encoder:
+                temp_model = AlignAtt(
                     cfg=self.cfg,
                     loaded_model=whisper_model,
                     mlx_encoder=self.mlx_encoder,
                     fw_encoder=self.fw_encoder,
                 )
                 temp_model.warmup(warmup_audio)
-                temp_model.remove_hooks()
             else:
-                # For standard encoder, use the original transcribe warmup
-                warmup_audio = load_file(self.warmup_file)
-                whisper_model.transcribe(warmup_audio, language=self.original_language if self.original_language != 'auto' else None)
+                whisper_model.transcribe(warmup_audio, language=self.lan if self.lan != 'auto' else None)
         return whisper_model
-    
-    def get_new_model_instance(self):
-        """
-        SimulStreaming cannot share the same backend because it uses global forward hooks on the attention layers.
-        Therefore, each user requires a separate model instance, which can be memory-intensive. To maintain speed, we preload the models into memory.
-        """
-        if len(self.models) == 0:
-            self.models.append(self.load_model())
-        new_model = self.models.pop()
-        return new_model
-        # self.models[0]
-
-    def new_model_to_stack(self):
-        self.models.append(self.load_model())
-        
 
     def set_translate_task(self):
         """Set up translation task."""
         if self.cfg.language == 'auto':
-            raise Exception('Translation cannot be done with language = auto')
+            raise ValueError('Translation cannot be done with language = auto')
         return tokenizer.get_tokenizer(
             multilingual=True,
             language=self.cfg.language,
@@ -360,4 +362,4 @@ class SimulStreamingASR():
         """
         Warmup is done directly in load_model
         """
-        pass
+        pass

whisperlivekit/simul_whisper/beam.py

@@ -1,17 +1,32 @@
-from .whisper.decoding import PyTorchInference
+from torch import Tensor
+
+from whisperlivekit.whisper.decoding import PyTorchInference
+
 
-# extention of PyTorchInference for beam search
 class BeamPyTorchInference(PyTorchInference):
+    """Extension of PyTorchInference for beam search with cross-attention support."""
 
-    def _kv_modules(self):
-        key_modules = [block.attn.key.cache_id for block in self.model.decoder.blocks]
-        value_modules = [block.attn.value.cache_id for block in self.model.decoder.blocks]
-        return key_modules + value_modules
+    def _kv_cache_ids(self):
+        """Get cache_id strings for self-attention key/value modules."""
+        key_ids = [block.attn.key_cache_id for block in self.model.decoder.blocks]
+        value_ids = [block.attn.value_cache_id for block in self.model.decoder.blocks]
+        return key_ids + value_ids
 
     def rearrange_kv_cache(self, source_indices):
         if source_indices != list(range(len(source_indices))):
-            for module_cache_id in self._kv_modules():
-                self.kv_cache[module_cache_id] = self.kv_cache[module_cache_id][source_indices].detach()
-    from torch import Tensor
-    def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
-        return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache)
+            for cache_id in self._kv_cache_ids():
+                if cache_id in self.kv_cache:
+                    self.kv_cache[cache_id] = self.kv_cache[cache_id][source_indices].detach()
+
+    def logits(
+        self, 
+        tokens: Tensor, 
+        audio_features: Tensor,
+        return_cross_attn: bool = False,
+    ):
+        """Get logits, optionally returning cross-attention weights."""
+        return self.model.decoder(
+            tokens, audio_features, 
+            kv_cache=self.kv_cache,
+            return_cross_attn=return_cross_attn,
+        )

whisperlivekit/simul_whisper/config.py

@@ -1,29 +1,24 @@
-# This code was originally in simul_whisper/transcriber/simul_whisper.py . It is adapted a lot for SimulStreaming.
-
 from dataclasses import dataclass, field
 from typing import Literal
 
-@dataclass
-class SimulWhisperConfig:
-    '''Options that are common for all simul policies that could be implemented in SimulWhisper.'''
-    model_path: str
-    language: str = field(default="zh")
-    nonspeech_prob: float = 0.5
-    audio_min_len: float = 1.0
-    decoder_type: Literal["greedy","beam"] = "greedy"
-    beam_size: int = 5
-    task: Literal["transcribe","translate"] = "transcribe"
-    init_prompt: str = field(default=None)
-    static_init_prompt: str = field(default=None)
-    max_context_tokens: int = field(default=None)
 
 @dataclass
-class AlignAttConfig(SimulWhisperConfig):
-    '''Options specific to the AlignAtt policy.'''
+class AlignAttConfig():
     eval_data_path: str = "tmp"
     segment_length: float = field(default=1.0, metadata = {"help": "in second"})
     frame_threshold: int = 4
     rewind_threshold: int = 200
     audio_max_len: float = 20.0
     cif_ckpt_path: str = ""
-    never_fire: bool = False
+    never_fire: bool = False
+    language: str = field(default="zh")
+    nonspeech_prob: float = 0.5
+    audio_min_len: float = 1.0
+    decoder_type: Literal["greedy","beam"] = "greedy"
+    beam_size: int = 5
+    task: Literal["transcribe","translate"] = "transcribe"
+    tokenizer_is_multilingual: bool = False
+    init_prompt: str = field(default=None)
+    static_init_prompt: str = field(default=None)
+    max_context_tokens: int = field(default=None)
+    

whisperlivekit/simul_whisper/decoder_state.py

@@ -0,0 +1,95 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Tuple
+import torch
+
+
+@dataclass
+class DecoderState:
+
+    kv_cache: Dict[str, torch.Tensor] = field(default_factory=dict)
+    
+    tokenizer: Any = None
+    detected_language: Optional[str] = None
+    reset_tokenizer_to_auto_next_call: bool = False
+    
+    tokens: List[torch.Tensor] = field(default_factory=list)
+    initial_tokens: Optional[torch.Tensor] = None
+    initial_token_length: int = 0
+    sot_index: int = 0
+    
+    align_source: Dict[int, List[Tuple[int, int]]] = field(default_factory=dict)
+    num_align_heads: int = 0
+    
+    segments: List[torch.Tensor] = field(default_factory=list)
+    
+    context: Any = None
+    
+    pending_incomplete_tokens: List[int] = field(default_factory=list)
+    
+    global_time_offset: float = 0.0
+    cumulative_time_offset: float = 0.0
+    first_timestamp: Optional[float] = None
+    last_attend_frame: int = 0
+    
+    speaker: int = -1
+    log_segments: int = 0
+    
+    CIFLinear: Optional[torch.nn.Module] = None
+    always_fire: bool = False
+    never_fire: bool = False
+    
+    suppress_tokens_fn: Any = None
+    
+    token_decoder: Any = None
+    decoder_type: str = "greedy"
+    
+    inference: Any = None
+    
+    def clean_cache(self):
+        """Clean the kv_cache after each inference step."""
+        # Explicitly delete tensor references to free GPU memory
+        if self.kv_cache:
+            for key in list(self.kv_cache.keys()):
+                tensor = self.kv_cache.pop(key, None)
+                if tensor is not None:
+                    del tensor
+
+        # Clear the dict
+        self.kv_cache.clear()
+
+        # Force GPU cache cleanup (only if CUDA is available)
+        import torch
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+        if self.decoder_type == "beam" and self.inference is not None:
+            # Create NEW dict instead of sharing reference
+            self.inference.kv_cache = {}
+            if self.token_decoder is not None:
+                self.token_decoder.reset()
+    
+    def reset(self, rewind_threshold: int = 200):
+        """
+        Reset transient state for a new segment.
+        
+        Args:
+            rewind_threshold: Value for resetting last_attend_frame
+        """
+        self.last_attend_frame = -rewind_threshold
+        self.cumulative_time_offset = 0.0
+        self.pending_incomplete_tokens = []
+        self.log_segments += 1
+    
+    def full_reset(self, rewind_threshold: int = 200):
+        """
+        Full reset including audio segments and tokens.
+        
+        Args:
+            rewind_threshold: Value for resetting last_attend_frame
+        """
+        self.reset(rewind_threshold)
+        self.segments = []
+        self.tokens = []
+        self.kv_cache = {}
+        self.first_timestamp = None
+

whisperlivekit/simul_whisper/generation_progress.py

@@ -1,43 +0,0 @@
-class Tokens:
-    def __init__(self, tokens):
-        self.tokens = tokens
-
-#    def clone(self):
-#        return Tokens(self.tokens.clone())
-
-    def __str__(self):
-        return str(self.tokens.tolist())
-
-    def __repr__(self):
-        return self.__str__()
-
-class BeamTokens(Tokens):
-    def __init__(self, tokens, beam_size):
-        self.tokens = tokens
-        self.beam_size = beam_size
-
-    def clone(self):
-        return BeamTokens(self.tokens.clone())
-
-    def __str__(self):
-        return f"BeamTokens({self.tokens.tolist()}, beam_size={self.beam_size})"
-
-    def __repr__(self):
-        return self.__str__()
-
-    def as_text(self, tokenizer):
-        return tokenizer.decode(self.tokens)
-
-class Logits(Tokens):
-    def __init__(self, logits):
-        super().__init__(logits)
-
-#    def clone(self):
-#        return Logits(self.tokens.clone(), self.beam_size)
-
-    def __str__(self):
-#        return "abc"
-        return f"Logits({self.tokens.shape})"
-
-    def __repr__(self):
-        return self.__str__()

whisperlivekit/simul_whisper/license_simulstreaming.py

@@ -1,5 +0,0 @@
-SIMULSTREAMING_LICENSE = f"""
-SimulStreaming backend is dual-licensed:
-• Non-Commercial Use: PolyForm Noncommercial License 1.0.0.
-• Commercial Use: Check SimulStreaming README (github.com/ufal/SimulStreaming) for more details.
-"""

whisperlivekit/simul_whisper/mlx/__init__.py

@@ -0,0 +1,11 @@
+from .decoder_state import MLXDecoderState
+from .decoders import MLXBeamSearchDecoder, MLXGreedyDecoder, MLXInference
+from .simul_whisper import MLXAlignAtt
+
+__all__ = [
+    "MLXAlignAtt",
+    "MLXBeamSearchDecoder",
+    "MLXDecoderState",
+    "MLXGreedyDecoder",
+    "MLXInference",
+]

whisperlivekit/simul_whisper/mlx/decoder_state.py

@@ -0,0 +1,76 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Tuple
+
+import mlx.core as mx
+import numpy as np
+
+
+@dataclass
+class MLXDecoderState:
+    """
+    mlx kv cache format: List of ((k, v), (cross_k, cross_v)) tuples per layer,
+    where each element is a tuple of mx.arrays.
+    """
+
+    kv_cache: Optional[List[Tuple[Tuple[mx.array, mx.array], Tuple[mx.array, mx.array]]]] = None
+    
+    tokenizer: Any = None
+    detected_language: Optional[str] = None
+    reset_tokenizer_to_auto_next_call: bool = False
+    
+    tokens: List[mx.array] = field(default_factory=list)
+    initial_tokens: Optional[mx.array] = None
+    initial_token_length: int = 0
+    sot_index: int = 0    
+    align_source: Dict[int, List[Tuple[int, int]]] = field(default_factory=dict)
+    num_align_heads: int = 0    
+    segments: List[np.ndarray] = field(default_factory=list)
+    
+    context: Any = None
+    
+    pending_incomplete_tokens: List[int] = field(default_factory=list)
+    
+    global_time_offset: float = 0.0
+    cumulative_time_offset: float = 0.0
+    first_timestamp: Optional[float] = None
+    last_attend_frame: int = 0
+    
+    speaker: int = -1
+    log_segments: int = 0    
+    cif_weights: Optional[mx.array] = None
+    always_fire: bool = False
+    never_fire: bool = False
+    
+    suppress_tokens: Optional[Tuple[int, ...]] = None
+    
+    token_decoder: Any = None
+    decoder_type: str = "greedy"
+    
+    inference: Any = None
+    
+    def clean_cache(self):
+        self.kv_cache = None
+        if self.decoder_type == "beam" and self.inference is not None:
+            self.inference.kv_cache = None
+            if self.token_decoder is not None:
+                self.token_decoder.reset()
+    
+    def reset(self, rewind_threshold: int = 200):
+        self.last_attend_frame = -rewind_threshold
+        self.cumulative_time_offset = 0.0
+        self.pending_incomplete_tokens = []
+        self.log_segments += 1
+    
+    def full_reset(self, rewind_threshold: int = 200):
+        """
+        Full reset including audio segments and tokens.
+        
+        Args:
+            rewind_threshold: Value for resetting last_attend_frame
+        """
+        self.reset(rewind_threshold)
+        self.segments = []
+        self.tokens = []
+        self.kv_cache = None
+        self.first_timestamp = None
+

whisperlivekit/simul_whisper/mlx/decoders.py

@@ -0,0 +1,219 @@
+"""
+MLX-native token decoders for streaming ASR.
+"""
+from typing import Any, Dict, List, Optional, Tuple
+
+import mlx.core as mx
+import numpy as np
+
+
+class MLXGreedyDecoder:
+    """Greedy decoder using MLX operations."""
+    
+    def __init__(self, temperature: float, eot: int):
+        self.temperature = temperature
+        self.eot = eot
+
+    def update(
+        self, tokens: mx.array, logits: mx.array, sum_logprobs: mx.array
+    ) -> Tuple[mx.array, bool]:
+        """
+        Update tokens with next predicted token.
+        
+        Args:
+            tokens: Current token sequence, shape (batch, seq_len)
+            logits: Logits for next token, shape (batch, vocab_size)
+            sum_logprobs: Cumulative log probabilities, shape (batch,)
+            
+        Returns:
+            Updated tokens and completion flag
+        """
+        if self.temperature == 0:
+            next_tokens = mx.argmax(logits, axis=-1)
+        else:
+            probs = mx.softmax(logits / self.temperature, axis=-1)
+            next_tokens = mx.random.categorical(mx.log(probs + 1e-10))
+        
+        logprobs = mx.softmax(logits, axis=-1)
+        logprobs = mx.log(logprobs + 1e-10)        
+        batch_size = logprobs.shape[0]
+        current_logprobs = logprobs[mx.arange(batch_size), next_tokens]        
+        mask = (tokens[:, -1] != self.eot).astype(mx.float32)
+        sum_logprobs = sum_logprobs + current_logprobs * mask        
+        eot_mask = (tokens[:, -1] == self.eot)
+        next_tokens = mx.where(eot_mask, mx.array(self.eot), next_tokens)        
+        tokens = mx.concatenate([tokens, next_tokens[:, None]], axis=1)        
+        completed = bool(mx.all(tokens[:, -1] == self.eot))
+        
+        return tokens, completed
+
+    def finalize(self, tokens: mx.array, sum_logprobs: mx.array):
+        """Finalize decoding by ensuring EOT at end."""
+        eot_column = mx.full((tokens.shape[0], 1), self.eot, dtype=tokens.dtype)
+        tokens = mx.concatenate([tokens, eot_column], axis=1)
+        return tokens, sum_logprobs.tolist()
+
+
+class MLXBeamSearchDecoder:
+    """Beam search decoder using MLX operations."""
+    
+    def __init__(
+        self,
+        beam_size: int,
+        eot: int,
+        inference: Any,
+        patience: Optional[float] = None,
+    ):
+        self.beam_size = beam_size
+        self.eot = eot
+        self.inference = inference
+        self.patience = patience or 1.0
+        self.max_candidates: int = round(beam_size * self.patience)
+        self.finished_sequences: Optional[List[Dict]] = None
+
+        assert (
+            self.max_candidates > 0
+        ), f"Invalid beam size ({beam_size}) or patience ({patience})"
+
+    def reset(self):
+        """Reset finished sequences for new segment."""
+        self.finished_sequences = None
+
+    def update(
+        self, tokens: mx.array, logits: mx.array, sum_logprobs: mx.array
+    ) -> Tuple[mx.array, bool]:
+        """
+        Update tokens using beam search.
+        
+        Args:
+            tokens: Current token sequences, shape (batch * beam_size, seq_len)
+            logits: Logits for next token, shape (batch * beam_size, vocab_size)
+            sum_logprobs: Cumulative log probabilities, shape (batch * beam_size,)
+            
+        Returns:
+            Updated tokens and completion flag
+        """
+        if tokens.shape[0] % self.beam_size != 0:
+            raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
+
+        n_audio = tokens.shape[0] // self.beam_size
+        if self.finished_sequences is None:
+            self.finished_sequences = [{} for _ in range(n_audio)]
+        logprobs = mx.softmax(logits, axis=-1)
+        logprobs = mx.log(logprobs + 1e-10)        
+        logprobs_np = np.array(logprobs)
+        tokens_np = np.array(tokens)
+        sum_logprobs_np = np.array(sum_logprobs)
+        
+        next_tokens, source_indices, finished_sequences = [], [], []
+        new_sum_logprobs = []
+        
+        for i in range(n_audio):
+            scores, sources, finished = {}, {}, {}
+            for j in range(self.beam_size):
+                idx = i * self.beam_size + j
+                prefix = tokens_np[idx].tolist()                
+                top_k_indices = np.argsort(logprobs_np[idx])[-self.beam_size - 1:][::-1]
+                
+                for token_idx in top_k_indices:
+                    logprob = logprobs_np[idx, token_idx]
+                    new_logprob = sum_logprobs_np[idx] + logprob
+                    sequence = tuple(prefix + [int(token_idx)])
+                    scores[sequence] = new_logprob
+                    sources[sequence] = idx
+            saved = 0
+            for sequence in sorted(scores, key=scores.get, reverse=True):
+                if sequence[-1] == self.eot:
+                    finished[sequence] = scores[sequence]
+                else:
+                    new_sum_logprobs.append(scores[sequence])
+                    next_tokens.append(sequence)
+                    source_indices.append(sources[sequence])
+
+                    saved += 1
+                    if saved == self.beam_size:
+                        break
+
+            finished_sequences.append(finished)
+        tokens = mx.array(np.array(next_tokens, dtype=np.int32))
+        sum_logprobs = mx.array(np.array(new_sum_logprobs, dtype=np.float32))        
+        self.inference.rearrange_kv_cache(source_indices)
+        assert len(self.finished_sequences) == len(finished_sequences)
+        for previously_finished, newly_finished in zip(
+            self.finished_sequences, finished_sequences
+        ):
+            for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
+                if len(previously_finished) >= self.max_candidates:
+                    break
+                previously_finished[seq] = newly_finished[seq]
+        completed = all(
+            len(sequences) >= self.max_candidates
+            for sequences in self.finished_sequences
+        )
+        
+        return tokens, completed
+
+    def finalize(self, preceding_tokens: mx.array, sum_logprobs: mx.array):
+        """Finalize beam search by selecting best sequences."""
+        preceding_tokens_np = np.array(preceding_tokens)
+        sum_logprobs_np = np.array(sum_logprobs)
+        
+        n_audio = preceding_tokens_np.shape[0] // self.beam_size
+        tokens_list: List[List[int]] = [[] for _ in range(n_audio)]
+        sum_logprobs_list: List[float] = [0.0] * n_audio
+
+        for i, sequences in enumerate(self.finished_sequences):
+            if sequences:
+                best_seq = max(sequences, key=sequences.get)
+                tokens_list[i] = list(best_seq)
+                sum_logprobs_list[i] = sequences[best_seq]
+            else:
+                idx = i * self.beam_size
+                tokens_list[i] = preceding_tokens_np[idx].tolist() + [self.eot]
+                sum_logprobs_list[i] = float(sum_logprobs_np[idx])
+        max_len = max(len(t) for t in tokens_list)
+        for i, t in enumerate(tokens_list):
+            tokens_list[i] = t + [self.eot] * (max_len - len(t))
+
+        tokens = mx.array(np.array(tokens_list, dtype=np.int32))
+        return tokens, sum_logprobs_list
+
+
+class MLXInference:
+    """MLX inference wrapper for beam search KV cache management."""
+    
+    def __init__(self, model, initial_token_length: int):
+        self.model = model
+        self.initial_token_length = initial_token_length
+        self.kv_cache = None
+    
+    def rearrange_kv_cache(self, source_indices: List[int]):
+        """Rearrange KV cache based on beam search source indices."""
+        if self.kv_cache is None:
+            return
+            
+        if source_indices == list(range(len(source_indices))):
+            return
+        
+        source_indices_mx = mx.array(source_indices, dtype=mx.int32)
+        
+        new_cache = []
+        for layer_cache in self.kv_cache:
+            (k, v), (cross_k, cross_v) = layer_cache            
+            new_k = k[source_indices_mx]
+            new_v = v[source_indices_mx]
+            new_cache.append(((new_k, new_v), (cross_k, cross_v)))
+        
+        self.kv_cache = new_cache
+    
+    def logits(
+        self, 
+        tokens: mx.array, 
+        audio_features: mx.array,
+    ) -> Tuple[mx.array, List]:
+        """Get logits from decoder with KV cache."""
+        logits, self.kv_cache, cross_qk = self.model.decoder(
+            tokens, audio_features, kv_cache=self.kv_cache
+        )
+        return logits, cross_qk
+

whisperlivekit/simul_whisper/mlx/simul_whisper.py

@@ -0,0 +1,421 @@
+"""MLX whisper AlignAtt streaming decoder."""
+import logging
+from typing import Any, List, Tuple
+
+import mlx.core as mx
+import numpy as np
+
+from mlx_whisper.audio import log_mel_spectrogram as mlx_log_mel_spectrogram
+from mlx_whisper.transcribe import pad_or_trim as mlx_pad_or_trim
+
+from whisperlivekit.whisper.audio import N_FRAMES, N_SAMPLES, TOKENS_PER_SECOND
+
+from ..align_att_base import DEC_PAD, AlignAttBase
+from ..config import AlignAttConfig
+from .decoder_state import MLXDecoderState
+from .decoders import MLXBeamSearchDecoder, MLXGreedyDecoder, MLXInference
+
+
+logger = logging.getLogger(__name__)
+
+
+class MLXTokenBuffer:
+    """Token buffer for MLX-based decoding."""
+
+    def __init__(self, text="", tokenizer=None, prefix_token_ids=None):
+        self.text = text
+        self.prefix_token_ids = prefix_token_ids or []
+        self.tokenizer = tokenizer
+        self.pending_token_ids = []
+
+    def as_token_ids(self, tokenizer=None):
+        if tokenizer is None:
+            tokenizer = self.tokenizer
+        if tokenizer is None:
+            raise ValueError("Tokenizer is not set.")
+        return self.prefix_token_ids + tokenizer.encode(self.text)
+
+    def as_mlx_array(self) -> mx.array:
+        tok_ids = self.as_token_ids()
+        return mx.array([tok_ids], dtype=mx.int32)
+
+    def as_mlx_array_beam(self, beam: int) -> mx.array:
+        t = self.as_mlx_array()
+        return mx.repeat(t, beam, axis=0)
+
+    def as_text(self):
+        return self.text
+
+    @staticmethod
+    def empty(*a, **kw):
+        return MLXTokenBuffer(*a, **kw)
+
+    @staticmethod
+    def from_text(text, *a, **kw):
+        return MLXTokenBuffer(*a, text=text, **kw)
+
+    def is_empty(self):
+        return self.text is None or self.text == ""
+
+    def trim_words(self, num=1, after=0):
+        tokenizer = self.tokenizer
+        assert tokenizer is not None, "Tokenizer is not set."
+        ids = tokenizer.encode(self.text[after:])
+        words, wids = self.tokenizer.split_to_word_tokens(ids)
+        if not words:
+            return 0
+        self.text = self.text[:after] + "".join(words[num:])
+        return sum(len(wi) for wi in wids[:num])
+
+    def append_token_ids(self, token_ids):
+        tokenizer = self.tokenizer
+        assert tokenizer is not None, "Tokenizer is not set."
+        all_tokens = self.pending_token_ids + token_ids
+        decoded = tokenizer.decode(all_tokens)
+        replacement_char = "\ufffd"
+        if replacement_char in decoded:
+            if len(all_tokens) > 1:
+                decoded_partial = tokenizer.decode(all_tokens[:-1])
+                if replacement_char not in decoded_partial:
+                    self.text += decoded_partial
+                    self.pending_token_ids = [all_tokens[-1]]
+                else:
+                    self.pending_token_ids = all_tokens
+            else:
+                self.pending_token_ids = all_tokens
+        else:
+            self.text += decoded
+            self.pending_token_ids = []
+
+
+def mlx_median_filter(x: mx.array, filter_width: int) -> mx.array:
+    """Apply median filter along the last axis."""
+    if filter_width <= 1:
+        return x
+    pad_width = filter_width // 2
+    shape = x.shape
+    left_pad = mx.repeat(x[..., :1], pad_width, axis=-1)
+    right_pad = mx.repeat(x[..., -1:], pad_width, axis=-1)
+    x_padded = mx.concatenate([left_pad, x, right_pad], axis=-1)
+    result = []
+    for i in range(shape[-1]):
+        window = x_padded[..., i:i + filter_width]
+        sorted_window = mx.sort(window, axis=-1)
+        median_val = sorted_window[..., filter_width // 2:filter_width // 2 + 1]
+        result.append(median_val)
+    return mx.concatenate(result, axis=-1)
+
+
+class MLXAlignAtt(AlignAttBase):
+    """
+    MLX-native Alignment-based Attention decoder for SimulStreaming.
+
+    Runs entirely on MLX, with no PyTorch dependencies for inference.
+    """
+
+    def __init__(
+        self,
+        cfg: AlignAttConfig,
+        mlx_model: Any,
+    ) -> None:
+        # Common init (sets self.model, self.cfg, decode_options, etc.)
+        self._base_init(cfg, mlx_model)
+        logger.info(f"MLX Model dimensions: {self.model.dims}")
+
+        # Per-session state
+        self.state = MLXDecoderState()
+        self._init_state(cfg)
+
+    def _init_state(self, cfg: AlignAttConfig):
+        self._init_state_common(cfg)
+
+        # CIF: MLX doesn't support CIF checkpoint loading
+        if cfg.cif_ckpt_path is None or not cfg.cif_ckpt_path:
+            if cfg.never_fire:
+                self.state.never_fire = True
+                self.state.always_fire = False
+            else:
+                self.state.always_fire = True
+                self.state.never_fire = False
+        else:
+            logger.warning(
+                "CIF checkpoint provided but MLX CIF not implemented. "
+                "Using always_fire=True"
+            )
+            self.state.always_fire = True
+            self.state.never_fire = cfg.never_fire
+
+        self._build_alignment_source()
+
+        # Suppress tokens
+        suppress_tokens = [
+            self.tokenizer.transcribe, self.tokenizer.translate,
+            self.tokenizer.sot, self.tokenizer.sot_prev,
+            self.tokenizer.sot_lm, self.tokenizer.no_timestamps,
+        ] + list(self.tokenizer.all_language_tokens)
+        if self.tokenizer.no_speech is not None:
+            suppress_tokens.append(self.tokenizer.no_speech)
+        self.state.suppress_tokens = tuple(sorted(set(suppress_tokens)))
+        logger.debug(f"Suppress tokens: {self.state.suppress_tokens}")
+
+        self.init_tokens()
+        self.init_context()
+
+        # Decoder type
+        self.state.decoder_type = cfg.decoder_type
+        if cfg.decoder_type == "greedy":
+            logger.info("Using MLX greedy decoder")
+            self.state.token_decoder = MLXGreedyDecoder(0.0, self.tokenizer.eot)
+        elif cfg.decoder_type == "beam":
+            logger.info("Using MLX beam decoder")
+            self.state.inference = MLXInference(
+                self.model, self.state.initial_token_length,
+            )
+            self.state.token_decoder = MLXBeamSearchDecoder(
+                inference=self.state.inference,
+                eot=self.tokenizer.eot,
+                beam_size=cfg.beam_size,
+            )
+
+    def _build_alignment_source(self):
+        """Build alignment source mapping from model's alignment_heads."""
+        self.state.align_source = {}
+        self.state.num_align_heads = 0
+        alignment_heads = self.model.alignment_heads
+        if alignment_heads is None:
+            logger.warning("No alignment heads found in model")
+            return
+        if hasattr(alignment_heads, 'tolist'):
+            heads_list = alignment_heads.tolist()
+        else:
+            heads_list = np.array(alignment_heads).tolist()
+        for layer_rank, head_id in heads_list:
+            layer_rank = int(layer_rank)
+            head_id = int(head_id)
+            heads = self.state.align_source.get(layer_rank, [])
+            heads.append((self.state.num_align_heads, head_id))
+            self.state.align_source[layer_rank] = heads
+            self.state.num_align_heads += 1
+
+    # === Abstract method implementations ===
+
+    def init_tokens(self):
+        logger.debug(f"init tokens, {len(self.state.segments)}")
+        self.state.initial_tokens = mx.array(
+            [self.tokenizer.sot_sequence_including_notimestamps],
+            dtype=mx.int32,
+        )
+        self.state.initial_token_length = self.state.initial_tokens.shape[1]
+        self.state.sot_index = self.tokenizer.sot_sequence.index(self.tokenizer.sot)
+        logger.debug(f"init tokens after, {len(self.state.segments)}")
+        self.state.tokens = [self.state.initial_tokens]
+
+    def init_context(self):
+        kw = {
+            'tokenizer': self.tokenizer,
+            'prefix_token_ids': [self.tokenizer.sot_prev],
+        }
+        self.state.context = MLXTokenBuffer.empty(**kw)
+        if self.cfg.static_init_prompt is not None:
+            self.state.context = MLXTokenBuffer.from_text(self.cfg.static_init_prompt, **kw)
+        if self.cfg.init_prompt is not None:
+            self.state.context.text += self.cfg.init_prompt
+
+    def insert_audio(self, segment=None):
+        if segment is not None:
+            if hasattr(segment, 'numpy'):
+                segment = segment.numpy()
+            self.state.segments.append(segment)
+        removed_len = 0
+        segments_len = self.segments_len()
+        while len(self.state.segments) > 1 and segments_len > self.cfg.audio_max_len:
+            removed_len = self.state.segments[0].shape[0] / 16000
+            segments_len -= removed_len
+            self.state.last_attend_frame -= int(TOKENS_PER_SECOND * removed_len)
+            self.state.cumulative_time_offset += removed_len
+            self.state.segments = self.state.segments[1:]
+            logger.debug(
+                f"remove segments: {len(self.state.segments)} {len(self.state.tokens)}, "
+                f"cumulative offset: {self.state.cumulative_time_offset:.2f}s"
+            )
+            if len(self.state.tokens) > 1:
+                token_list = np.array(self.state.tokens[1][0, :]).tolist()
+                self.state.context.append_token_ids(token_list)
+                self.state.tokens = [self.state.initial_tokens] + self.state.tokens[2:]
+        return removed_len
+
+    def _current_tokens(self) -> mx.array:
+        toks = self.state.tokens
+        if toks[0].shape[0] == 1:
+            toks[0] = mx.repeat(toks[0], self.cfg.beam_size, axis=0)
+        if not self.state.context.is_empty():
+            context_toks = self.state.context.as_mlx_array_beam(self.cfg.beam_size)
+            toks = [context_toks] + toks
+        if len(toks) > 1:
+            current_tokens = mx.concatenate(toks, axis=1)
+        else:
+            current_tokens = toks[0]
+        logger.debug("debug print current_tokens:")
+        self.debug_print_tokens(current_tokens)
+        return current_tokens
+
+    def fire_at_boundary(self, chunked_encoder_feature: mx.array) -> bool:
+        if self.state.always_fire:
+            return True
+        if self.state.never_fire:
+            return False
+        return True  # MLX CIF not implemented
+
+    def lang_id(self, encoder_features: mx.array) -> Tuple[mx.array, List[dict]]:
+        n_audio = encoder_features.shape[0]
+        x = mx.array([[self.tokenizer.sot]] * n_audio, dtype=mx.int32)
+        logits, _, _ = self.model.decoder(x, encoder_features, kv_cache=None)
+        logits = logits[:, 0]
+
+        mask = mx.ones(logits.shape[-1], dtype=mx.bool_)
+        language_token_indices = mx.array(
+            list(self.tokenizer.all_language_tokens), dtype=mx.int32,
+        )
+        mask = mask.at[language_token_indices].add(False)
+        logits = mx.where(mask, mx.array(-float('inf')), logits)
+
+        language_tokens = mx.argmax(logits, axis=-1)
+        language_token_probs = mx.softmax(logits, axis=-1)
+        probs_np = np.array(language_token_probs)
+        language_probs = [
+            {
+                c: float(probs_np[i, j])
+                for j, c in zip(
+                    self.tokenizer.all_language_tokens,
+                    self.tokenizer.all_language_codes,
+                )
+            }
+            for i in range(n_audio)
+        ]
+        self._clean_cache()
+        return language_tokens, language_probs
+
+    def _concat_segments(self):
+        if len(self.state.segments) > 1:
+            return np.concatenate(self.state.segments, axis=0)
+        return self.state.segments[0]
+
+    def _encode(self, input_segments):
+        mlx_mel_padded = mlx_log_mel_spectrogram(
+            audio=input_segments,
+            n_mels=self.model.dims.n_mels,
+            padding=N_SAMPLES,
+        )
+        mlx_mel = mlx_pad_or_trim(mlx_mel_padded, N_FRAMES, axis=-2)
+        encoder_feature = self.model.encoder(mlx_mel[None])
+        content_mel_len = int((mlx_mel_padded.shape[0] - mlx_mel.shape[0]) / 2)
+        return encoder_feature, content_mel_len
+
+    def _init_sum_logprobs(self):
+        return mx.zeros((self.cfg.beam_size,), dtype=mx.float32)
+
+    def _get_logits_and_cross_attn(self, tokens, encoder_feature):
+        if self.state.decoder_type == "greedy":
+            logits, self.state.kv_cache, cross_qk = self.model.decoder(
+                tokens, encoder_feature, kv_cache=self.state.kv_cache,
+            )
+            return logits, cross_qk
+        else:
+            return self.state.inference.logits(tokens, encoder_feature)
+
+    def _check_no_speech(self, logits):
+        if self.tokenizer.no_speech is not None:
+            probs_at_sot = mx.softmax(logits[:, self.state.sot_index, :], axis=-1)
+            no_speech_probs = np.array(
+                probs_at_sot[:, self.tokenizer.no_speech],
+            ).tolist()
+            if no_speech_probs[0] > self.cfg.nonspeech_prob:
+                logger.info("no speech, stop")
+                return True
+        return False
+
+    def _suppress_blank_tokens(self, logits):
+        blank_tokens = self.tokenizer.encode(" ") + [self.tokenizer.eot]
+        logits = logits.at[:, blank_tokens].add(-float('inf'))
+        return logits
+
+    def _apply_token_suppression(self, logits):
+        if self.state.suppress_tokens:
+            suppress_indices = mx.array(
+                list(self.state.suppress_tokens), dtype=mx.int32,
+            )
+            logits = logits.at[:, suppress_indices].add(-float('inf'))
+        return logits
+
+    def _update_tokens(self, current_tokens, logits, sum_logprobs):
+        return self.state.token_decoder.update(current_tokens, logits, sum_logprobs)
+
+    def _process_cross_attention(
+        self, cross_attns: List, content_mel_len: int,
+    ) -> mx.array:
+        attn_of_alignment_heads = [[] for _ in range(self.state.num_align_heads)]
+        num_decoder_layers = self.num_decoder_layers
+
+        if cross_attns and isinstance(cross_attns[0], list):
+            flattened_attns = [attn for layer_list in cross_attns for attn in layer_list]
+        else:
+            flattened_attns = cross_attns
+
+        for idx, attn_mat in enumerate(flattened_attns):
+            if attn_mat is None:
+                continue
+            layer_rank = idx % num_decoder_layers
+            align_heads_in_layer = self.state.align_source.get(layer_rank, [])
+            if not align_heads_in_layer:
+                continue
+            attn_mat = mx.softmax(attn_mat, axis=-1)
+            for align_head_rank, head_id in align_heads_in_layer:
+                if self.cfg.beam_size == 1:
+                    if attn_mat.ndim == 4:
+                        a = attn_mat[0, head_id, :, :]
+                    else:
+                        a = attn_mat[head_id, :, :]
+                    a = a[None, :, :]
+                else:
+                    a = attn_mat[:, head_id, :, :]
+                attn_of_alignment_heads[align_head_rank].append(a)
+
+        tmp = []
+        for mat in attn_of_alignment_heads:
+            if mat:
+                tmp.append(mx.concatenate(mat, axis=1))
+        if not tmp:
+            return mx.zeros((self.cfg.beam_size, 1, content_mel_len))
+
+        attn_of_alignment_heads = mx.stack(tmp, axis=1)
+        std = mx.std(attn_of_alignment_heads, axis=-2, keepdims=True)
+        mean = mx.mean(attn_of_alignment_heads, axis=-2, keepdims=True)
+        attn_of_alignment_heads = (attn_of_alignment_heads - mean) / (std + 1e-8)
+        attn_of_alignment_heads = mlx_median_filter(attn_of_alignment_heads, 7)
+        attn_of_alignment_heads = mx.mean(attn_of_alignment_heads, axis=1)
+        attn_of_alignment_heads = attn_of_alignment_heads[:, :, :content_mel_len]
+        mx.eval(attn_of_alignment_heads)
+        return attn_of_alignment_heads
+
+    def _get_attended_frames(self, attn):
+        most_attended_frames = mx.argmax(attn[:, -1, :], axis=-1)
+        frames_np = np.array(most_attended_frames)
+        return frames_np.tolist(), int(frames_np[0])
+
+    def _is_special_token(self, current_tokens):
+        return int(np.array(current_tokens[0, -2])) >= DEC_PAD
+
+    def _rewind_tokens(self):
+        if len(self.state.tokens) > 0:
+            return mx.concatenate(self.state.tokens, axis=1)
+        return self.state.tokens[0]
+
+    def _tokens_to_list(self, current_tokens, start_col):
+        return np.array(current_tokens[0, start_col:]).tolist()
+
+    def _make_new_tokens_tensor(self, hypothesis):
+        new_tokens = mx.array([hypothesis], dtype=mx.int32)
+        return mx.repeat(new_tokens, self.cfg.beam_size, axis=0)
+
+    def _evaluate(self, tensor):
+        mx.eval(tensor)

whisperlivekit/simul_whisper/mlx_encoder.py

@@ -5,24 +5,11 @@ import mlx.core as mx
 import mlx.nn as nn
 from huggingface_hub import snapshot_download
 from mlx.utils import tree_unflatten
-
 from mlx_whisper import whisper
 
-mlx_model_mapping = {
-    "tiny.en": "mlx-community/whisper-tiny.en-mlx",
-    "tiny": "mlx-community/whisper-tiny-mlx",
-    "base.en": "mlx-community/whisper-base.en-mlx",
-    "base": "mlx-community/whisper-base-mlx",
-    "small.en": "mlx-community/whisper-small.en-mlx",
-    "small": "mlx-community/whisper-small-mlx",
-    "medium.en": "mlx-community/whisper-medium.en-mlx",
-    "medium": "mlx-community/whisper-medium-mlx",
-    "large-v1": "mlx-community/whisper-large-v1-mlx",
-    "large-v2": "mlx-community/whisper-large-v2-mlx",
-    "large-v3": "mlx-community/whisper-large-v3-mlx",
-    "large-v3-turbo": "mlx-community/whisper-large-v3-turbo",
-    "large": "mlx-community/whisper-large-mlx",
-}
+from whisperlivekit.model_mapping import MLX_MODEL_MAPPING
+
+mlx_model_mapping = MLX_MODEL_MAPPING
 
 def load_mlx_encoder(
     path_or_hf_repo: str,
@@ -69,4 +56,40 @@ def load_mlx_encoder(
 
     model.update(encoder_weights)
     mx.eval(model.parameters())
+    return model
+
+
+def load_mlx_model(
+    path_or_hf_repo: str,
+    dtype: mx.Dtype = mx.float32,
+) -> whisper.Whisper:
+    model_path = Path(path_or_hf_repo)
+    if not model_path.exists():
+        model_path = Path(snapshot_download(repo_id=path_or_hf_repo))
+
+    with open(str(model_path / "config.json"), "r") as f:
+        config = json.loads(f.read())
+        config.pop("model_type", None)
+        quantization = config.pop("quantization", None)
+
+    model_args = whisper.ModelDimensions(**config)
+
+    wf = model_path / "weights.safetensors"
+    if not wf.exists():
+        wf = model_path / "weights.npz"
+    weights = mx.load(str(wf))
+
+    model = whisper.Whisper(model_args, dtype)
+
+    if quantization is not None:
+        class_predicate = (
+            lambda p, m: isinstance(m, (nn.Linear, nn.Embedding))
+            and f"{p}.scales" in weights
+        )
+        nn.quantize(model, **quantization, class_predicate=class_predicate)
+
+    weights = tree_unflatten(list(weights.items()))
+    
+    model.update(weights)
+    mx.eval(model.parameters())
     return model

whisperlivekit/simul_whisper/simul_whisper.py

@@ -1,290 +1,198 @@
-# This code was originally in simul_whisper/transcriber/simul_whisper.py . It is adapted a lot for SimulStreaming.
-
-import os
 import logging
+import os
+from typing import List
 
+import numpy as np
 import torch
 import torch.nn.functional as F
 
-from .whisper import load_model, DecodingOptions, tokenizer
-from .config import AlignAttConfig
-from .whisper.audio import log_mel_spectrogram, TOKENS_PER_SECOND, pad_or_trim, N_SAMPLES, N_FRAMES
-from .whisper.timing import median_filter
-from .whisper.decoding import GreedyDecoder, BeamSearchDecoder, SuppressTokens, detect_language
+from whisperlivekit.backend_support import (faster_backend_available,
+                                            mlx_backend_available)
+from whisperlivekit.whisper.audio import (N_FRAMES, N_SAMPLES,
+                                          TOKENS_PER_SECOND,
+                                          log_mel_spectrogram, pad_or_trim)
+from whisperlivekit.whisper.decoding import (BeamSearchDecoder, GreedyDecoder,
+                                             SuppressTokens)
+from whisperlivekit.whisper.timing import median_filter
+
+from .align_att_base import DEC_PAD, AlignAttBase
 from .beam import BeamPyTorchInference
+from .config import AlignAttConfig
+from .decoder_state import DecoderState
 from .eow_detection import fire_at_boundary, load_cif
-import os
-from time import time
 from .token_buffer import TokenBuffer
 
-import numpy as np
-from .generation_progress import *
-
-DEC_PAD = 50257
 logger = logging.getLogger(__name__)
 
-
-try:
-    from mlx_whisper.audio import log_mel_spectrogram as mlx_log_mel_spectrogram
+if mlx_backend_available():
+    from mlx_whisper.audio import \
+        log_mel_spectrogram as mlx_log_mel_spectrogram
     from mlx_whisper.transcribe import pad_or_trim as mlx_pad_or_trim
-    HAS_MLX_WHISPER = True
-except ImportError:
-    HAS_MLX_WHISPER = False
-if HAS_MLX_WHISPER:
-    HAS_FASTER_WHISPER = False
-else:
+
+if faster_backend_available():
+    from faster_whisper.audio import pad_or_trim as fw_pad_or_trim
+    from faster_whisper.feature_extractor import FeatureExtractor
+
+USE_MLCORE = False
+
+
+def load_coreml_encoder():
     try:
-        from faster_whisper.audio import pad_or_trim as fw_pad_or_trim
-        from faster_whisper.feature_extractor import FeatureExtractor
-        HAS_FASTER_WHISPER = True
+        from coremltools.models import MLModel
     except ImportError:
-        HAS_FASTER_WHISPER = False
+        logger.warning("coremltools is not installed")
+        return None
+    COREML_ENCODER_PATH = os.environ.get(
+        "MLCORE_ENCODER_PATH",
+        "whisperlivekit/whisper/whisper_encoder.mlpackage",
+    )
+    _coreml_encoder = MLModel(COREML_ENCODER_PATH)
+    spec = _coreml_encoder.get_spec()
+    _coreml_input_name = spec.description.input[0].name if spec.description.input else "mel"
+    _coreml_output_name = spec.description.output[0].name if spec.description.output else None
+    return _coreml_encoder, _coreml_input_name, _coreml_output_name
+
+
+class AlignAtt(AlignAttBase):
+    """
+    PyTorch Alignment-based Attention decoder for SimulStreaming.
+
+    Hookless — the model can be shared across multiple sessions,
+    with each session maintaining its own DecoderState.
+    """
 
-# New features added to the original version of Simul-Whisper: 
-# - large-v3 model support
-# - translation support
-# - beam search
-# - prompt -- static vs. non-static
-# - context
-class PaddedAlignAttWhisper:
     def __init__(
-            self, 
-            cfg: AlignAttConfig,
-            loaded_model=None,
-            mlx_encoder=None,
-            fw_encoder=None,
-        ) -> None:
-        self.log_segments = 0
-        model_name = os.path.basename(cfg.model_path).replace(".pt", "")
-        model_path = os.path.dirname(os.path.abspath(cfg.model_path))
-        if loaded_model:
-            self.model = loaded_model
-        else:
-            self.model = load_model(name=model_name, download_root=model_path)
-
+        self,
+        cfg: AlignAttConfig,
+        loaded_model=None,
+        mlx_encoder=None,
+        fw_encoder=None,
+    ) -> None:
         self.mlx_encoder = mlx_encoder
         self.fw_encoder = fw_encoder
         if fw_encoder:
-            self.fw_feature_extractor = FeatureExtractor(feature_size=self.model.dims.n_mels)
-            
+            self.fw_feature_extractor = FeatureExtractor(
+                feature_size=loaded_model.dims.n_mels,
+            )
+        self.coreml_encoder_tuple = None
+        if USE_MLCORE:
+            self.coreml_encoder_tuple = load_coreml_encoder()
+        self.use_mlcore = self.coreml_encoder_tuple is not None
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+        # Common init (sets self.model, self.cfg, decode_options, etc.)
+        self._base_init(cfg, loaded_model)
         logger.info(f"Model dimensions: {self.model.dims}")
 
-        self.decode_options = DecodingOptions(
-            language = cfg.language, 
-            without_timestamps = True,
-            task=cfg.task
+        # Per-session state
+        self.state = DecoderState()
+        self._init_state(cfg)
+
+    def _init_state(self, cfg: AlignAttConfig):
+        self._init_state_common(cfg)
+
+        # CIF helpers for end-of-word boundary detection
+        self.state.CIFLinear, self.state.always_fire, self.state.never_fire = load_cif(
+            cfg, n_audio_state=self.model.dims.n_audio_state, device=self.model.device,
         )
-        self.tokenizer_is_multilingual = not model_name.endswith(".en")
-        self.create_tokenizer(cfg.language if cfg.language != "auto" else None)
-        self.detected_language = cfg.language if cfg.language != "auto" else None
-        
-        self.max_text_len = self.model.dims.n_text_ctx
-        self.num_decoder_layers = len(self.model.decoder.blocks)
-        self.cfg = cfg
-        self.l_hooks = []
 
-        # model to detect end-of-word boundary at the end of the segment
-        self.CIFLinear, self.always_fire, self.never_fire = load_cif(cfg,
-                                                                     n_audio_state=self.model.dims.n_audio_state,
-                                                                     device=self.model.device)
-
-        # install hooks to access encoder-decoder attention
-        self.dec_attns = []
-        def layer_hook(module, net_input, net_output):
-            # net_output[1]: B*num_head*token_len*audio_len
-            t = F.softmax(net_output[1], dim=-1)
-            self.dec_attns.append(t.squeeze(0))
-        for b in self.model.decoder.blocks:
-            hook = b.cross_attn.register_forward_hook(layer_hook)
-            self.l_hooks.append(hook)
-        
-        self.kv_cache = {}
-        def kv_hook(module: torch.nn.Linear, _, net_output: torch.Tensor):
-            if module.cache_id not in self.kv_cache or net_output.shape[1] > self.max_text_len:
-                # save as-is, for the first token or cross attention
-                self.kv_cache[module.cache_id] = net_output
-            else:
-                x = self.kv_cache[module.cache_id]
-                self.kv_cache[module.cache_id] = torch.cat([x, net_output], dim=1).detach()
-            return self.kv_cache[module.cache_id] 
-
-        for i,b in enumerate(self.model.decoder.blocks):
-            hooks = [
-                b.attn.key.register_forward_hook(kv_hook),
-                b.attn.value.register_forward_hook(kv_hook),
-                b.cross_attn.key.register_forward_hook(kv_hook),
-                b.cross_attn.value.register_forward_hook(kv_hook),
-            ]
-            self.l_hooks.extend(hooks)
-
-        self.align_source = {}
-        self.num_align_heads = 0
+        # Build alignment source mapping
+        self.state.align_source = {}
+        self.state.num_align_heads = 0
         for layer_rank, head_id in self.model.alignment_heads.indices().T:
             layer_rank = layer_rank.item()
-            heads = self.align_source.get(layer_rank, [])
-            heads.append((self.num_align_heads, head_id.item()))
-            self.align_source[layer_rank] = heads
-            self.num_align_heads += 1
+            heads = self.state.align_source.get(layer_rank, [])
+            heads.append((self.state.num_align_heads, head_id.item()))
+            self.state.align_source[layer_rank] = heads
+            self.state.num_align_heads += 1
 
-
-        # tokens to be suppressed from decoding, to prevent hallucinations
+        # Build suppress tokens function
         suppress_tokens = [
-                self.tokenizer.transcribe,
-                self.tokenizer.translate,
-                self.tokenizer.sot,
-                self.tokenizer.sot_prev,
-                self.tokenizer.sot_lm,
-                # self.tokenizer.eot 
-                self.tokenizer.no_timestamps,  # added by DM
-            ] + list(self.tokenizer.all_language_tokens)  # added by DM
+            self.tokenizer.transcribe, self.tokenizer.translate,
+            self.tokenizer.sot, self.tokenizer.sot_prev,
+            self.tokenizer.sot_lm, self.tokenizer.no_timestamps,
+        ] + list(self.tokenizer.all_language_tokens)
         if self.tokenizer.no_speech is not None:
             suppress_tokens.append(self.tokenizer.no_speech)
-        suppress_tokens =  tuple(sorted(set(suppress_tokens)))
+        suppress_tokens = tuple(sorted(set(suppress_tokens)))
         logger.debug(f"Suppress tokens: {suppress_tokens}")
         sup_tokens = SuppressTokens(suppress_tokens)
-        self.suppress_tokens = lambda logits: sup_tokens.apply(logits, None)
-        # blank tokens are suppresed for new segments near the line 334
+        self.state.suppress_tokens_fn = lambda logits: sup_tokens.apply(logits, None)
 
-        # it's going to be regenerated after lang id
-        self.segments = []
         self.init_tokens()
-        
-        self.last_attend_frame = -self.cfg.rewind_threshold
-        self.cumulative_time_offset = 0.0
-
-        if self.cfg.max_context_tokens is None:
-            self.max_context_tokens = self.max_text_len
-        else:
-            self.max_context_tokens = self.cfg.max_context_tokens
         self.init_context()
 
-        # decoder type: greedy or beam
+        # Decoder type
+        self.state.decoder_type = cfg.decoder_type
         if cfg.decoder_type == "greedy":
             logger.info("Using greedy decoder")
-            self.token_decoder = GreedyDecoder(0.0, self.tokenizer.eot)
-            self.decoder_type = "greedy"
-
+            self.state.token_decoder = GreedyDecoder(0.0, self.tokenizer.eot)
         elif cfg.decoder_type == "beam":
-            self.decoder_type = "beam"
-            self.inference = BeamPyTorchInference(self.model, self.initial_token_length)
-            self.inference.kv_cache = self.kv_cache
+            logger.info("Using beam decoder")
+            self.state.inference = BeamPyTorchInference(
+                self.model, self.state.initial_token_length,
+            )
+            self.state.inference.kv_cache = self.state.kv_cache
+            self.state.token_decoder = BeamSearchDecoder(
+                inference=self.state.inference,
+                eot=self.tokenizer.eot,
+                beam_size=cfg.beam_size,
+            )
 
-            self.token_decoder = BeamSearchDecoder(inference=self.inference, eot=self.tokenizer.eot, beam_size=cfg.beam_size)
-            
-    def remove_hooks(self):
-        print('remove hook')
-        for hook in self.l_hooks:
-            hook.remove()
-
-    def warmup(self, audio):
-        try:
-            self.insert_audio(audio)
-            self.infer(is_last=True)
-            self.refresh_segment(complete=True)
-            logger.info("Model warmed up successfully")
-        except Exception as e:
-            logger.exception(f"Model warmup failed: {e}")
-
-    def create_tokenizer(self, language=None):
-        self.tokenizer = tokenizer.get_tokenizer(
-            multilingual=self.tokenizer_is_multilingual,  
-            language=language,
-            num_languages=self.model.num_languages,
-            task=self.decode_options.task
-        )
-
-    def init_context(self):
-        kw = {'tokenizer': self.tokenizer, 
-              'device': self.model.device, 
-              'prefix_token_ids': [self.tokenizer.sot_prev]}
-        self.context = TokenBuffer.empty(**kw)
-        if self.cfg.static_init_prompt is not None:
-            self.context = TokenBuffer.from_text(self.cfg.static_init_prompt, **kw)
-        if self.cfg.init_prompt is not None:
-            self.context.text += self.cfg.init_prompt
+    # === Abstract method implementations ===
 
     def init_tokens(self):
-        logger.debug(f"init tokens, {len(self.segments)}")
-        # init tokens (mandatory prompt)
-        self.initial_tokens = torch.tensor(
-            self.tokenizer.sot_sequence_including_notimestamps, 
-            dtype=torch.long, 
-            device=self.model.device).unsqueeze(0)
-        self.initial_token_length = self.initial_tokens.shape[1]
-        self.sot_index = self.tokenizer.sot_sequence.index(self.tokenizer.sot)
-#        self.segments = []
-        logger.debug(f"init tokens after, {len(self.segments)}")
-        self.tokens = [self.initial_tokens]
+        logger.debug(f"init tokens, {len(self.state.segments)}")
+        self.state.initial_tokens = torch.tensor(
+            self.tokenizer.sot_sequence_including_notimestamps,
+            dtype=torch.long, device=self.model.device,
+        ).unsqueeze(0)
+        self.state.initial_token_length = self.state.initial_tokens.shape[1]
+        self.state.sot_index = self.tokenizer.sot_sequence.index(self.tokenizer.sot)
+        logger.debug(f"init tokens after, {len(self.state.segments)}")
+        self.state.tokens = [self.state.initial_tokens]
 
-    def trim_context(self):
-        logger.info("Trimming context")
-        c = len(self.context.as_token_ids()) - len(self.context.prefix_token_ids)
-#        logger.debug(f"c= {len(self.context.as_token_ids())}, {len(self.context.prefix_token_ids)}")
-        logger.info(f"Context text: {self.context.as_text()}")
-#        logger.debug(f"Context tensor: {self.context.as_tensor()}")
-        l = sum(t.shape[1] for t in self.tokens) + c
-#        logger.debug(f"len {l}, c {c}, max_context_tokens {self.max_context_tokens}")
-        if self.cfg.static_init_prompt is None:
-            after = 0
-        else:
-            after = len(self.cfg.static_init_prompt)
-#        logger.debug(f"len {l}, c {c}, max_context_tokens {self.max_context_tokens}")
-        while c > self.max_context_tokens or l > self.max_text_len - 20:
-            t = self.context.trim_words(after=after)
-            l -= t
-            c -= t
-            logger.debug(f"len {l}, c {c}, max_context_tokens {self.max_context_tokens}")
-            if t == 0:
-                break
-#        logger.debug(f"len {l}, c {c}, max_context_tokens {self.max_context_tokens}")
-        logger.info(f"Context after trim: {self.context.text} (len: {l})")
-
-
-    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor) -> torch.Tensor:
-        if self.cfg.decoder_type == "greedy":
-            logit = self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache)
-        else:
-            logger.debug(f"Logits shape: {tokens.shape}")
-            logit = self.inference.logits(tokens, audio_features)
-        return logit
-    
-
-    def refresh_segment(self, complete=False):
-
-        logger.debug("Refreshing segment:")
-        self.init_tokens()
-        self.last_attend_frame = -self.cfg.rewind_threshold       
-        self.detected_language = None
-        self.cumulative_time_offset = 0.0
-        self.init_context()
-        logger.debug(f"Context: {self.context}")
-        if not complete and len(self.segments) > 2:
-            logger.debug("keeping last two segments because they are and it is not complete.")
-            self.segments = self.segments[-2:]
-        else:
-            logger.debug("removing all segments.")
-            self.segments = []
-        self.log_segments += 1
-
-
-    def fire_at_boundary(self, chunked_encoder_feature: torch.Tensor):
-        if self.always_fire: return True
-        if self.never_fire: return False
-        return fire_at_boundary(chunked_encoder_feature, self.CIFLinear)
+    def init_context(self):
+        kw = {
+            'tokenizer': self.tokenizer,
+            'device': self.model.device,
+            'prefix_token_ids': [self.tokenizer.sot_prev],
+        }
+        self.state.context = TokenBuffer.empty(**kw)
+        if self.cfg.static_init_prompt is not None:
+            self.state.context = TokenBuffer.from_text(self.cfg.static_init_prompt, **kw)
+        if self.cfg.init_prompt is not None:
+            self.state.context.text += self.cfg.init_prompt
 
+    def insert_audio(self, segment=None):
+        if segment is not None:
+            self.state.segments.append(segment)
+        removed_len = 0
+        segments_len = self.segments_len()
+        while len(self.state.segments) > 1 and segments_len > self.cfg.audio_max_len:
+            removed_len = self.state.segments[0].shape[0] / 16000
+            segments_len -= removed_len
+            self.state.last_attend_frame -= int(TOKENS_PER_SECOND * removed_len)
+            self.state.cumulative_time_offset += removed_len
+            self.state.segments = self.state.segments[1:]
+            logger.debug(
+                f"remove segments: {len(self.state.segments)} {len(self.state.tokens)}, "
+                f"cumulative offset: {self.state.cumulative_time_offset:.2f}s"
+            )
+            if len(self.state.tokens) > 1:
+                self.state.context.append_token_ids(self.state.tokens[1][0, :].tolist())
+                self.state.tokens = [self.state.initial_tokens] + self.state.tokens[2:]
+        return removed_len
 
     def _current_tokens(self):
-
-        toks = self.tokens
-        # very first infer: duplicate start of seq to beam_size
+        toks = self.state.tokens
         if toks[0].shape[0] == 1:
-            toks[0] = toks[0].repeat_interleave(self.cfg.beam_size,dim=0)
-
-        if not self.context.is_empty():
-            context_toks = self.context.as_tensor_beam(self.cfg.beam_size, device=self.model.device)
+            toks[0] = toks[0].repeat_interleave(self.cfg.beam_size, dim=0)
+        if not self.state.context.is_empty():
+            context_toks = self.state.context.as_tensor_beam(
+                self.cfg.beam_size, device=self.model.device,
+            )
             toks = [context_toks] + toks
-
-        # make it one tensor
         if len(toks) > 1:
             current_tokens = torch.cat(toks, dim=1)
         else:
@@ -293,66 +201,19 @@ class PaddedAlignAttWhisper:
         self.debug_print_tokens(current_tokens)
         return current_tokens
 
-
-    def debug_print_tokens(self, tokens):
-        for i in range(self.cfg.beam_size):
-            logger.debug(self.tokenizer.decode_with_timestamps(tokens[i].tolist()))
-
-    ### audio buffer 
-
-    def segments_len(self):
-        segments_len = sum(s.shape[0] for s in self.segments) / 16000
-        return segments_len
-
-    def _apply_minseglen(self):
-        segments_len = self.segments_len()
-        # wait for long enough audio to start
-        if segments_len < self.cfg.audio_min_len: 
-            logger.debug("waiting for next segment")
+    def fire_at_boundary(self, chunked_encoder_feature: torch.Tensor):
+        if self.state.always_fire:
+            return True
+        if self.state.never_fire:
             return False
-        return True
-
-    def insert_audio(self, segment=None):
-        if segment is not None:
-            self.segments.append(segment)
-
-        removed_len = 0
-        # len of audio is bigger than buffer_len. Going to remove the first segment
-        segments_len = self.segments_len()
-        while len(self.segments) > 1 and segments_len > self.cfg.audio_max_len:
-            removed_len = self.segments[0].shape[0] / 16000
-            segments_len -= removed_len
-            self.last_attend_frame -= int(TOKENS_PER_SECOND*removed_len)
-            self.cumulative_time_offset += removed_len  # Track cumulative time removed
-            self.segments = self.segments[1:]
-            logger.debug(f"remove segments: {len(self.segments)} {len(self.tokens)}, cumulative offset: {self.cumulative_time_offset:.2f}s")
-            if len(self.tokens) > 1:
-                self.context.append_token_ids(self.tokens[1][0,:])
-                self.tokens = [self.initial_tokens] + self.tokens[2:]
-        return removed_len
-
-    def _clean_cache(self):
-        '''clean the cache that stores the attention matrices and kv_cache.
-        It must be called every time after generation with the model.'''
-        # cleaning cache
-        self.dec_attns = []
-        self.kv_cache = {}
-        if self.decoder_type == "beam":
-            self.inference.kv_cache = self.kv_cache
-            self.token_decoder.reset()
+        return fire_at_boundary(chunked_encoder_feature, self.state.CIFLinear)
 
     @torch.no_grad()
     def lang_id(self, encoder_features):
-        """Language detection from encoder features.
-        This code is trimmed and copy-pasted from whisper.decoding.detect_language .
-        """
-    
-        # forward pass using a single token, startoftranscript
         n_audio = encoder_features.shape[0]
-        x = torch.tensor([[self.tokenizer.sot]] * n_audio).to(self.model.device)  # [n_audio, 1]
+        x = torch.tensor([[self.tokenizer.sot]] * n_audio).to(self.model.device)
         logits = self.model.logits(x, encoder_features)[:, 0]
 
-        # collect detected languages; suppress all non-language tokens
         mask = torch.ones(logits.shape[-1], dtype=torch.bool)
         mask[list(self.tokenizer.all_language_tokens)] = False
         logits[:, mask] = -np.inf
@@ -361,311 +222,187 @@ class PaddedAlignAttWhisper:
         language_probs = [
             {
                 c: language_token_probs[i, j].item()
-                for j, c in zip(self.tokenizer.all_language_tokens, self.tokenizer.all_language_codes)
+                for j, c in zip(
+                    self.tokenizer.all_language_tokens,
+                    self.tokenizer.all_language_codes,
+                )
             }
             for i in range(n_audio)
         ]
-
         single = encoder_features.ndim == 2
         if single:
             language_tokens = language_tokens[0]
             language_probs = language_probs[0]
-
         self._clean_cache()
         return language_tokens, language_probs
 
-    ### transcription / translation
+    def _concat_segments(self):
+        if len(self.state.segments) > 1:
+            return torch.cat(self.state.segments, dim=0)
+        return self.state.segments[0]
+
+    def _encode(self, input_segments):
+        if self.use_mlcore:
+            coreml_encoder, coreml_input_name, coreml_output_name = self.coreml_encoder_tuple
+            mel_padded = log_mel_spectrogram(
+                input_segments, n_mels=self.model.dims.n_mels,
+                padding=N_SAMPLES, device="cpu",
+            ).unsqueeze(0)
+            mel = pad_or_trim(mel_padded, N_FRAMES)
+            content_mel_len = int((mel_padded.shape[2] - mel.shape[2]) / 2)
+            mel_np = np.ascontiguousarray(mel.numpy())
+            ml_inputs = {coreml_input_name or "mel": mel_np}
+            coreml_outputs = coreml_encoder.predict(ml_inputs)
+            if coreml_output_name and coreml_output_name in coreml_outputs:
+                encoder_feature_np = coreml_outputs[coreml_output_name]
+            else:
+                encoder_feature_np = next(iter(coreml_outputs.values()))
+            encoder_feature = torch.as_tensor(
+                np.array(encoder_feature_np), device=self.device,
+            )
+        if self.mlx_encoder:
+            mlx_mel_padded = mlx_log_mel_spectrogram(
+                audio=input_segments.detach(),
+                n_mels=self.model.dims.n_mels, padding=N_SAMPLES,
+            )
+            mlx_mel = mlx_pad_or_trim(mlx_mel_padded, N_FRAMES, axis=-2)
+            mlx_encoder_feature = self.mlx_encoder.encoder(mlx_mel[None])
+            encoder_feature = torch.as_tensor(mlx_encoder_feature)
+            content_mel_len = int((mlx_mel_padded.shape[0] - mlx_mel.shape[0]) / 2)
+        elif self.fw_encoder:
+            audio_length_seconds = len(input_segments) / 16000
+            content_mel_len = int(audio_length_seconds * 100) // 2
+            mel_padded_2 = self.fw_feature_extractor(
+                waveform=input_segments.numpy(), padding=N_SAMPLES,
+            )[None, :]
+            mel = fw_pad_or_trim(mel_padded_2, N_FRAMES, axis=-1)
+            encoder_feature_ctranslate = self.fw_encoder.encode(mel)
+            if self.device == 'cpu':
+                encoder_feature_ctranslate = np.array(encoder_feature_ctranslate)
+            try:
+                encoder_feature = torch.as_tensor(encoder_feature_ctranslate, device=self.device)
+            except TypeError:
+                # Some numpy/ctranslate2 versions produce object_ dtype arrays; force float32
+                arr = np.array(encoder_feature_ctranslate)
+                if arr.dtype == np.object_:
+                    arr = np.array(arr.tolist(), dtype=np.float32)
+                encoder_feature = torch.as_tensor(arr, device=self.device)
+        else:
+            mel_padded = log_mel_spectrogram(
+                input_segments, n_mels=self.model.dims.n_mels,
+                padding=N_SAMPLES, device=self.device,
+            ).unsqueeze(0)
+            mel = pad_or_trim(mel_padded, N_FRAMES)
+            content_mel_len = int((mel_padded.shape[2] - mel.shape[2]) / 2)
+            encoder_feature = self.model.encoder(mel)
+        return encoder_feature, content_mel_len
+
+    def _init_sum_logprobs(self):
+        return torch.zeros(self.cfg.beam_size, device=self.device)
+
+    def _get_logits_and_cross_attn(self, tokens, encoder_feature):
+        if self.state.decoder_type == "greedy":
+            return self.model.decoder(
+                tokens, encoder_feature,
+                kv_cache=self.state.kv_cache,
+                return_cross_attn=True,
+            )
+        else:
+            logger.debug(f"Logits shape: {tokens.shape}")
+            return self.state.inference.logits(
+                tokens, encoder_feature, return_cross_attn=True,
+            )
+
+    def _check_no_speech(self, logits):
+        if self.tokenizer.no_speech is not None:
+            probs_at_sot = logits[:, self.state.sot_index, :].float().softmax(dim=-1)
+            no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
+            if no_speech_probs[0] > self.cfg.nonspeech_prob:
+                logger.info("no speech, stop")
+                return True
+        return False
+
+    def _suppress_blank_tokens(self, logits):
+        logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
+        return logits
+
+    def _apply_token_suppression(self, logits):
+        self.state.suppress_tokens_fn(logits)
+        return logits
+
+    def _update_tokens(self, current_tokens, logits, sum_logprobs):
+        return self.state.token_decoder.update(current_tokens, logits, sum_logprobs)
+
+    def _process_cross_attention(
+        self, cross_attns: List, content_mel_len: int,
+    ) -> torch.Tensor:
+        attn_of_alignment_heads = [[] for _ in range(self.state.num_align_heads)]
+        num_decoder_layers = len(self.model.decoder.blocks)
+
+        if cross_attns and isinstance(cross_attns[0], list):
+            flattened_attns = [attn for layer_list in cross_attns for attn in layer_list]
+        else:
+            flattened_attns = cross_attns
+
+        for idx, attn_mat in enumerate(flattened_attns):
+            layer_rank = idx % num_decoder_layers
+            align_heads_in_layer = self.state.align_source.get(layer_rank, [])
+            if not align_heads_in_layer:
+                continue
+            attn_mat = F.softmax(attn_mat, dim=-1)
+            for align_head_rank, head_id in align_heads_in_layer:
+                if self.cfg.beam_size == 1:
+                    if attn_mat.dim() == 4:
+                        a = attn_mat[0, head_id, :, :]
+                    else:
+                        a = attn_mat[head_id, :, :]
+                    a = a.unsqueeze(0)
+                else:
+                    a = attn_mat[:, head_id, :, :]
+                attn_of_alignment_heads[align_head_rank].append(a)
+
+        tmp = []
+        for mat in attn_of_alignment_heads:
+            if mat:
+                tmp.append(torch.cat(mat, dim=1))
+        if not tmp:
+            return torch.zeros(self.cfg.beam_size, 1, content_mel_len, device=self.device)
+
+        attn_of_alignment_heads = torch.stack(tmp, dim=1)
+        std, mean = torch.std_mean(
+            attn_of_alignment_heads, dim=-2, keepdim=True, unbiased=False,
+        )
+        attn_of_alignment_heads = (attn_of_alignment_heads - mean) / (std + 1e-8)
+        attn_of_alignment_heads = median_filter(attn_of_alignment_heads, 7)
+        attn_of_alignment_heads = attn_of_alignment_heads.mean(dim=1)
+        attn_of_alignment_heads = attn_of_alignment_heads[:, :, :content_mel_len]
+        return attn_of_alignment_heads
+
+    def _get_attended_frames(self, attn):
+        most_attended_frames = torch.argmax(attn[:, -1, :], dim=-1)
+        return most_attended_frames.tolist(), most_attended_frames[0].item()
+
+    def _is_special_token(self, current_tokens):
+        return current_tokens[0, -2].item() >= DEC_PAD
+
+    def _rewind_tokens(self):
+        if len(self.state.tokens) > 0:
+            return torch.cat(self.state.tokens, dim=1)
+        return self.state.tokens[0]
+
+    def _tokens_to_list(self, current_tokens, start_col):
+        return current_tokens[0, start_col:].flatten().tolist()
+
+    def _make_new_tokens_tensor(self, hypothesis):
+        return (
+            torch.tensor([hypothesis], dtype=torch.long)
+            .repeat_interleave(self.cfg.beam_size, dim=0)
+            .to(device=self.device)
+        )
+
+    def _evaluate(self, tensor):
+        pass  # No-op for PyTorch
 
     @torch.no_grad()
     def infer(self, is_last=False):
-        new_segment = True
-        if len(self.segments) == 0:
-            logger.debug("No segments, nothing to do")
-            return [], {}
-        if not self._apply_minseglen():
-            logger.debug(f"applied minseglen {self.cfg.audio_min_len} > {self.segments_len()}.")
-            input_segments = torch.cat(self.segments, dim=0)
-            return [], {}
-
-        # input_segments is concatenation of audio, it's one array
-        if len(self.segments) > 1:
-            input_segments = torch.cat(self.segments, dim=0)
-        else:
-            input_segments = self.segments[0]
-
-        # NEW : we can use a different encoder, before using standart whisper for cross attention with the hooks on the decoder
-        beg_encode = time()
-        if self.mlx_encoder:
-            mlx_mel_padded = mlx_log_mel_spectrogram(audio=input_segments.detach(), n_mels=self.model.dims.n_mels, padding=N_SAMPLES)
-            mlx_mel = mlx_pad_or_trim(mlx_mel_padded, N_FRAMES, axis=-2)
-            mlx_encoder_feature = self.mlx_encoder.encoder(mlx_mel[None])
-            encoder_feature = torch.tensor(np.array(mlx_encoder_feature))
-            content_mel_len = int((mlx_mel_padded.shape[0] - mlx_mel.shape[0])/2)
-            device = 'cpu'
-        elif self.fw_encoder:
-            audio_length_seconds = len(input_segments) / 16000   
-            content_mel_len = int(audio_length_seconds * 100)//2      
-            mel_padded_2 = self.fw_feature_extractor(waveform=input_segments.numpy(), padding=N_SAMPLES)[None, :]
-            mel = fw_pad_or_trim(mel_padded_2, N_FRAMES, axis=-1)
-            encoder_feature_ctranslate = self.fw_encoder.encode(mel)
-            encoder_feature = torch.Tensor(np.array(encoder_feature_ctranslate))
-            device = 'cpu'
-        else:
-            # mel + padding to 30s
-            mel_padded = log_mel_spectrogram(input_segments, n_mels=self.model.dims.n_mels, padding=N_SAMPLES, 
-                                                device=self.model.device).unsqueeze(0)
-            # trim to 3000
-            mel = pad_or_trim(mel_padded, N_FRAMES)
-            # the len of actual audio
-            content_mel_len = int((mel_padded.shape[2] - mel.shape[2])/2)
-            encoder_feature = self.model.encoder(mel)
-            device = mel.device
-        end_encode = time()
-        # print('Encoder duration:', end_encode-beg_encode)
-                
-#        logger.debug(f"Encoder feature shape: {encoder_feature.shape}")
-#        if mel.shape[-2:] != (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
-#            logger.debug("mel ")
-        if self.cfg.language == "auto" and self.detected_language is None:
-            language_tokens, language_probs = self.lang_id(encoder_feature) 
-            logger.debug(f"Language tokens: {language_tokens}, probs: {language_probs}")
-            top_lan, p = max(language_probs[0].items(), key=lambda x: x[1])
-            logger.info(f"Detected language: {top_lan} with p={p:.4f}")
-            #self.tokenizer.language = top_lan
-            #self.tokenizer.__post_init__()
-            self.create_tokenizer(top_lan)
-            self.detected_language = top_lan
-            self.init_tokens()
-            logger.info(f"Tokenizer language: {self.tokenizer.language}, {self.tokenizer.sot_sequence_including_notimestamps}")
-
-        self.trim_context()
-        current_tokens = self._current_tokens()
-#        
-        fire_detected = self.fire_at_boundary(encoder_feature[:, :content_mel_len, :])
-
-
-        ####################### Decoding loop
-        logger.info("Decoding loop starts\n")
-
-        sum_logprobs = torch.zeros(self.cfg.beam_size, device=device)
-        completed = False
-
-        attn_of_alignment_heads = None
-        most_attended_frame = None
-
-        token_len_before_decoding = current_tokens.shape[1]
-        
-        generation_progress = []
-        generation = {
-            "starting_tokens": BeamTokens(current_tokens[0,:].clone(), self.cfg.beam_size),
-            "token_len_before_decoding": token_len_before_decoding,
-            #"fire_detected": fire_detected,
-            "frames_len": content_mel_len,
-            "frames_threshold": 4 if is_last else self.cfg.frame_threshold,
-
-            # to be filled later
-            "logits_starting": None,
-
-            # to be filled later
-            "no_speech_prob": None,
-            "no_speech": False,
-
-            # to be filled in the loop
-            "progress": generation_progress,
-        }
-        while not completed and current_tokens.shape[1] < self.max_text_len: # bos is 3 tokens
-            generation_progress_loop = []
-
-            if new_segment:
-                tokens_for_logits = current_tokens
-            else:
-                # only need to use the last token except in the first forward pass
-                tokens_for_logits = current_tokens[:,-1:]
-
-            logits = self.logits(tokens_for_logits, encoder_feature) # B, len(tokens), token dict size
-            if new_segment:
-                generation["logits_starting"] = Logits(logits[:,:,:])
-
-            if new_segment and self.tokenizer.no_speech is not None:
-                probs_at_sot = logits[:, self.sot_index, :].float().softmax(dim=-1)
-                no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
-                generation["no_speech_prob"] = no_speech_probs[0]
-                if no_speech_probs[0] > self.cfg.nonspeech_prob:
-                    generation["no_speech"] = True
-                    logger.info("no speech, stop")
-                    break
-
-            logits = logits[:, -1, :] # logits for the last token
-            generation_progress_loop.append(("logits_before_suppress",Logits(logits)))
-
-            # supress blank tokens only at the beginning of the segment
-            if new_segment:
-                logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
-            new_segment = False
-            self.suppress_tokens(logits)
-            #generation_progress_loop.append(("logits_after_suppres",BeamLogits(logits[0,:].clone(), self.cfg.beam_size)))
-            generation_progress_loop.append(("logits_after_suppress",Logits(logits)))
-
-            current_tokens, completed = self.token_decoder.update(current_tokens, logits, sum_logprobs)
-            generation_progress_loop.append(("beam_tokens",Tokens(current_tokens[:,-1].clone())))
-            generation_progress_loop.append(("sum_logprobs",sum_logprobs.tolist()))
-            generation_progress_loop.append(("completed",completed))
-
-            logger.debug(f"Decoding completed: {completed}, sum_logprobs: {sum_logprobs.tolist()}, tokens: ")
-            self.debug_print_tokens(current_tokens)
-
-
-            # if self.decoder_type == "beam":
-            #     logger.debug(f"Finished sequences: {self.token_decoder.finished_sequences}")
-
-            #     logprobs = F.log_softmax(logits.float(), dim=-1)
-            #     idx = 0
-            #     logger.debug(f"Beam search topk: {logprobs[idx].topk(self.cfg.beam_size + 1)}")
-            #     logger.debug(f"Greedy search argmax: {logits.argmax(dim=-1)}")
-            # if completed:
-            #     self.debug_print_tokens(current_tokens)
-
-            #     logger.debug("decode stopped because decoder completed")
-
-            attn_of_alignment_heads = [[] for _ in range(self.num_align_heads)]
-            for i, attn_mat in enumerate(self.dec_attns):
-                layer_rank = int(i % len(self.model.decoder.blocks))
-                align_heads_in_layer = self.align_source.get(layer_rank, [])
-                if len(align_heads_in_layer) == 0:
-                    continue
-                for align_head_rank, head_id in align_heads_in_layer:
-                    if self.cfg.beam_size == 1:
-                        a = attn_mat[head_id, :, :]
-                        a = a.unsqueeze(0)
-                    else:
-                        a = attn_mat[:, head_id, :, :]
-                    attn_of_alignment_heads[align_head_rank].append(a)
-            tmp = []
-            for mat in attn_of_alignment_heads:
-                t = torch.cat(mat, dim=1)
-                tmp.append(t) 
-            attn_of_alignment_heads = torch.stack(tmp, dim=1)
-#            logger.debug(str(attn_of_alignment_heads.shape) + " tttady")
-            std, mean = torch.std_mean(attn_of_alignment_heads, dim=-2, keepdim=True, unbiased=False)
-            attn_of_alignment_heads = (attn_of_alignment_heads - mean) / std
-            attn_of_alignment_heads = median_filter(attn_of_alignment_heads, 7) # from whisper.timing
-            attn_of_alignment_heads = attn_of_alignment_heads.mean(dim=1)
-#            logger.debug(str(attn_of_alignment_heads.shape) + " po mean")
-            attn_of_alignment_heads = attn_of_alignment_heads[:,:, :content_mel_len]
-#            logger.debug(str(attn_of_alignment_heads.shape) + " pak ")
-
-            # for each beam, the most attended frame is:
-            most_attended_frames = torch.argmax(attn_of_alignment_heads[:,-1,:], dim=-1)
-            generation_progress_loop.append(("most_attended_frames",most_attended_frames.clone().tolist()))
-            
-            # Calculate absolute timestamps accounting for cumulative offset
-            absolute_timestamps = [(frame * 0.02 + self.cumulative_time_offset) for frame in most_attended_frames.tolist()]
-            generation_progress_loop.append(("absolute_timestamps", absolute_timestamps))
-            
-            logger.debug(str(most_attended_frames.tolist()) + " most att frames")
-            logger.debug(f"Absolute timestamps: {absolute_timestamps} (offset: {self.cumulative_time_offset:.2f}s)")
-
-            most_attended_frame = most_attended_frames[0].item()
-
-
-            generation_progress.append(dict(generation_progress_loop))
-            logger.debug("current tokens" + str(current_tokens.shape))
-            if completed:
-            #    # stripping the last token, the eot
-                current_tokens = current_tokens[:, :-1]
-                break
-            
-            # for some rare cases where the attention fails
-            if not is_last and self.last_attend_frame - most_attended_frame > self.cfg.rewind_threshold:
-                # TODO: check this
-                if current_tokens.shape[1] > 1 and current_tokens[0, -2] >= DEC_PAD:
-                    logger.debug("ommit rewinding from special tokens")
-                    self.last_attend_frame = most_attended_frame
-                else:
-                    logger.debug(
-                        f"[rewind detected] current attention pos: {most_attended_frame}, "
-                        f"last attention pos: {self.last_attend_frame}; omit this segment")
-                    self.last_attend_frame = -self.cfg.rewind_threshold
-                    current_tokens = torch.cat(self.tokens, dim=1) if len(self.tokens) > 0 else self.tokens[0]
-                    break
-            else:
-                self.last_attend_frame = most_attended_frame
-
-            if content_mel_len - most_attended_frame <= (4 if is_last else self.cfg.frame_threshold):
-                logger.debug(f"attention reaches the end: {most_attended_frame}/{content_mel_len}")
-                # stripping the last token, the one that is attended too close to the end
-                current_tokens = current_tokens[:, :-1]
-                break
-        
-            # debug print
-            for i in range(self.cfg.beam_size):
-                logger.debug("attn: {}, current pos: {}, current token: {}({})".format(
-                    attn_of_alignment_heads.shape if attn_of_alignment_heads is not None else None,
-                    most_attended_frames[i], 
-                    current_tokens[i, -1].item(),
-                    self.tokenizer.decode([current_tokens[i, -1].item()])
-                ))
-
-#        for k,v in generation.items():
-#            print(k,v,file=sys.stderr)
-#        for x in generation_progress:
-#            for y in x.items():
-#                print("\t\t",*y,file=sys.stderr)
-#            print("\t","----", file=sys.stderr)
-#        print("\t", "end of generation_progress_loop", file=sys.stderr)
-        #    sys.exit(1)
-        ####################### End of decoding loop
-
-        logger.info("End of decoding loop")
-
-        # if attn_of_alignment_heads is not None:
-        #     seg_len = int(segment.shape[0] / 16000 * TOKENS_PER_SECOND)
-
-        #     # Lets' now consider only the top hypothesis in the beam search
-        #     top_beam_attn_of_alignment_heads = attn_of_alignment_heads[0]
-
-        #     # debug print: how is the new token attended?
-        #     new_token_attn = top_beam_attn_of_alignment_heads[token_len_before_decoding:, -seg_len:]
-        #     logger.debug(f"New token attention shape: {new_token_attn.shape}")
-        #     if new_token_attn.shape[0] == 0:  # it's not attended in the current audio segment
-        #         logger.debug("no token generated")
-        #     else:  # it is, and the max attention is:
-        #         new_token_max_attn, _ = new_token_attn.max(dim=-1)
-        #         logger.debug(f"segment max attention: {new_token_max_attn.mean().item()/len(self.segments)}")
-
-
-        # let's now operate only with the top beam hypothesis
-        tokens_to_split = current_tokens[0, token_len_before_decoding:]
-        if fire_detected or is_last:
-            new_hypothesis = tokens_to_split.flatten().tolist()
-        else:
-            # going to truncate the tokens after the last space
-            split_words, split_tokens = self.tokenizer.split_to_word_tokens(tokens_to_split.tolist())
-            generation["result"] = {"split_words": split_words[:-1], "split_tokens": split_tokens[:-1]}
-            generation["result_truncated"] = {"split_words": split_words[-1:], "split_tokens": split_tokens[-1:]}
-
-#            text_to_split = self.tokenizer.decode(tokens_to_split)
-#            logger.debug(f"text_to_split: {text_to_split}")
-#            logger.debug("text at current step: {}".format(text_to_split.replace(" ", "<space>")))
-#            text_before_space = " ".join(text_to_split.split(" ")[:-1])
-#            logger.debug("before the last space: {}".format(text_before_space.replace(" ", "<space>")))
-            if len(split_words) > 1:
-                new_hypothesis = [i for sublist in split_tokens[:-1] for i in sublist]  
-            else:
-                new_hypothesis = []
-
-
-        ### new hypothesis
-        logger.debug(f"new_hypothesis: {new_hypothesis}")
-        new_tokens = torch.tensor([new_hypothesis], dtype=torch.long).repeat_interleave(self.cfg.beam_size, dim=0).to(
-            device=self.model.device,
-        )
-        self.tokens.append(new_tokens)
-        # TODO: test if this is redundant or not
-#        ret = ret[ret<DEC_PAD]
-
-        logger.info(f"Output: {self.tokenizer.decode(new_hypothesis)}")
-        
-        self._clean_cache()
-
-        return new_hypothesis, generation
+        return super().infer(is_last)

whisperlivekit/simul_whisper/token_buffer.py

@@ -1,5 +1,8 @@
-import torch
 import sys
+
+import torch
+
+
 class TokenBuffer:
 
     def __init__(self, text="", tokenizer=None, device=None, prefix_token_ids=[]):
@@ -7,6 +10,7 @@ class TokenBuffer:
         self.prefix_token_ids = prefix_token_ids
         self.tokenizer = tokenizer
         self.device = device
+        self.pending_token_ids = []
 
     def as_token_ids(self, tokenizer=None):
 
@@ -64,7 +68,26 @@ class TokenBuffer:
     def append_token_ids(self, token_ids):
         tokenizer = self.tokenizer
         assert tokenizer is not None, "Tokenizer is not set."
-        self.text += self.tokenizer.decode(token_ids)
+
+        all_tokens = self.pending_token_ids + token_ids
+
+        decoded = tokenizer.decode(all_tokens)
+        replacement_char = "\ufffd"
+
+        if replacement_char in decoded:
+            if len(all_tokens) > 1:
+                decoded_partial = tokenizer.decode(all_tokens[:-1])
+
+                if replacement_char not in decoded_partial:
+                    self.text += decoded_partial
+                    self.pending_token_ids = [all_tokens[-1]]
+                else:
+                    self.pending_token_ids = all_tokens
+            else:
+                self.pending_token_ids = all_tokens
+        else:
+            self.text += decoded
+            self.pending_token_ids = []
 
     def as_split_word_tokens(self):
         tokenizer = self.tokenizer

whisperlivekit/simul_whisper/whisper/__init__.py

@@ -1,168 +0,0 @@
-import hashlib
-import io
-import os
-import urllib
-import warnings
-from typing import List, Optional, Union
-
-import torch
-from tqdm import tqdm
-
-from .audio import load_audio, log_mel_spectrogram, pad_or_trim
-from .decoding import DecodingOptions, DecodingResult, decode, detect_language
-from .model import ModelDimensions, Whisper
-from .transcribe import transcribe
-from .version import __version__
-
-_MODELS = {
-    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
-    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
-    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
-    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
-    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
-    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
-    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
-    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
-    "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
-    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
-    "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
-    "large": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
-    "large-v3-turbo": "https://openaipublic.azureedge.net/main/whisper/models/aff26ae408abcba5fbf8813c21e62b0941638c5f6eebfb145be0c9839262a19a/large-v3-turbo.pt",
-    "turbo": "https://openaipublic.azureedge.net/main/whisper/models/aff26ae408abcba5fbf8813c21e62b0941638c5f6eebfb145be0c9839262a19a/large-v3-turbo.pt",
-}
-
-# base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are
-# highly correlated to the word-level timing, i.e. the alignment between audio and text tokens.
-_ALIGNMENT_HEADS = {
-    "tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00",
-    "tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO",
-    "base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00",
-    "base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-<FaQ7m",
-    "small.en": b"ABzY8>?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00",
-    "small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P<N0000",
-    "medium.en": b"ABzY8usPae0{>%R7<zz_OvQ{)4kMa0BMw6u5rT}kRKX;$NfYBv00*Hl@qhsU00",
-    "medium": b"ABzY8B0Jh+0{>%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9",
-    "large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR<kSfC2yj",
-    "large-v2": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj",
-    "large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
-    "large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
-    "large-v3-turbo": b"ABzY8j^C+e0{>%RARaKHP%t(lGR*)0g!tONPyhe`",
-    "turbo": b"ABzY8j^C+e0{>%RARaKHP%t(lGR*)0g!tONPyhe`",
-}
-
-
-def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
-    os.makedirs(root, exist_ok=True)
-
-    expected_sha256 = url.split("/")[-2]
-    download_target = os.path.join(root, os.path.basename(url))
-
-    if os.path.exists(download_target) and not os.path.isfile(download_target):
-        raise RuntimeError(f"{download_target} exists and is not a regular file")
-
-    if os.path.isfile(download_target):
-        with open(download_target, "rb") as f:
-            model_bytes = f.read()
-        if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
-            return model_bytes if in_memory else download_target
-        else:
-            warnings.warn(
-                f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
-            )
-
-    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
-        with tqdm(
-            total=int(source.info().get("Content-Length")),
-            ncols=80,
-            unit="iB",
-            unit_scale=True,
-            unit_divisor=1024,
-        ) as loop:
-            while True:
-                buffer = source.read(8192)
-                if not buffer:
-                    break
-
-                output.write(buffer)
-                loop.update(len(buffer))
-
-    model_bytes = open(download_target, "rb").read()
-    if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
-        raise RuntimeError(
-            "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
-        )
-
-    return model_bytes if in_memory else download_target
-
-
-def available_models() -> List[str]:
-    """Returns the names of available models"""
-    return list(_MODELS.keys())
-
-
-def load_model(
-    name: str,
-    device: Optional[Union[str, torch.device]] = None,
-    download_root: str = None,
-    in_memory: bool = False,
-    decoder_only=False
-) -> Whisper:
-    """
-    Load a Whisper ASR model
-
-    Parameters
-    ----------
-    name : str
-        one of the official model names listed by `whisper.available_models()`, or
-        path to a model checkpoint containing the model dimensions and the model state_dict.
-    device : Union[str, torch.device]
-        the PyTorch device to put the model into
-    download_root: str
-        path to download the model files; by default, it uses "~/.cache/whisper"
-    in_memory: bool
-        whether to preload the model weights into host memory
-
-    Returns
-    -------
-    model : Whisper
-        The Whisper ASR model instance
-    """
-
-    if device is None:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if download_root is None:
-        default = os.path.join(os.path.expanduser("~"), ".cache")
-        download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper")
-
-    if name in _MODELS:
-        checkpoint_file = _download(_MODELS[name], download_root, in_memory)
-        alignment_heads = _ALIGNMENT_HEADS[name]
-    elif os.path.isfile(name):
-        checkpoint_file = open(name, "rb").read() if in_memory else name
-        alignment_heads = None
-    else:
-        raise RuntimeError(
-            f"Model {name} not found; available models = {available_models()}"
-        )
-
-    with (
-        io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")
-    ) as fp:
-        checkpoint = torch.load(fp, map_location=device)
-    del checkpoint_file
-
-    dims = ModelDimensions(**checkpoint["dims"])
-    model = Whisper(dims, decoder_only=decoder_only)
-    
-    if decoder_only:
-        checkpoint["model_state_dict"] = {
-            k: v for k, v in checkpoint["model_state_dict"].items() 
-            if 'encoder' not in k
-        }
-
-    model.load_state_dict(checkpoint["model_state_dict"])
-
-    if alignment_heads is not None:
-        model.set_alignment_heads(alignment_heads)
-
-    return model.to(device)

whisperlivekit/thread_safety.py

@@ -0,0 +1,139 @@
+"""
+Thread Safety Configuration for WhisperLiveKit
+
+This module provides thread safety configuration and utilities.
+
+Environment Variables:
+    WHISPERLIVEKIT_MODEL_LOCK: Enable/disable model locking (default: 1)
+        Set to "0" to disable for single-connection deployments
+
+    WHISPERLIVEKIT_LOCK_TIMEOUT: Lock acquisition timeout in seconds (default: 30)
+
+Usage:
+    # Enable model locking (default)
+    export WHISPERLIVEKIT_MODEL_LOCK=1
+
+    # Disable for single-connection deployment
+    export WHISPERLIVEKIT_MODEL_LOCK=0
+
+    # Custom timeout
+    export WHISPERLIVEKIT_LOCK_TIMEOUT=60
+"""
+
+import os
+import logging
+import threading
+
+logger = logging.getLogger(__name__)
+
+# Configuration
+USE_MODEL_LOCK = os.environ.get("WHISPERLIVEKIT_MODEL_LOCK", "1") == "1"
+LOCK_TIMEOUT = float(os.environ.get("WHISPERLIVEKIT_LOCK_TIMEOUT", "30.0"))
+
+# Global model lock
+_model_lock = threading.Lock()
+
+# Log configuration on import
+if USE_MODEL_LOCK:
+    logger.info(f"Model locking ENABLED (timeout: {LOCK_TIMEOUT}s)")
+    logger.info("For single-connection deployments, set WHISPERLIVEKIT_MODEL_LOCK=0")
+else:
+    logger.warning("Model locking DISABLED - only safe for single-connection deployments")
+
+
+def get_model_lock():
+    """Get the global model lock instance"""
+    return _model_lock
+
+
+def acquire_model_lock(timeout=None):
+    """
+    Acquire model lock with timeout.
+
+    Args:
+        timeout: Lock acquisition timeout (default: use LOCK_TIMEOUT)
+
+    Returns:
+        bool: True if lock acquired, False on timeout
+    """
+    if not USE_MODEL_LOCK:
+        return True
+
+    timeout = timeout or LOCK_TIMEOUT
+    acquired = _model_lock.acquire(timeout=timeout)
+
+    if not acquired:
+        logger.error(f"Failed to acquire model lock within {timeout}s")
+
+    return acquired
+
+
+def release_model_lock():
+    """Release model lock"""
+    if not USE_MODEL_LOCK:
+        return
+
+    try:
+        _model_lock.release()
+    except RuntimeError:
+        # Lock not held - this is fine
+        pass
+
+
+class ModelLockContext:
+    """Context manager for model lock"""
+
+    def __init__(self, timeout=None):
+        self.timeout = timeout
+        self.acquired = False
+
+    def __enter__(self):
+        self.acquired = acquire_model_lock(self.timeout)
+        return self.acquired
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.acquired:
+            release_model_lock()
+        return False
+
+
+# Concurrency recommendations
+RECOMMENDED_CONNECTIONS_PER_WORKER = 1 if USE_MODEL_LOCK else 1
+RECOMMENDED_WORKERS = 4
+
+def print_deployment_recommendations():
+    """Print recommended deployment configuration"""
+    print("\n" + "="*60)
+    print("WhisperLiveKit Deployment Recommendations")
+    print("="*60)
+
+    if USE_MODEL_LOCK:
+        print("⚠️  Model locking is ENABLED")
+        print("   This serializes inference across connections.")
+        print()
+        print("Recommended deployment:")
+        print(f"  gunicorn -w {RECOMMENDED_WORKERS} \\")
+        print("    -k uvicorn.workers.UvicornWorker \\")
+        print("    --worker-connections 1 \\")
+        print("    whisperlivekit.basic_server:app")
+        print()
+        print("Expected capacity:")
+        print(f"  - {RECOMMENDED_WORKERS} concurrent users (1 per worker)")
+        print(f"  - Memory: ~{RECOMMENDED_WORKERS}x model size")
+    else:
+        print("✅ Model locking is DISABLED")
+        print("   ⚠️  ONLY safe for single-connection deployments")
+        print()
+        print("Recommended deployment:")
+        print("  uvicorn whisperlivekit.basic_server:app \\")
+        print("    --host 0.0.0.0 --port 8000 \\")
+        print("    --workers 1")
+        print()
+        print("Expected capacity:")
+        print("  - 1 concurrent user only")
+
+    print("="*60 + "\n")
+
+
+if __name__ == "__main__":
+    print_deployment_recommendations()

whisperlivekit/timed_objects.py

@@ -1,20 +1,53 @@
-from dataclasses import dataclass
-from typing import Optional
+from dataclasses import dataclass, field
+from datetime import timedelta
+from typing import Any, Dict, List, Optional, Union
+
+PUNCTUATION_MARKS = {'.', '!', '?', '。', '！', '？'}
+
+def format_time(seconds: float) -> str:
+    """Format seconds as HH:MM:SS."""
+    return str(timedelta(seconds=int(seconds)))
 
 @dataclass
-class TimedText:
-    start: Optional[float]
-    end: Optional[float]
+class Timed:
+    start: Optional[float] = 0
+    end: Optional[float] = 0
+
+@dataclass
+class TimedText(Timed):
     text: Optional[str] = ''
     speaker: Optional[int] = -1
-    probability: Optional[float] = None
-    is_dummy: Optional[bool] = False
+    detected_language: Optional[str] = None
+    
+    def has_punctuation(self) -> bool:
+        return any(char in PUNCTUATION_MARKS for char in self.text.strip())
+    
+    def is_within(self, other: 'TimedText') -> bool:
+        return other.contains_timespan(self)
 
-@dataclass
+    def duration(self) -> float:
+        return self.end - self.start
+
+    def contains_timespan(self, other: 'TimedText') -> bool:
+        return self.start <= other.start and self.end >= other.end
+    
+    def __bool__(self) -> bool:
+        return bool(self.text)
+    
+    def __str__(self) -> str:
+        return str(self.text)
+
+@dataclass()
 class ASRToken(TimedText):
+    probability: Optional[float] = None
+
     def with_offset(self, offset: float) -> "ASRToken":
         """Return a new token with the time offset added."""
-        return ASRToken(self.start + offset, self.end + offset, self.text, self.speaker, self.probability)
+        return ASRToken(self.start + offset, self.end + offset, self.text, self.speaker, detected_language=self.detected_language, probability=self.probability)
+
+    def is_silence(self) -> bool:
+        return False
+
 
 @dataclass
 class Sentence(TimedText):
@@ -22,15 +55,176 @@ class Sentence(TimedText):
 
 @dataclass
 class Transcript(TimedText):
-    pass
+    """
+    represents a concatenation of several ASRToken
+    """
+
+    @classmethod
+    def from_tokens(
+        cls,
+        tokens: List[ASRToken],
+        sep: Optional[str] = None,
+        offset: float = 0
+    ) -> "Transcript":
+        """Collapse multiple ASR tokens into a single transcript span."""
+        sep = sep if sep is not None else ' '
+        text = sep.join(token.text for token in tokens)
+        if tokens:
+            start = offset + tokens[0].start
+            end = offset + tokens[-1].end
+        else:
+            start = None
+            end = None
+        return cls(start, end, text)
+
 
 @dataclass
-class SpeakerSegment(TimedText):
+class SpeakerSegment(Timed):
     """Represents a segment of audio attributed to a specific speaker.
     No text nor probability is associated with this segment.
     """
+    speaker: Optional[int] = -1
+    pass
+
+@dataclass
+class Translation(TimedText):
     pass
 
 @dataclass
 class Silence():
-    duration: float
+    start: Optional[float] = None
+    end: Optional[float] = None
+    duration: Optional[float] = None
+    is_starting: bool = False
+    has_ended: bool = False
+
+    def compute_duration(self) -> Optional[float]:
+        if self.start is None or self.end is None:
+            return None
+        self.duration = self.end - self.start
+        return self.duration
+    
+    def is_silence(self) -> bool:
+        return True
+
+
+@dataclass
+class Segment(TimedText):
+    """Generic contiguous span built from tokens or silence markers."""
+    start: Optional[float]
+    end: Optional[float]
+    text: Optional[str]
+    speaker: Optional[str]
+    tokens: Optional[ASRToken] = None
+    translation: Optional[Translation] = None
+
+    @classmethod
+    def from_tokens(
+        cls,
+        tokens: List[Union[ASRToken, Silence]],
+        is_silence: bool = False
+    ) -> Optional["Segment"]:
+        """Return a normalized segment representing the provided tokens."""
+        if not tokens:
+            return None
+        
+        start_token = tokens[0]
+        end_token = tokens[-1]        
+        if is_silence:
+            return cls(
+                start=start_token.start,
+                end=end_token.end,
+                text=None,
+                speaker=-2
+            )
+        else:
+            return cls(
+                start=start_token.start,
+                end=end_token.end,
+                text=''.join(token.text for token in tokens),
+                speaker=-1,
+                detected_language=start_token.detected_language
+            )
+
+    def is_silence(self) -> bool:
+        """True when this segment represents a silence gap."""
+        return self.speaker == -2
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Serialize the segment for frontend consumption."""
+        _dict: Dict[str, Any] = {
+            'speaker': int(self.speaker) if self.speaker != -1 else 1,
+            'text': self.text,
+            'start': format_time(self.start),
+            'end': format_time(self.end),
+        }
+        if self.translation:
+            _dict['translation'] = self.translation
+        if self.detected_language:
+            _dict['detected_language'] = self.detected_language
+        return _dict
+
+
+@dataclass
+class PuncSegment(Segment):
+    pass
+
+class SilentSegment(Segment):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+        self.speaker = -2
+        self.text = ''
+
+
+@dataclass  
+class FrontData():
+    status: str = ''
+    error: str = ''
+    lines: list[Segment] = field(default_factory=list)
+    buffer_transcription: str = ''
+    buffer_diarization: str = ''
+    buffer_translation: str = ''
+    remaining_time_transcription: float = 0.
+    remaining_time_diarization: float = 0.
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Serialize the front-end data payload."""
+        _dict: Dict[str, Any] = {
+            'status': self.status,
+            'lines': [line.to_dict() for line in self.lines if (line.text or line.speaker == -2)],
+            'buffer_transcription': self.buffer_transcription,
+            'buffer_diarization': self.buffer_diarization,
+            'buffer_translation': self.buffer_translation,
+            'remaining_time_transcription': self.remaining_time_transcription,
+            'remaining_time_diarization': self.remaining_time_diarization,
+        }
+        if self.error:
+            _dict['error'] = self.error
+        return _dict
+
+@dataclass  
+class ChangeSpeaker:
+    speaker: int
+    start: int
+
+@dataclass  
+class State():
+    """Unified state class for audio processing.
+    
+    Contains both persistent state (tokens, buffers) and temporary update buffers
+    (new_* fields) that are consumed by TokensAlignment.
+    """
+    # Persistent state
+    tokens: List[ASRToken] = field(default_factory=list)
+    buffer_transcription: Transcript = field(default_factory=Transcript)
+    end_buffer: float = 0.0
+    end_attributed_speaker: float = 0.0
+    remaining_time_transcription: float = 0.0
+    remaining_time_diarization: float = 0.0
+    
+    # Temporary update buffers (consumed by TokensAlignment.update())
+    new_tokens: List[Union[ASRToken, Silence]] = field(default_factory=list)
+    new_translation: List[Any] = field(default_factory=list)
+    new_diarization: List[Any] = field(default_factory=list)
+    new_tokens_buffer: List[Any] = field(default_factory=list)  # only when local agreement
+    new_translation_buffer= TimedText()

whisperlivekit/tokens_alignment.py

@@ -0,0 +1,220 @@
+from time import time
+from typing import Any, List, Optional, Tuple, Union
+
+from whisperlivekit.timed_objects import (ASRToken, Segment, PuncSegment, Silence,
+                                          SilentSegment, SpeakerSegment,
+                                          TimedText)
+
+
+class TokensAlignment:
+
+    def __init__(self, state: Any, args: Any, sep: Optional[str]) -> None:
+        self.state = state
+        self.diarization = args.diarization
+        self._tokens_index: int = 0
+        self._diarization_index: int = 0
+        self._translation_index: int = 0
+
+        self.all_tokens: List[ASRToken] = []
+        self.all_diarization_segments: List[SpeakerSegment] = []
+        self.all_translation_segments: List[Any] = []
+
+        self.new_tokens: List[ASRToken] = []
+        self.new_diarization: List[SpeakerSegment] = []
+        self.new_translation: List[Any] = []
+        self.new_translation_buffer: Union[TimedText, str] = TimedText()
+        self.new_tokens_buffer: List[Any] = []
+        self.sep: str = sep if sep is not None else ' '
+        self.beg_loop: Optional[float] = None
+
+        self.validated_segments: List[Segment] = []
+        self.current_line_tokens: List[ASRToken] = []
+        self.diarization_buffer: List[ASRToken] = []
+
+        self.last_punctuation = None
+        self.last_uncompleted_punc_segment: PuncSegment = None
+        self.unvalidated_tokens: PuncSegment = []
+
+    def update(self) -> None:
+        """Drain state buffers into the running alignment context."""
+        self.new_tokens, self.state.new_tokens = self.state.new_tokens, []
+        self.new_diarization, self.state.new_diarization = self.state.new_diarization, []
+        self.new_translation, self.state.new_translation = self.state.new_translation, []
+        self.new_tokens_buffer, self.state.new_tokens_buffer = self.state.new_tokens_buffer, []
+
+        self.all_tokens.extend(self.new_tokens)
+        self.all_diarization_segments.extend(self.new_diarization)
+        self.all_translation_segments.extend(self.new_translation)
+        self.new_translation_buffer = self.state.new_translation_buffer
+
+    def add_translation(self, segment: Segment) -> None:
+        """Append translated text segments that overlap with a segment."""
+        if segment.translation is None:
+            segment.translation = ''
+        for ts in self.all_translation_segments:
+            if ts.is_within(segment):
+                if ts.text:
+                    segment.translation += ts.text + self.sep
+            elif segment.translation:
+                break
+
+
+    def compute_punctuations_segments(self, tokens: Optional[List[ASRToken]] = None) -> List[PuncSegment]:
+        """Group tokens into segments split by punctuation and explicit silence."""
+        segments = []
+        segment_start_idx = 0
+        for i, token in enumerate(self.all_tokens):
+            if token.is_silence():
+                previous_segment = PuncSegment.from_tokens(
+                        tokens=self.all_tokens[segment_start_idx: i],
+                    )
+                if previous_segment:
+                    segments.append(previous_segment)
+                segment = PuncSegment.from_tokens(
+                    tokens=[token],
+                    is_silence=True
+                )
+                segments.append(segment)
+                segment_start_idx = i+1
+            else:
+                if token.has_punctuation():
+                    segment = PuncSegment.from_tokens(
+                        tokens=self.all_tokens[segment_start_idx: i+1],
+                    )
+                    segments.append(segment)
+                    segment_start_idx = i+1
+
+        final_segment = PuncSegment.from_tokens(
+            tokens=self.all_tokens[segment_start_idx:],
+        )
+        if final_segment:
+            segments.append(final_segment)
+        return segments
+
+    def compute_new_punctuations_segments(self) -> List[PuncSegment]:
+        new_punc_segments = []
+        segment_start_idx = 0
+        self.unvalidated_tokens += self.new_tokens
+        for i, token in enumerate(self.unvalidated_tokens):
+            if token.is_silence():
+                previous_segment = PuncSegment.from_tokens(
+                        tokens=self.unvalidated_tokens[segment_start_idx: i],
+                    )
+                if previous_segment:
+                    new_punc_segments.append(previous_segment)
+                segment = PuncSegment.from_tokens(
+                    tokens=[token],
+                    is_silence=True
+                )
+                new_punc_segments.append(segment)
+                segment_start_idx = i+1
+            else:
+                if token.has_punctuation():
+                    segment = PuncSegment.from_tokens(
+                        tokens=self.unvalidated_tokens[segment_start_idx: i+1],
+                    )
+                    new_punc_segments.append(segment)
+                    segment_start_idx = i+1
+
+        self.unvalidated_tokens = self.unvalidated_tokens[segment_start_idx:]
+        return new_punc_segments
+
+
+    def concatenate_diar_segments(self) -> List[SpeakerSegment]:
+        """Merge consecutive diarization slices that share the same speaker."""
+        if not self.all_diarization_segments:
+            return []
+        merged = [self.all_diarization_segments[0]]
+        for segment in self.all_diarization_segments[1:]:
+            if segment.speaker == merged[-1].speaker:
+                merged[-1].end = segment.end
+            else:
+                merged.append(segment)
+        return merged
+
+
+    @staticmethod
+    def intersection_duration(seg1: TimedText, seg2: TimedText) -> float:
+        """Return the overlap duration between two timed segments."""
+        start = max(seg1.start, seg2.start)
+        end = min(seg1.end, seg2.end)
+
+        return max(0, end - start)
+
+    def get_lines_diarization(self) -> Tuple[List[Segment], str]:
+        """Build segments when diarization is enabled and track overflow buffer."""
+        diarization_buffer = ''
+        punctuation_segments = self.compute_punctuations_segments()
+        diarization_segments = self.concatenate_diar_segments()
+        for punctuation_segment in punctuation_segments:
+            if not punctuation_segment.is_silence():
+                if diarization_segments and punctuation_segment.start >= diarization_segments[-1].end:
+                    diarization_buffer += punctuation_segment.text
+                else:
+                    max_overlap = 0.0
+                    max_overlap_speaker = 1
+                    for diarization_segment in diarization_segments:
+                        intersec = self.intersection_duration(punctuation_segment, diarization_segment)
+                        if intersec > max_overlap:
+                            max_overlap = intersec
+                            max_overlap_speaker = diarization_segment.speaker + 1
+                    punctuation_segment.speaker = max_overlap_speaker
+        
+        segments = []
+        if punctuation_segments:
+            segments = [punctuation_segments[0]]
+            for segment in punctuation_segments[1:]:
+                if segment.speaker == segments[-1].speaker:
+                    if segments[-1].text:
+                        segments[-1].text += segment.text
+                    segments[-1].end = segment.end
+                else:
+                    segments.append(segment)
+
+        return segments, diarization_buffer
+
+
+    def get_lines(
+            self, 
+            diarization: bool = False,
+            translation: bool = False,
+            current_silence: Optional[Silence] = None
+        ) -> Tuple[List[Segment], str, Union[str, TimedText]]:
+        """Return the formatted segments plus buffers, optionally with diarization/translation."""
+        if diarization:
+            segments, diarization_buffer = self.get_lines_diarization()
+        else:
+            diarization_buffer = ''
+            for token in self.new_tokens:
+                if isinstance(token, Silence):
+                    if self.current_line_tokens:
+                        self.validated_segments.append(Segment.from_tokens(self.current_line_tokens))
+                        self.current_line_tokens = []
+
+                    end_silence = token.end if token.has_ended else time() - self.beg_loop
+                    if self.validated_segments and self.validated_segments[-1].is_silence():
+                        self.validated_segments[-1].end = end_silence
+                    else:
+                        self.validated_segments.append(SilentSegment(
+                            start=token.start,
+                            end=end_silence
+                        ))
+                else:
+                    self.current_line_tokens.append(token)
+            
+            segments = list(self.validated_segments)
+            if self.current_line_tokens:
+                segments.append(Segment.from_tokens(self.current_line_tokens))
+
+        if current_silence:
+            end_silence = current_silence.end if current_silence.has_ended else time() - self.beg_loop
+            if segments and segments[-1].is_silence():
+                segments[-1] = SilentSegment(start=segments[-1].start, end=end_silence)
+            else:
+                segments.append(SilentSegment(
+                    start=current_silence.start,
+                    end=end_silence
+                ))
+        if translation:
+            [self.add_translation(segment) for segment in segments if not segment.is_silence()]
+        return segments, diarization_buffer, self.new_translation_buffer.text

whisperlivekit/trail_repetition.py

@@ -1,60 +0,0 @@
-from typing import Sequence, Callable, Any, Optional, Dict
-
-def _detect_tail_repetition(
-    seq: Sequence[Any],
-    key: Callable[[Any], Any] = lambda x: x,  # extract comparable value
-    min_block: int = 1,                       # set to 2 to ignore 1-token loops like "."
-    max_tail: int = 300,                      # search window from the end for speed
-    prefer: str = "longest",                  # "longest" coverage or "smallest" block
-) -> Optional[Dict]:
-    vals = [key(x) for x in seq][-max_tail:]
-    n = len(vals)
-    best = None
-
-    # try every possible block length
-    for b in range(min_block, n // 2 + 1):
-        block = vals[-b:]
-        # count how many times this block repeats contiguously at the very end
-        count, i = 0, n
-        while i - b >= 0 and vals[i - b:i] == block:
-            count += 1
-            i -= b
-
-        if count >= 2:
-            cand = {
-                "block_size": b,
-                "count": count,
-                "start_index": len(seq) - count * b,  # in original seq
-                "end_index": len(seq),
-            }
-            if (best is None or
-                (prefer == "longest" and count * b > best["count"] * best["block_size"]) or
-                (prefer == "smallest" and b < best["block_size"])):
-                best = cand
-    return best
-
-def trim_tail_repetition(
-    seq: Sequence[Any],
-    key: Callable[[Any], Any] = lambda x: x,
-    min_block: int = 1,
-    max_tail: int = 300,
-    prefer: str = "longest",
-    keep: int = 1,  # how many copies of the repeating block to keep at the end (0 or 1 are common)
-):
-    """
-    Returns a new sequence with repeated tail trimmed.
-    keep=1 -> keep a single copy of the repeated block.
-    keep=0 -> remove all copies of the repeated block.
-    """
-    rep = _detect_tail_repetition(seq, key, min_block, max_tail, prefer)
-    if not rep:
-        return seq, False  # nothing to trim
-
-    b, c = rep["block_size"], rep["count"]
-    if keep < 0:
-        keep = 0
-    if keep >= c:
-        return seq, False  # nothing to trim (already <= keep copies)
-    # new length = total - (copies_to_remove * block_size)
-    new_len = len(seq) - (c - keep) * b
-    return seq[:new_len], True

whisperlivekit/voxtral_hf_streaming.py

@@ -0,0 +1,395 @@
+"""
+Voxtral Mini Realtime streaming backend using HuggingFace Transformers.
+
+Uses VoxtralRealtimeForConditionalGeneration with a background generate thread
+and queue-based audio feeding for real-time streaming transcription.
+Supports CUDA, CPU, and MPS devices.
+"""
+
+import logging
+import queue
+import sys
+import threading
+import time
+from typing import List, Optional, Tuple
+
+import numpy as np
+
+from whisperlivekit.timed_objects import ASRToken, Transcript
+
+logger = logging.getLogger(__name__)
+
+
+class VoxtralHFStreamingASR:
+    """Voxtral model holder using HuggingFace Transformers."""
+
+    sep = " "
+
+    def __init__(self, logfile=sys.stderr, **kwargs):
+        import torch
+        from transformers import (
+            AutoProcessor,
+            VoxtralRealtimeForConditionalGeneration,
+        )
+
+        self.logfile = logfile
+        self.transcribe_kargs = {}
+
+        lan = kwargs.get("lan", "auto")
+        self.original_language = None if lan == "auto" else lan
+
+        DEFAULT_MODEL = "mistralai/Voxtral-Mini-4B-Realtime-2602"
+        model_path = kwargs.get("model_dir") or kwargs.get("model_path")
+        if not model_path:
+            model_size = kwargs.get("model_size", "")
+            if model_size and ("/" in model_size or model_size.startswith(".")):
+                model_path = model_size
+            else:
+                model_path = DEFAULT_MODEL
+
+        t = time.time()
+        logger.info(f"Loading Voxtral model '{model_path}' via HF Transformers...")
+        self.processor = AutoProcessor.from_pretrained(model_path)
+        self.model = VoxtralRealtimeForConditionalGeneration.from_pretrained(
+            model_path,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+        )
+        logger.info(f"Voxtral HF model loaded in {time.time() - t:.2f}s on {self.model.device}")
+
+        self.backend_choice = "voxtral"
+        self.tokenizer = None  # sentence tokenizer — not needed for streaming
+
+    def transcribe(self, audio):
+        pass
+
+
+class VoxtralHFStreamingOnlineProcessor:
+    """
+    Online processor for Voxtral streaming ASR via HuggingFace Transformers.
+
+    Uses a background thread running model.generate() with a queue-based
+    input_features_generator and TextIteratorStreamer for real-time output.
+    Each decoded token corresponds to ~80ms of audio.
+    """
+
+    SAMPLING_RATE = 16000
+
+    def __init__(self, asr: VoxtralHFStreamingASR, logfile=sys.stderr):
+        self.asr = asr
+        self.logfile = logfile
+        self.end = 0.0
+        self.buffer = []
+        self.audio_buffer = np.array([], dtype=np.float32)
+
+        processor = asr.processor
+        self._first_chunk_samples = processor.num_samples_first_audio_chunk
+        self._chunk_samples = processor.num_samples_per_audio_chunk
+        self._chunk_step = processor.raw_audio_length_per_tok
+        n_right_pad = processor.num_right_pad_tokens
+        if callable(n_right_pad):
+            n_right_pad = n_right_pad()
+        self._right_pad_samples = int(n_right_pad * processor.raw_audio_length_per_tok)
+        self._seconds_per_token = processor.raw_audio_length_per_tok / self.SAMPLING_RATE
+
+        self._reset_state()
+
+        logger.info(
+            f"[voxtral-hf] Initialized. first_chunk={self._first_chunk_samples} samples, "
+            f"chunk={self._chunk_samples}, step={self._chunk_step}, "
+            f"right_pad={self._right_pad_samples}"
+        )
+
+    def _reset_state(self):
+        self._pending_audio = np.zeros(0, dtype=np.float32)
+        self._audio_queue: queue.Queue = queue.Queue()
+        self._streamer_texts: List[str] = []
+        self._generate_thread: Optional[threading.Thread] = None
+        self._generate_started = False
+        self._generate_finished = False
+        self._generate_error: Optional[Exception] = None
+
+        # Text accumulation and word extraction
+        self._accumulated_text = ""
+        self._n_text_tokens_received = 0
+        self._n_committed_words = 0
+        self._global_time_offset = 0.0
+
+        # Lock for text state accessed from both generate thread and main thread
+        self._text_lock = threading.Lock()
+
+    # ── Interface methods ──
+
+    def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time: float):
+        self.end = audio_stream_end_time
+        self._pending_audio = np.append(self._pending_audio, audio)
+        self.audio_buffer = self._pending_audio
+
+    def process_iter(self, is_last=False) -> Tuple[List[ASRToken], float]:
+        try:
+            return self._process_iter_inner(is_last)
+        except Exception as e:
+            logger.warning(f"[voxtral-hf] process_iter exception: {e}", exc_info=True)
+            return [], self.end
+
+    def get_buffer(self) -> Transcript:
+        """Return all uncommitted text as buffer."""
+        with self._text_lock:
+            text = self._accumulated_text
+        if not text:
+            return Transcript(start=None, end=None, text="")
+
+        words = text.split()
+        uncommitted = words[self._n_committed_words:]
+        if uncommitted:
+            return Transcript(start=self.end, end=self.end, text=" ".join(uncommitted))
+        return Transcript(start=None, end=None, text="")
+
+    def start_silence(self) -> Tuple[List[ASRToken], float]:
+        """Flush all uncommitted words when silence starts."""
+        self._drain_streamer()
+        words = self._flush_all_pending_words()
+        logger.info(f"[voxtral-hf] start_silence: flushed {len(words)} words")
+        return words, self.end
+
+    def end_silence(self, silence_duration: float, offset: float):
+        self._global_time_offset += silence_duration
+        self.end += silence_duration
+
+    def new_speaker(self, change_speaker):
+        self.start_silence()
+
+    def warmup(self, audio, init_prompt=""):
+        pass
+
+    def finish(self) -> Tuple[List[ASRToken], float]:
+        """Flush remaining audio with right-padding and stop the generate thread."""
+        # Add right-padding so the model can finish decoding
+        if self._right_pad_samples > 0:
+            right_pad = np.zeros(self._right_pad_samples, dtype=np.float32)
+            self._pending_audio = np.append(self._pending_audio, right_pad)
+
+        # Feed remaining audio
+        if self._generate_started and not self._generate_finished:
+            self._feed_pending_audio()
+            # Signal end of audio
+            self._audio_queue.put(None)
+            # Wait for generate to finish
+            if self._generate_thread is not None:
+                self._generate_thread.join(timeout=30.0)
+        elif not self._generate_started and len(self._pending_audio) >= self._first_chunk_samples:
+            # Never started but have enough audio — start and immediately finish
+            self._start_generate_thread()
+            self._feed_pending_audio()
+            self._audio_queue.put(None)
+            if self._generate_thread is not None:
+                self._generate_thread.join(timeout=30.0)
+
+        self._drain_streamer()
+        words = self._flush_all_pending_words()
+        logger.info(f"[voxtral-hf] finish: flushed {len(words)} words")
+        return words, self.end
+
+    # ── Generate thread management ──
+
+    def _start_generate_thread(self):
+        """Start model.generate() in a background thread with streaming."""
+        import torch
+        from transformers import TextIteratorStreamer
+
+        processor = self.asr.processor
+        model = self.asr.model
+
+        # Extract first chunk
+        first_chunk_audio = self._pending_audio[:self._first_chunk_samples]
+        self._pending_audio = self._pending_audio[self._first_chunk_samples:]
+
+        first_inputs = processor(
+            first_chunk_audio,
+            is_streaming=True,
+            is_first_audio_chunk=True,
+            return_tensors="pt",
+        )
+        first_inputs = first_inputs.to(model.device, dtype=model.dtype)
+
+        streamer = TextIteratorStreamer(
+            processor.tokenizer,
+            skip_prompt=True,
+            skip_special_tokens=True,
+        )
+        self._streamer = streamer
+
+        audio_queue = self._audio_queue
+
+        def input_features_gen():
+            yield first_inputs.input_features
+            while True:
+                chunk_audio = audio_queue.get()
+                if chunk_audio is None:
+                    break
+                inputs = processor(
+                    chunk_audio,
+                    is_streaming=True,
+                    is_first_audio_chunk=False,
+                    return_tensors="pt",
+                )
+                inputs = inputs.to(model.device, dtype=model.dtype)
+                yield inputs.input_features
+
+        def run_generate():
+            try:
+                with torch.no_grad():
+                    # Pass generator as input_features — the model detects GeneratorType
+                    # and internally converts it to input_features_generator
+                    generate_kwargs = {
+                        k: v for k, v in first_inputs.items()
+                        if k != "input_features"
+                    }
+                    model.generate(
+                        input_features=input_features_gen(),
+                        streamer=streamer,
+                        **generate_kwargs,
+                    )
+            except Exception as e:
+                logger.error(f"[voxtral-hf] generate error: {e}", exc_info=True)
+                self._generate_error = e
+            finally:
+                self._generate_finished = True
+
+        self._generate_thread = threading.Thread(target=run_generate, daemon=True)
+        self._generate_thread.start()
+        self._generate_started = True
+        logger.info("[voxtral-hf] generate thread started")
+
+    def _feed_pending_audio(self):
+        """Convert pending audio into properly-sized chunks for the generator."""
+        chunk_size = self._chunk_samples
+        step_size = self._chunk_step
+
+        while len(self._pending_audio) >= chunk_size:
+            chunk = self._pending_audio[:chunk_size]
+            self._audio_queue.put(chunk)
+            self._pending_audio = self._pending_audio[step_size:]
+
+        self.audio_buffer = self._pending_audio
+
+    def _drain_streamer(self):
+        """Non-blocking drain of all available text from the streamer."""
+        if not self._generate_started:
+            return
+
+        text_queue = self._streamer.text_queue
+        while True:
+            try:
+                text_fragment = text_queue.get_nowait()
+            except queue.Empty:
+                break
+            # TextIteratorStreamer uses None as end-of-stream sentinel
+            if text_fragment is None:
+                self._generate_finished = True
+                break
+            if text_fragment:
+                with self._text_lock:
+                    self._accumulated_text += text_fragment
+                self._n_text_tokens_received += 1
+
+    # ── Word extraction ──
+
+    def _pos_to_time(self, token_position: int) -> float:
+        """Convert token position to seconds."""
+        return token_position * self._seconds_per_token + self._global_time_offset
+
+    def _extract_new_words(self) -> List[ASRToken]:
+        """Extract complete words (all but the last, which may still be growing)."""
+        with self._text_lock:
+            text = self._accumulated_text
+        if not text:
+            return []
+
+        words = text.split()
+        new_words: List[ASRToken] = []
+        n_tokens = self._n_text_tokens_received
+        n_words_total = len(words)
+
+        while len(words) > self._n_committed_words + 1:
+            word = words[self._n_committed_words]
+            word_idx = self._n_committed_words
+
+            tok_start = int(word_idx / n_words_total * n_tokens) if n_words_total > 0 else 0
+            tok_end = int((word_idx + 1) / n_words_total * n_tokens) if n_words_total > 0 else 0
+
+            start_time = self._pos_to_time(tok_start)
+            end_time = self._pos_to_time(tok_end)
+
+            text_out = word if self._n_committed_words == 0 else " " + word
+            new_words.append(ASRToken(start=start_time, end=end_time, text=text_out))
+            self._n_committed_words += 1
+
+        return new_words
+
+    def _flush_all_pending_words(self) -> List[ASRToken]:
+        """Flush ALL words including the last partial one."""
+        with self._text_lock:
+            text = self._accumulated_text
+        if not text:
+            return []
+
+        words = text.split()
+        new_words: List[ASRToken] = []
+        n_tokens = max(self._n_text_tokens_received, 1)
+        n_words_total = max(len(words), 1)
+
+        while self._n_committed_words < len(words):
+            word = words[self._n_committed_words]
+            word_idx = self._n_committed_words
+
+            tok_start = int(word_idx / n_words_total * n_tokens)
+            tok_end = int((word_idx + 1) / n_words_total * n_tokens)
+
+            start_time = self._pos_to_time(tok_start)
+            end_time = self._pos_to_time(tok_end)
+
+            text_out = word if self._n_committed_words == 0 else " " + word
+            new_words.append(ASRToken(start=start_time, end=end_time, text=text_out))
+            self._n_committed_words += 1
+
+        return new_words
+
+    # ── Core processing ──
+
+    def _process_iter_inner(self, is_last: bool) -> Tuple[List[ASRToken], float]:
+        # Start generate thread when enough audio is buffered
+        if not self._generate_started:
+            if len(self._pending_audio) >= self._first_chunk_samples:
+                self._start_generate_thread()
+                self._feed_pending_audio()
+            else:
+                return [], self.end
+
+        # Feed any new pending audio
+        if self._generate_started and not self._generate_finished:
+            self._feed_pending_audio()
+
+        # If generate finished unexpectedly (EOS) but new audio arrived, restart
+        if self._generate_finished and len(self._pending_audio) >= self._first_chunk_samples:
+            self._drain_streamer()
+            flush_words = self._flush_all_pending_words()
+            # Reset for new utterance
+            old_offset = self._global_time_offset
+            self._reset_state()
+            self._global_time_offset = old_offset
+            self._start_generate_thread()
+            self._feed_pending_audio()
+            return flush_words, self.end
+
+        # Drain available text from streamer
+        self._drain_streamer()
+
+        # Extract complete words
+        new_words = self._extract_new_words()
+
+        if new_words:
+            logger.info(f"[voxtral-hf] returning {len(new_words)} words: {[w.text for w in new_words]}")
+
+        self.buffer = []
+        return new_words, self.end

whisperlivekit/voxtral_mlx/__init__.py

@@ -0,0 +1,6 @@
+"""Pure-MLX Voxtral Realtime backend for WhisperLiveKit."""
+
+from .loader import load_voxtral_model
+from .model import VoxtralMLXModel
+
+__all__ = ["load_voxtral_model", "VoxtralMLXModel"]

whisperlivekit/voxtral_mlx/loader.py

@@ -0,0 +1,282 @@
+"""
+Model weight loading for the MLX Voxtral Realtime backend.
+
+Supports two on-disk formats:
+  1. **Converted** (``config.json`` + ``model.safetensors``): ready-to-load,
+     with optional quantisation metadata.
+  2. **Original Mistral** (``params.json`` + ``consolidated.safetensors``):
+     requires weight renaming and conv-weight transposition.
+
+The public entry point is :func:`load_voxtral_model` which returns the
+model, tokenizer, and raw config dict.
+"""
+
+import json
+import logging
+import re
+from pathlib import Path
+
+import mlx.core as mx
+import mlx.nn as nn
+from huggingface_hub import snapshot_download
+
+from .model import VoxtralMLXModel
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MODEL_ID = "mlx-community/Voxtral-Mini-4B-Realtime-6bit"
+
+# ---------------------------------------------------------------------------
+# Downloading
+# ---------------------------------------------------------------------------
+
+_ALLOWED_PATTERNS = [
+    "consolidated.safetensors",
+    "model*.safetensors",
+    "model.safetensors.index.json",
+    "params.json",
+    "config.json",
+    "tekken.json",
+]
+
+
+def download_weights(model_id: str = DEFAULT_MODEL_ID) -> Path:
+    """Download model files from HuggingFace Hub and return the local path."""
+    return Path(snapshot_download(model_id, allow_patterns=_ALLOWED_PATTERNS))
+
+
+# ---------------------------------------------------------------------------
+# Weight name remapping (Mistral → our naming)
+# ---------------------------------------------------------------------------
+
+_NAME_RULES: list[tuple[str, str]] = [
+    # Encoder convolutions
+    (r"whisper_encoder\.conv_layers\.0\.conv\.(.*)", r"encoder.conv1.\1"),
+    (r"whisper_encoder\.conv_layers\.1\.conv\.(.*)", r"encoder.conv2.\1"),
+    # Encoder transformer blocks
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.attention\.wq\.(.*)",
+     r"encoder.blocks.\1.self_attn.q_proj.\2"),
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.attention\.wk\.(.*)",
+     r"encoder.blocks.\1.self_attn.k_proj.\2"),
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.attention\.wv\.(.*)",
+     r"encoder.blocks.\1.self_attn.v_proj.\2"),
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.attention\.wo\.(.*)",
+     r"encoder.blocks.\1.self_attn.out_proj.\2"),
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.attention_norm\.(.*)",
+     r"encoder.blocks.\1.pre_attn_norm.\2"),
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.feed_forward\.w1\.(.*)",
+     r"encoder.blocks.\1.ffn.gate.\2"),
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.feed_forward\.w2\.(.*)",
+     r"encoder.blocks.\1.ffn.down.\2"),
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.feed_forward\.w3\.(.*)",
+     r"encoder.blocks.\1.ffn.up.\2"),
+    (r"whisper_encoder\.transformer\.layers\.(\d+)\.ffn_norm\.(.*)",
+     r"encoder.blocks.\1.pre_ffn_norm.\2"),
+    (r"whisper_encoder\.transformer\.norm\.(.*)", r"encoder.final_norm.\1"),
+    # Adapter
+    (r"audio_language_projection\.0\.weight", r"adapter.linear1.weight"),
+    (r"audio_language_projection\.2\.weight", r"adapter.linear2.weight"),
+    # Decoder embedding
+    (r"tok_embeddings\.weight", r"decoder.token_embedding.weight"),
+    # Decoder blocks
+    (r"layers\.(\d+)\.attention\.wq\.weight",
+     r"decoder.blocks.\1.self_attn.q_proj.weight"),
+    (r"layers\.(\d+)\.attention\.wk\.weight",
+     r"decoder.blocks.\1.self_attn.k_proj.weight"),
+    (r"layers\.(\d+)\.attention\.wv\.weight",
+     r"decoder.blocks.\1.self_attn.v_proj.weight"),
+    (r"layers\.(\d+)\.attention\.wo\.weight",
+     r"decoder.blocks.\1.self_attn.out_proj.weight"),
+    (r"layers\.(\d+)\.attention_norm\.weight",
+     r"decoder.blocks.\1.pre_attn_norm.weight"),
+    (r"layers\.(\d+)\.feed_forward\.w1\.weight",
+     r"decoder.blocks.\1.ffn.gate.weight"),
+    (r"layers\.(\d+)\.feed_forward\.w2\.weight",
+     r"decoder.blocks.\1.ffn.down.weight"),
+    (r"layers\.(\d+)\.feed_forward\.w3\.weight",
+     r"decoder.blocks.\1.ffn.up.weight"),
+    (r"layers\.(\d+)\.ffn_norm\.weight",
+     r"decoder.blocks.\1.pre_ffn_norm.weight"),
+    (r"layers\.(\d+)\.ada_rms_norm_t_cond\.0\.weight",
+     r"decoder.blocks.\1.adaptive_scale.proj_in.weight"),
+    (r"layers\.(\d+)\.ada_rms_norm_t_cond\.2\.weight",
+     r"decoder.blocks.\1.adaptive_scale.proj_out.weight"),
+    # Decoder final norm
+    (r"norm\.weight", r"decoder.final_norm.weight"),
+]
+
+_PREFIX_STRIP = re.compile(
+    r"^(mm_streams_embeddings\.embedding_module|mm_whisper_embeddings)\."
+)
+
+
+def _translate_weight_name(name: str) -> str | None:
+    name = _PREFIX_STRIP.sub("", name)
+    for pattern, replacement in _NAME_RULES:
+        result, n = re.subn(f"^{pattern}$", replacement, name)
+        if n:
+            return result
+    return None
+
+
+def _is_conv_weight(name: str) -> bool:
+    return ("conv1.weight" in name or "conv2.weight" in name) and "bias" not in name
+
+
+# ---------------------------------------------------------------------------
+# Converted-format weight remapping (voxmlx names → our names)
+# ---------------------------------------------------------------------------
+
+_CONVERTED_RULES: list[tuple[str, str]] = [
+    # Adapter
+    (r"adapter\.w_in\.(.*)", r"adapter.linear1.\1"),
+    (r"adapter\.w_out\.(.*)", r"adapter.linear2.\1"),
+    # Encoder transformer blocks
+    (r"encoder\.layers\.(\d+)\.attention\.(.*)", r"encoder.blocks.\1.self_attn.\2"),
+    (r"encoder\.layers\.(\d+)\.attn_norm\.(.*)", r"encoder.blocks.\1.pre_attn_norm.\2"),
+    (r"encoder\.layers\.(\d+)\.mlp\.gate_proj\.(.*)", r"encoder.blocks.\1.ffn.gate.\2"),
+    (r"encoder\.layers\.(\d+)\.mlp\.down_proj\.(.*)", r"encoder.blocks.\1.ffn.down.\2"),
+    (r"encoder\.layers\.(\d+)\.mlp\.up_proj\.(.*)", r"encoder.blocks.\1.ffn.up.\2"),
+    (r"encoder\.layers\.(\d+)\.ffn_norm\.(.*)", r"encoder.blocks.\1.pre_ffn_norm.\2"),
+    (r"encoder\.norm\.(.*)", r"encoder.final_norm.\1"),
+    # Decoder embedding
+    (r"language_model\.embed_tokens\.(.*)", r"decoder.token_embedding.\1"),
+    # Decoder blocks
+    (r"language_model\.layers\.(\d+)\.attention\.(.*)", r"decoder.blocks.\1.self_attn.\2"),
+    (r"language_model\.layers\.(\d+)\.attn_norm\.(.*)", r"decoder.blocks.\1.pre_attn_norm.\2"),
+    (r"language_model\.layers\.(\d+)\.mlp\.gate_proj\.(.*)", r"decoder.blocks.\1.ffn.gate.\2"),
+    (r"language_model\.layers\.(\d+)\.mlp\.down_proj\.(.*)", r"decoder.blocks.\1.ffn.down.\2"),
+    (r"language_model\.layers\.(\d+)\.mlp\.up_proj\.(.*)", r"decoder.blocks.\1.ffn.up.\2"),
+    (r"language_model\.layers\.(\d+)\.ffn_norm\.(.*)", r"decoder.blocks.\1.pre_ffn_norm.\2"),
+    (r"language_model\.layers\.(\d+)\.ada_norm\.linear_in\.(.*)",
+     r"decoder.blocks.\1.adaptive_scale.proj_in.\2"),
+    (r"language_model\.layers\.(\d+)\.ada_norm\.linear_out\.(.*)",
+     r"decoder.blocks.\1.adaptive_scale.proj_out.\2"),
+    (r"language_model\.norm\.(.*)", r"decoder.final_norm.\1"),
+]
+
+# Also remap o_proj → out_proj in both encoder and decoder
+_POST_RENAME = [
+    (r"\.o_proj\.", r".out_proj."),
+]
+
+
+def _remap_converted_name(name: str) -> str:
+    """Translate a converted-format weight name to our naming convention."""
+    for pattern, replacement in _CONVERTED_RULES:
+        result, n = re.subn(f"^{pattern}$", replacement, name)
+        if n:
+            name = result
+            break
+    for pattern, replacement in _POST_RENAME:
+        name = re.sub(pattern, replacement, name)
+    return name
+
+
+# ---------------------------------------------------------------------------
+# Loading strategies
+# ---------------------------------------------------------------------------
+
+def _has_converted_layout(path: Path) -> bool:
+    return (path / "config.json").exists() and not (path / "consolidated.safetensors").exists()
+
+
+def _load_converted_weights(path: Path):
+    with open(path / "config.json") as f:
+        config = json.load(f)
+
+    model = VoxtralMLXModel(config)
+
+    quant = config.get("quantization")
+    if quant is not None:
+        gs = quant["group_size"]
+        nn.quantize(
+            model,
+            group_size=gs,
+            bits=quant["bits"],
+            class_predicate=lambda _p, m: (
+                hasattr(m, "to_quantized") and m.weight.shape[-1] % gs == 0
+            ),
+        )
+
+    index_file = path / "model.safetensors.index.json"
+    if index_file.exists():
+        with open(index_file) as f:
+            shard_map = json.load(f)
+        shard_files = sorted(set(shard_map["weight_map"].values()))
+        weights = {}
+        for sf in shard_files:
+            weights.update(mx.load(str(path / sf)))
+    else:
+        weights = mx.load(str(path / "model.safetensors"))
+
+    remapped = {_remap_converted_name(k): v for k, v in weights.items()}
+    model.load_weights(list(remapped.items()))
+    mx.eval(model.parameters())
+    return model, config
+
+
+def _load_original_weights(path: Path):
+    with open(path / "params.json") as f:
+        config = json.load(f)
+
+    model = VoxtralMLXModel(config)
+
+    raw = mx.load(str(path / "consolidated.safetensors"))
+    mapped: dict[str, mx.array] = {}
+    skipped: list[str] = []
+
+    for name, tensor in raw.items():
+        if name == "output.weight":
+            continue
+        new_name = _translate_weight_name(name)
+        if new_name is None:
+            skipped.append(name)
+            continue
+        # Conv weights: PyTorch [C_out, C_in, K] → MLX [C_out, K, C_in]
+        if _is_conv_weight(new_name):
+            tensor = mx.swapaxes(tensor, 1, 2)
+        mapped[new_name] = tensor
+
+    if skipped:
+        logger.warning("Skipped %d unrecognised weight keys (first 5: %s)", len(skipped), skipped[:5])
+
+    model.load_weights(list(mapped.items()))
+    mx.eval(model.parameters())
+    return model, config
+
+
+# ---------------------------------------------------------------------------
+# Tokenizer
+# ---------------------------------------------------------------------------
+
+def _load_tokenizer(model_dir: Path):
+    from mistral_common.tokens.tokenizers.tekken import Tekkenizer
+    return Tekkenizer.from_file(str(model_dir / "tekken.json"))
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+def load_voxtral_model(path_or_id: str = DEFAULT_MODEL_ID):
+    """Load a Voxtral Realtime model and its tokenizer.
+
+    Args:
+        path_or_id: Local directory path **or** a HuggingFace model ID.
+
+    Returns:
+        ``(model, tokenizer, config)``
+    """
+    p = Path(path_or_id)
+    if not p.exists():
+        p = download_weights(path_or_id)
+
+    if _has_converted_layout(p):
+        model, config = _load_converted_weights(p)
+    else:
+        model, config = _load_original_weights(p)
+
+    tokenizer = _load_tokenizer(p)
+    logger.info("Voxtral MLX model loaded from %s", p)
+    return model, tokenizer, config

whisperlivekit/voxtral_mlx/model.py

@@ -0,0 +1,534 @@
+"""
+Voxtral Realtime MLX model — encoder, decoder, adapter, and top-level model.
+
+Architecture:
+    audio → StreamingEncoder → EncoderToDecoderAdapter → TextDecoder → logits
+    with DelayEmbedding providing time-conditioning to the decoder.
+
+The model supports both batch inference (full audio) and incremental streaming
+(one chunk at a time with cached encoder/decoder state).
+"""
+
+import math
+
+import mlx.core as mx
+import mlx.nn as nn
+
+
+# ---------------------------------------------------------------------------
+# KV Cache
+# ---------------------------------------------------------------------------
+
+
+class SlidingKVCache:
+    """Bounded key-value cache with rotating buffer for sliding-window attention.
+
+    Uses in-place writes for single-token autoregressive steps and
+    concatenation for multi-token prefills. Pre-allocates in blocks of
+    ``alloc_step`` entries to reduce repeated allocation.
+    """
+
+    alloc_step = 256
+
+    def __init__(self, capacity: int):
+        self.capacity = capacity
+        self.keys = None
+        self.values = None
+        self._offset = 0
+        self._write_idx = 0
+
+    @property
+    def offset(self) -> int:
+        return self._offset
+
+    # -- helpers --
+
+    def _reorder(self, buf):
+        """Return *buf* in temporal order (unwrap the circular buffer)."""
+        if self._write_idx == buf.shape[2]:
+            return buf
+        if self._write_idx < self._offset:
+            return mx.concatenate(
+                [buf[..., self._write_idx:, :], buf[..., : self._write_idx, :]],
+                axis=2,
+            )
+        return buf[..., : self._write_idx, :]
+
+    def _drop_oldest(self, buf, n_drop, tail=None):
+        parts = [buf[..., n_drop:, :]] if n_drop > 0 else [buf]
+        if tail is not None:
+            parts.append(tail)
+        return mx.concatenate(parts, axis=2)
+
+    # -- update strategies --
+
+    def _append_concat(self, k, v):
+        """Multi-token update via concatenation (used during prefill)."""
+        if self.keys is None:
+            self.keys, self.values = k, v
+        else:
+            self.keys = self._reorder(self.keys)
+            self.values = self._reorder(self.values)
+            self._write_idx = self.keys.shape[2]
+            overflow = self._write_idx - self.capacity + 1
+            self.keys = self._drop_oldest(self.keys, overflow, k)
+            self.values = self._drop_oldest(self.values, overflow, v)
+        self._offset += k.shape[2]
+        self._write_idx = self.keys.shape[2]
+        return self.keys, self.values
+
+    def _write_inplace(self, k, v):
+        """Single-token update via in-place write (autoregressive step)."""
+        B, n_heads, S, dim_k = k.shape
+        dim_v = v.shape[3]
+        prev = self._offset
+
+        if self.keys is None or (
+            prev >= self.keys.shape[2] and self.keys.shape[2] < self.capacity
+        ):
+            n_new = min(self.alloc_step, self.capacity - prev)
+            fresh_k = mx.zeros((B, n_heads, n_new, dim_k), k.dtype)
+            fresh_v = mx.zeros((B, n_heads, n_new, dim_v), v.dtype)
+            if self.keys is not None:
+                self.keys = mx.concatenate([self.keys, fresh_k], axis=2)
+                self.values = mx.concatenate([self.values, fresh_v], axis=2)
+            else:
+                self.keys, self.values = fresh_k, fresh_v
+            self._write_idx = prev
+
+        overflow = self.keys.shape[2] - self.capacity
+        if overflow > 0:
+            self.keys = self._drop_oldest(self.keys, overflow)
+            self.values = self._drop_oldest(self.values, overflow)
+            self._write_idx = self.capacity
+
+        if self._write_idx == self.capacity:
+            self._write_idx = 0
+
+        self.keys[..., self._write_idx : self._write_idx + S, :] = k
+        self.values[..., self._write_idx : self._write_idx + S, :] = v
+        self._offset += S
+        self._write_idx += S
+
+        if self._offset < self.capacity:
+            return (
+                self.keys[..., : self._offset, :],
+                self.values[..., : self._offset, :],
+            )
+        return self.keys, self.values
+
+    # -- public API --
+
+    def update_and_fetch(self, k, v):
+        if k.shape[2] == 1:
+            return self._write_inplace(k, v)
+        return self._append_concat(k, v)
+
+
+# ---------------------------------------------------------------------------
+# Encoder components
+# ---------------------------------------------------------------------------
+
+
+class CausalConv(nn.Module):
+    """1-D causal convolution (left-padded so no future leakage)."""
+
+    def __init__(self, channels_in: int, channels_out: int, kernel: int, stride: int = 1):
+        super().__init__()
+        self.stride = stride
+        self.kernel = kernel
+        self.left_pad = kernel - stride
+        self.weight = mx.zeros((channels_out, kernel, channels_in))
+        self.bias = mx.zeros((channels_out,))
+
+    def __call__(self, x: mx.array) -> mx.array:
+        if self.left_pad > 0:
+            x = mx.pad(x, [(0, 0), (self.left_pad, 0), (0, 0)])
+        return mx.conv1d(x, self.weight, stride=self.stride) + self.bias
+
+
+class _EncoderSelfAttention(nn.Module):
+    def __init__(self, dim: int, n_heads: int, head_dim: int, rope_theta: float):
+        super().__init__()
+        self.n_heads = n_heads
+        self.head_dim = head_dim
+        self.scale = head_dim**-0.5
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=True)
+        self.k_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
+        self.v_proj = nn.Linear(dim, n_heads * head_dim, bias=True)
+        self.out_proj = nn.Linear(n_heads * head_dim, dim, bias=True)
+        self.rope_theta = rope_theta
+
+    def __call__(self, x, mask, cache=None):
+        B, L, _ = x.shape
+        q = self.q_proj(x).reshape(B, L, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
+        k = self.k_proj(x).reshape(B, L, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
+        v = self.v_proj(x).reshape(B, L, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
+
+        pos = cache.offset if cache is not None else 0
+        q = mx.fast.rope(q, self.head_dim, traditional=True, base=self.rope_theta, scale=1.0, offset=pos)
+        k = mx.fast.rope(k, self.head_dim, traditional=True, base=self.rope_theta, scale=1.0, offset=pos)
+
+        if cache is not None:
+            k, v = cache.update_and_fetch(k, v)
+
+        out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=mask)
+        return self.out_proj(out.transpose(0, 2, 1, 3).reshape(B, L, -1))
+
+
+class _EncoderFFN(nn.Module):
+    """SwiGLU feed-forward for encoder layers."""
+
+    def __init__(self, dim: int, hidden: int):
+        super().__init__()
+        self.gate = nn.Linear(dim, hidden, bias=False)
+        self.up = nn.Linear(dim, hidden, bias=False)
+        self.down = nn.Linear(hidden, dim, bias=True)
+
+    def __call__(self, x):
+        return self.down(nn.silu(self.gate(x)) * self.up(x))
+
+
+class _EncoderBlock(nn.Module):
+    def __init__(self, dim, n_heads, head_dim, hidden, rope_theta):
+        super().__init__()
+        self.pre_attn_norm = nn.RMSNorm(dim, eps=1e-5)
+        self.self_attn = _EncoderSelfAttention(dim, n_heads, head_dim, rope_theta)
+        self.pre_ffn_norm = nn.RMSNorm(dim, eps=1e-5)
+        self.ffn = _EncoderFFN(dim, hidden)
+
+    def __call__(self, x, mask, cache=None):
+        x = x + self.self_attn(self.pre_attn_norm(x), mask, cache=cache)
+        x = x + self.ffn(self.pre_ffn_norm(x))
+        return x
+
+
+class StreamingEncoder(nn.Module):
+    """Causal Whisper-style encoder with two causal convolutions followed by
+    a stack of transformer blocks.  Supports both full-sequence and
+    incremental (streaming) forward passes."""
+
+    def __init__(
+        self,
+        mel_channels: int = 128,
+        dim: int = 1280,
+        n_layers: int = 32,
+        n_heads: int = 32,
+        head_dim: int = 64,
+        hidden_dim: int = 5120,
+        rope_theta: float = 1e6,
+        sliding_window: int = 750,
+    ):
+        super().__init__()
+        self.conv1 = CausalConv(mel_channels, dim, kernel=3, stride=1)
+        self.conv2 = CausalConv(dim, dim, kernel=3, stride=2)
+        self.blocks = [
+            _EncoderBlock(dim, n_heads, head_dim, hidden_dim, rope_theta)
+            for _ in range(n_layers)
+        ]
+        self.final_norm = nn.RMSNorm(dim, eps=1e-5)
+        self.sliding_window = sliding_window
+
+    # -- full-sequence --
+
+    def _apply_convs(self, mel: mx.array) -> mx.array:
+        x = mel.T[None, :, :]  # [1, T, mel_channels]
+        x = nn.gelu(self.conv1(x))
+        x = nn.gelu(self.conv2(x))
+        return x
+
+    def forward(self, mel: mx.array) -> mx.array:
+        x = self._apply_convs(mel.astype(self.conv1.weight.dtype))
+        for blk in self.blocks:
+            x = blk(x, mask="causal")
+        return self.final_norm(x)
+
+    # -- incremental (streaming) --
+
+    def forward_conv_incremental(self, x_in, tail1, tail2):
+        """Process new mel frames through the two causal convs using cached tails.
+
+        Args:
+            x_in: [1, N, mel_channels]
+            tail1: [1, pad1, mel_channels] or None (first call)
+            tail2: [1, pad2, dim] or None (first call)
+
+        Returns:
+            (out, new_tail1, new_tail2)
+        """
+        # Conv1 (kernel=3, stride=1 → left_pad=2)
+        if tail1 is not None:
+            c1_in = mx.concatenate([tail1, x_in], axis=1)
+        else:
+            c1_in = mx.pad(x_in, [(0, 0), (self.conv1.left_pad, 0), (0, 0)])
+        new_tail1 = x_in[:, -self.conv1.left_pad :, :]
+        c1_out = nn.gelu(
+            mx.conv1d(c1_in, self.conv1.weight, stride=self.conv1.stride) + self.conv1.bias
+        )
+
+        # Conv2 (kernel=3, stride=2 → left_pad=1)
+        if tail2 is not None:
+            c2_in = mx.concatenate([tail2, c1_out], axis=1)
+        else:
+            c2_in = mx.pad(c1_out, [(0, 0), (self.conv2.left_pad, 0), (0, 0)])
+        new_tail2 = c1_out[:, -self.conv2.left_pad :, :]
+        c2_out = nn.gelu(
+            mx.conv1d(c2_in, self.conv2.weight, stride=self.conv2.stride) + self.conv2.bias
+        )
+
+        return c2_out, new_tail1, new_tail2
+
+    def forward_transformer_incremental(self, x, cache_list):
+        """Run transformer blocks with per-layer KV caches."""
+        for i, blk in enumerate(self.blocks):
+            x = blk(x, mask="causal", cache=cache_list[i])
+        return self.final_norm(x)
+
+
+# ---------------------------------------------------------------------------
+# Decoder components
+# ---------------------------------------------------------------------------
+
+
+class _DecoderAttention(nn.Module):
+    """Grouped-query attention for the text decoder."""
+
+    def __init__(self, dim, n_heads, n_kv_heads, head_dim, rope_theta):
+        super().__init__()
+        self.n_heads = n_heads
+        self.n_kv_heads = n_kv_heads
+        self.head_dim = head_dim
+        self.scale = head_dim**-0.5
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
+        self.out_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
+        self.rope_theta = rope_theta
+
+    def __call__(self, x, mask=None, cache=None):
+        B, L, _ = x.shape
+        q = self.q_proj(x).reshape(B, L, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
+        k = self.k_proj(x).reshape(B, L, self.n_kv_heads, self.head_dim).transpose(0, 2, 1, 3)
+        v = self.v_proj(x).reshape(B, L, self.n_kv_heads, self.head_dim).transpose(0, 2, 1, 3)
+
+        pos = cache.offset if cache is not None else 0
+        q = mx.fast.rope(q, self.head_dim, traditional=True, base=self.rope_theta, scale=1.0, offset=pos)
+        k = mx.fast.rope(k, self.head_dim, traditional=True, base=self.rope_theta, scale=1.0, offset=pos)
+
+        if cache is not None:
+            k, v = cache.update_and_fetch(k, v)
+
+        out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=mask)
+        return self.out_proj(out.transpose(0, 2, 1, 3).reshape(B, L, -1))
+
+
+class _DecoderFFN(nn.Module):
+    """SwiGLU feed-forward for decoder layers."""
+
+    def __init__(self, dim, hidden):
+        super().__init__()
+        self.gate = nn.Linear(dim, hidden, bias=False)
+        self.up = nn.Linear(dim, hidden, bias=False)
+        self.down = nn.Linear(hidden, dim, bias=False)
+
+    def __call__(self, x):
+        return self.down(nn.silu(self.gate(x)) * self.up(x))
+
+
+class AdaptiveScaling(nn.Module):
+    """Small MLP that produces a multiplicative scale from the delay embedding,
+    used to condition the FFN on the streaming delay."""
+
+    def __init__(self, dim, bottleneck):
+        super().__init__()
+        self.proj_in = nn.Linear(dim, bottleneck, bias=False)
+        self.proj_out = nn.Linear(bottleneck, dim, bias=False)
+
+    def __call__(self, cond):
+        return self.proj_out(nn.gelu(self.proj_in(cond)))
+
+
+class _DecoderBlock(nn.Module):
+    def __init__(self, dim, n_heads, n_kv_heads, head_dim, hidden, rope_theta, cond_dim):
+        super().__init__()
+        self.pre_attn_norm = nn.RMSNorm(dim, eps=1e-5)
+        self.self_attn = _DecoderAttention(dim, n_heads, n_kv_heads, head_dim, rope_theta)
+        self.adaptive_scale = AdaptiveScaling(dim, cond_dim)
+        self.pre_ffn_norm = nn.RMSNorm(dim, eps=1e-5)
+        self.ffn = _DecoderFFN(dim, hidden)
+
+    def __call__(self, x, delay_cond, mask=None, cache=None):
+        x = x + self.self_attn(self.pre_attn_norm(x), mask, cache)
+        scaled = self.pre_ffn_norm(x) * (1.0 + self.adaptive_scale(delay_cond))
+        x = x + self.ffn(scaled)
+        return x
+
+
+class TextDecoder(nn.Module):
+    """Mistral-style causal language model with adaptive time-conditioning."""
+
+    def __init__(
+        self,
+        dim: int = 3072,
+        n_layers: int = 26,
+        n_heads: int = 32,
+        n_kv_heads: int = 8,
+        head_dim: int = 128,
+        hidden_dim: int = 9216,
+        vocab_size: int = 131072,
+        rope_theta: float = 1e6,
+        cond_dim: int = 32,
+    ):
+        super().__init__()
+        self.token_embedding = nn.Embedding(vocab_size, dim)
+        self.blocks = [
+            _DecoderBlock(dim, n_heads, n_kv_heads, head_dim, hidden_dim, rope_theta, cond_dim)
+            for _ in range(n_layers)
+        ]
+        self.final_norm = nn.RMSNorm(dim, eps=1e-5)
+
+    def embed(self, token_ids: mx.array) -> mx.array:
+        return self.token_embedding(token_ids)
+
+    def __call__(self, x, delay_cond, mask=None, cache=None):
+        delay_cond = delay_cond.astype(x.dtype)
+        for i, blk in enumerate(self.blocks):
+            blk_cache = cache[i] if cache is not None else None
+            x = blk(x, delay_cond, mask, blk_cache)
+        x = self.final_norm(x)
+        return self.token_embedding.as_linear(x)
+
+
+# ---------------------------------------------------------------------------
+# Adapter & embeddings
+# ---------------------------------------------------------------------------
+
+
+class EncoderToDecoderAdapter(nn.Module):
+    """Two-layer projection from encoder space to decoder space."""
+
+    def __init__(self, enc_dim: int, dec_dim: int):
+        super().__init__()
+        self.linear1 = nn.Linear(enc_dim, dec_dim, bias=False)
+        self.linear2 = nn.Linear(dec_dim, dec_dim, bias=False)
+
+    def __call__(self, x):
+        return self.linear2(nn.gelu(self.linear1(x)))
+
+
+class DelayEmbedding(nn.Module):
+    """Sinusoidal embedding that encodes the streaming delay as a conditioning
+    vector for the decoder's adaptive scaling."""
+
+    def __init__(self, dim: int = 3072, theta: float = 10000.0):
+        super().__init__()
+        self.dim = dim
+        half = dim // 2
+        freqs = mx.exp(-math.log(theta) * mx.arange(half, dtype=mx.float32) / half)
+        self._freqs = freqs
+
+    def __call__(self, delay: mx.array) -> mx.array:
+        t = delay.reshape(-1, 1).astype(mx.float32)
+        angles = t * self._freqs
+        return mx.concatenate([mx.cos(angles), mx.sin(angles)], axis=-1)
+
+
+# ---------------------------------------------------------------------------
+# Top-level model
+# ---------------------------------------------------------------------------
+
+
+class VoxtralMLXModel(nn.Module):
+    """Top-level Voxtral Realtime model wiring encoder, adapter, and decoder."""
+
+    def __init__(self, config: dict):
+        super().__init__()
+
+        enc_cfg = config["multimodal"]["whisper_model_args"]["encoder_args"]
+        audio_cfg = enc_cfg["audio_encoding_args"]
+        ds_factor = config["multimodal"]["whisper_model_args"]["downsample_args"]["downsample_factor"]
+
+        self.encoder = StreamingEncoder(
+            mel_channels=audio_cfg["num_mel_bins"],
+            dim=enc_cfg["dim"],
+            n_layers=enc_cfg["n_layers"],
+            n_heads=enc_cfg["n_heads"],
+            head_dim=enc_cfg["head_dim"],
+            hidden_dim=enc_cfg["hidden_dim"],
+            rope_theta=enc_cfg["rope_theta"],
+            sliding_window=enc_cfg["sliding_window"],
+        )
+
+        adapter_input_dim = enc_cfg["dim"] * ds_factor
+        decoder_dim = config["dim"]
+        cond_bottleneck = config.get("ada_rms_norm_t_cond_dim", 32)
+
+        self.adapter = EncoderToDecoderAdapter(adapter_input_dim, decoder_dim)
+
+        self.decoder = TextDecoder(
+            dim=decoder_dim,
+            n_layers=config["n_layers"],
+            n_heads=config["n_heads"],
+            n_kv_heads=config["n_kv_heads"],
+            head_dim=config["head_dim"],
+            hidden_dim=config["hidden_dim"],
+            vocab_size=config["vocab_size"],
+            rope_theta=config["rope_theta"],
+            cond_dim=cond_bottleneck,
+        )
+
+        self.delay_embedding = DelayEmbedding(dim=decoder_dim)
+        self.ds_factor = ds_factor
+
+    # -- batch encode --
+
+    def encode(self, mel: mx.array) -> mx.array:
+        T = mel.shape[1]
+        if T % 2 != 0:
+            mel = mel[:, 1:]
+
+        h = self.encoder.forward(mel)  # [1, T/2, enc_dim]
+        h = h[0]
+
+        n = h.shape[0]
+        trim = n % self.ds_factor
+        if trim:
+            h = h[trim:]
+            n = h.shape[0]
+
+        h = h.reshape(n // self.ds_factor, -1)
+        return self.adapter(h)
+
+    # -- incremental encode --
+
+    def encode_incremental(self, new_mel, conv_tail1, conv_tail2, enc_cache, ds_remainder):
+        """Incrementally encode new mel frames.
+
+        Returns:
+            (audio_embeds | None, conv_tail1, conv_tail2, enc_cache, ds_remainder)
+        """
+        x = new_mel.T[None, :, :].astype(self.encoder.conv1.weight.dtype)
+
+        x, conv_tail1, conv_tail2 = self.encoder.forward_conv_incremental(x, conv_tail1, conv_tail2)
+
+        if enc_cache is None:
+            enc_cache = [SlidingKVCache(100_000) for _ in range(len(self.encoder.blocks))]
+
+        x = self.encoder.forward_transformer_incremental(x, enc_cache)
+        x = x[0]  # [N, enc_dim]
+
+        if ds_remainder is not None:
+            x = mx.concatenate([ds_remainder, x])
+
+        n_full = (x.shape[0] // self.ds_factor) * self.ds_factor
+        if n_full == 0:
+            return None, conv_tail1, conv_tail2, enc_cache, x
+
+        leftover = x[n_full:] if x.shape[0] > n_full else None
+        x = x[:n_full].reshape(n_full // self.ds_factor, -1)
+        return self.adapter(x), conv_tail1, conv_tail2, enc_cache, leftover
+
+    # -- decode --
+
+    def decode(self, embeddings, delay_cond, mask=None, cache=None):
+        return self.decoder(embeddings, delay_cond, mask, cache)