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base: LLM:main
Branches Tags
LLM:main LLM:voxtral_tests LLM:feature/voxtral LLM:api_live LLM:VAD-evolutions LLM:rework_state_light LLM:new_api LLM:language-detection LLM:ScriptProcessorNode-to-AudioWorklet LLM:translation-improvements LLM:windows_audio_loopback LLM:new-api LLM:regularfry-ayo-logging-fixes LLM:seamless-streaming
LLM:v0.2.19 LLM:0.2.18 LLM:0.2.17.post1 LLM:0.2.17 LLM:0.2.16 LLM:0.2.15 LLM:0.2.14 LLM:0.2.13 LLM:0.2.12 LLM:0.2.11 LLM:0.2.10 LLM:0.2.9 LLM:0.2.8 LLM:0.2.7 LLM:0.2.6 LLM:0.2.5 LLM:0.2.4.dev0 LLM:0.2.2 LLM:0.2.1 LLM:0.1.9 LLM:0.1.8 LLM:0.1.7 LLM:0.1.6 LLM:0.1.5 LLM:0.1.4 LLM:0.1.3 LLM:0.1.1 LLM:0.0.1
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compare: LLM:feature/voxtral
Branches Tags
LLM:main LLM:voxtral_tests LLM:feature/voxtral LLM:api_live LLM:VAD-evolutions LLM:rework_state_light LLM:new_api LLM:language-detection LLM:ScriptProcessorNode-to-AudioWorklet LLM:translation-improvements LLM:windows_audio_loopback LLM:new-api LLM:regularfry-ayo-logging-fixes LLM:seamless-streaming
LLM:v0.2.19 LLM:0.2.18 LLM:0.2.17.post1 LLM:0.2.17 LLM:0.2.16 LLM:0.2.15 LLM:0.2.14 LLM:0.2.13 LLM:0.2.12 LLM:0.2.11 LLM:0.2.10 LLM:0.2.9 LLM:0.2.8 LLM:0.2.7 LLM:0.2.6 LLM:0.2.5 LLM:0.2.4.dev0 LLM:0.2.2 LLM:0.2.1 LLM:0.1.9 LLM:0.1.8 LLM:0.1.7 LLM:0.1.6 LLM:0.1.5 LLM:0.1.4 LLM:0.1.3 LLM:0.1.1 LLM:0.0.1

1 Commits
main ... feature/vo

Author SHA1 Message Date
Quentin Fuxa
7ea507ed8e Add Voxtral MLX streaming backend 
Integrates the voxmlx-based Voxtral Mini Realtime streaming pipeline:
- VoxtralStreamingASR and VoxtralStreamingOnlineProcessor
- Incremental audio encoding and token-by-token autoregressive decoding
- Selectable via --backend voxtral-mlx

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-17 09:20:28 +01:00
59 changed files with 1839 additions and 14260 deletions
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13
.dockerignore
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@@ -1,13 +0,0 @@
.git
.github
.venv
__pycache__
*.pyc
.pytest_cache
.mypy_cache
.ruff_cache
.cache
.tmp
.secrets
dist
build

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.github/workflows/publish-docker.yml vendored
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name: Publish Docker Images
on:
push:
tags:
- "v*"
workflow_dispatch:
inputs:
tag:
description: "Image tag to publish (without image suffix)"
required: true
type: string
permissions:
contents: read
packages: write
jobs:
docker:
runs-on: ubuntu-latest
env:
IMAGE_TAG: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.tag || github.ref_name }}
strategy:
fail-fast: false
matrix:
include:
- image_suffix: cpu-diarization-sortformer
dockerfile: Dockerfile.cpu
extras: cpu,diarization-sortformer
- image_suffix: cu129-diarization-sortformer
dockerfile: Dockerfile
extras: cu129,diarization-sortformer
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set lowercase owner
id: owner
run: echo "value=${GITHUB_REPOSITORY_OWNER,,}" >> "${GITHUB_OUTPUT}"
- name: Login to GHCR
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Setup Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Build and push image
uses: docker/build-push-action@v6
with:
context: .
file: ./${{ matrix.dockerfile }}
push: true
build-args: |
EXTRAS=${{ matrix.extras }}
tags: |
ghcr.io/${{ steps.owner.outputs.value }}/whisperlivekit:${{ env.IMAGE_TAG }}-${{ matrix.image_suffix }}
ghcr.io/${{ steps.owner.outputs.value }}/whisperlivekit:latest-${{ matrix.image_suffix }}

6
.gitignore vendored
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@@ -119,11 +119,9 @@ run_*.sh
*.pt
# Debug & testing
/test_*.py
!test_backend_offline.py
test_*.py
launch.json
.DS_Store
/test/
!tests/
test/*
nllb-200-distilled-600M-ctranslate2/*
*.mp3

205
BENCHMARK.md
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# 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

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Dockerfile
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@@ -1,75 +1,84 @@
FROM ghcr.io/astral-sh/uv:0.10.4 AS uvbin
FROM nvidia/cuda:12.9.1-cudnn-devel-ubuntu24.04
# --- MARK: Builder Stage
FROM nvidia/cuda:12.9.1-cudnn-devel-ubuntu24.04 AS builder-gpu
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
WORKDIR /app
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
python3-dev && \
rm -rf /var/lib/apt/lists/*
# Install UV and set up the environment
COPY --from=uvbin /uv /uvx /bin/
ENV UV_COMPILE_BYTECODE=1 UV_LINK_MODE=copy UV_NO_DEV=1
ENV UV_PYTHON_PREFERENCE=only-managed
ENV UV_PYTHON_INSTALL_DIR=/python
RUN uv python install 3.12
# Install dependencies first to leverage caching
ARG EXTRAS=cu129
COPY pyproject.toml uv.lock /app/
RUN set -eux; \
set --; \
for extra in $(echo "${EXTRAS:-}" | tr ',' ' '); do \
set -- "$@" --extra "$extra"; \
done; \
uv sync --frozen --no-install-project --no-editable --no-cache "$@"
# Copy the source code and install the package only
COPY whisperlivekit /app/whisperlivekit
RUN set -eux; \
set --; \
for extra in $(echo "${EXTRAS:-}" | tr ',' ' '); do \
set -- "$@" --extra "$extra"; \
done; \
uv sync --frozen --no-editable --no-cache "$@"
# --- MARK: Runtime Stage
FROM nvidia/cuda:12.9.1-cudnn-runtime-ubuntu24.04
ENV DEBIAN_FRONTEND=noninteractive
WORKDIR /app
ARG EXTRAS
ARG HF_PRECACHE_DIR
ARG HF_TKN_FILE
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ffmpeg &&\
rm -rf /var/lib/apt/lists/*
apt-get install -y --no-install-recommends \
python3 \
python3-pip \
python3-venv \
ffmpeg \
git \
build-essential \
python3-dev \
ca-certificates && \
rm -rf /var/lib/apt/lists/*
# Copy UV binaries
COPY --from=uvbin /uv /uvx /bin/
RUN python3 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
# Copy the Python version
COPY --from=builder-gpu --chown=python:python /python /python
# timeout/retries for large torch wheels
RUN pip3 install --upgrade pip setuptools wheel && \
pip3 --disable-pip-version-check install --timeout=120 --retries=5 \
--index-url https://download.pytorch.org/whl/cu129 \
torch torchaudio \
|| (echo "Initial install failed — retrying with extended timeout..." && \
pip3 --disable-pip-version-check install --timeout=300 --retries=3 \
--index-url https://download.pytorch.org/whl/cu129 \
torch torchvision torchaudio)
# Copy the virtual environment with all dependencies installed
COPY --from=builder-gpu /app/.venv /app/.venv
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[$EXTRAS]"; \
else \
echo "Installing base package only"; \
pip install --no-cache-dir whisperlivekit; \
fi
# In-container caching for Hugging Face models by:
# A) Make the cache directory persistent via an anonymous volume.
# Note: This only persists for a single, named container. This is
# only for convenience at de/test stage.
# For prod, it is better to use a named volume via host mount/k8s.
VOLUME ["/root/.cache/huggingface/hub"]
# or
# B) Conditionally copy a local pre-cache from the build context to the
# container's cache via the HF_PRECACHE_DIR build-arg.
# WARNING: This will copy ALL files in the pre-cache location.
# Conditionally copy a cache directory if provided
RUN if [ -n "$HF_PRECACHE_DIR" ]; then \
echo "Copying Hugging Face cache from $HF_PRECACHE_DIR"; \
mkdir -p /root/.cache/huggingface/hub && \
cp -r $HF_PRECACHE_DIR/* /root/.cache/huggingface/hub; \
else \
echo "No local Hugging Face cache specified, skipping copy"; \
fi
# Conditionally copy a Hugging Face token if provided. Useful for Diart backend (pyannote audio models)
RUN if [ -n "$HF_TKN_FILE" ]; then \
echo "Copying Hugging Face token from $HF_TKN_FILE"; \
mkdir -p /root/.cache/huggingface && \
cp $HF_TKN_FILE /root/.cache/huggingface/token; \
else \
echo "No Hugging Face token file specified, skipping token setup"; \
fi
EXPOSE 8000
ENV PATH="/app/.venv/bin:$PATH"
ENV UV_PYTHON_DOWNLOADS=0
HEALTHCHECK --interval=30s --timeout=5s --start-period=120s --retries=3 \
CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/')" || exit 1
ENTRYPOINT ["whisperlivekit-server", "--host", "0.0.0.0"]
CMD ["--model", "medium"]

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Dockerfile.cpu
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@@ -1,76 +1,61 @@
FROM ghcr.io/astral-sh/uv:0.10.4 AS uvbin
FROM python:3.13-slim
# --- MARK: Builder Stage
FROM debian:bookworm-slim AS builder-cpu
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
WORKDIR /app
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
python3-dev && \
rm -rf /var/lib/apt/lists/*
# Install UV and set up the environment
COPY --from=uvbin /uv /uvx /bin/
ENV UV_COMPILE_BYTECODE=1 UV_LINK_MODE=copy UV_NO_DEV=1
ENV UV_PYTHON_PREFERENCE=only-managed
ENV UV_PYTHON_INSTALL_DIR=/python
RUN uv python install 3.12
# Install dependencies first to leverage caching
ARG EXTRAS=cpu
COPY pyproject.toml uv.lock /app/
RUN set -eux; \
set --; \
for extra in $(echo "${EXTRAS:-}" | tr ',' ' '); do \
set -- "$@" --extra "$extra"; \
done; \
uv sync --frozen --no-install-project --no-editable --no-cache "$@"
# Copy the source code and install the package only
COPY whisperlivekit /app/whisperlivekit
RUN set -eux; \
set --; \
for extra in $(echo "${EXTRAS:-}" | tr ',' ' '); do \
set -- "$@" --extra "$extra"; \
done; \
uv sync --frozen --no-editable --no-cache "$@"
# --- MARK: Runtime Stage
FROM debian:bookworm-slim
ENV DEBIAN_FRONTEND=noninteractive
WORKDIR /app
ARG EXTRAS
ARG HF_PRECACHE_DIR
ARG HF_TKN_FILE
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ffmpeg &&\
rm -rf /var/lib/apt/lists/*
apt-get install -y --no-install-recommends \
ffmpeg \
git \
build-essential \
python3-dev && \
rm -rf /var/lib/apt/lists/*
# Copy UV binaries
COPY --from=uvbin /uv /uvx /bin/
# Install CPU-only PyTorch
RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
# Copy the Python version
COPY --from=builder-cpu --chown=python:python /python /python
COPY . .
# Copy the virtual environment with all dependencies installed
COPY --from=builder-cpu /app/.venv /app/.venv
# Install WhisperLiveKit directly, allowing for optional dependencies
RUN if [ -n "$EXTRAS" ]; then \
echo "Installing with extras: [$EXTRAS]"; \
pip install --no-cache-dir whisperlivekit[$EXTRAS]; \
else \
echo "Installing base package only"; \
pip install --no-cache-dir whisperlivekit; \
fi
# Enable in-container caching for Hugging Face models
VOLUME ["/root/.cache/huggingface/hub"]
# Conditionally copy a local pre-cache from the build context
RUN if [ -n "$HF_PRECACHE_DIR" ]; then \
echo "Copying Hugging Face cache from $HF_PRECACHE_DIR"; \
mkdir -p /root/.cache/huggingface/hub && \
cp -r $HF_PRECACHE_DIR/* /root/.cache/huggingface/hub; \
else \
echo "No local Hugging Face cache specified, skipping copy"; \
fi
# Conditionally copy a Hugging Face token if provided
RUN if [ -n "$HF_TKN_FILE" ]; then \
echo "Copying Hugging Face token from $HF_TKN_FILE"; \
mkdir -p /root/.cache/huggingface && \
cp $HF_TKN_FILE /root/.cache/huggingface/token; \
else \
echo "No Hugging Face token file specified, skipping token setup"; \
fi
# Expose port for the transcription server
EXPOSE 8000
ENV PATH="/app/.venv/bin:$PATH"
ENV UV_PYTHON_DOWNLOADS=0
HEALTHCHECK --interval=30s --timeout=5s --start-period=120s --retries=3 \
CMD python -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
CMD ["--model", "tiny"]
CMD ["--model", "tiny"]

120
README.md
View File

@@ -18,15 +18,13 @@
</p>
### Powered by Leading Research:
#### Powered by Leading Research:
**See the interactive playground in [this repo](https://github.com/QuentinFuxa/streamlit-d3-network) to explore how AlignAtt works**
- 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).
- 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
@@ -73,61 +71,19 @@ Go to `chrome-extension` for instructions.
#### Optional Dependencies
| Feature | `uv sync` | `pip install -e` |
|-----------|-------------|-------------|
| **Apple Silicon MLX Whisper backend** | `uv sync --extra mlx-whisper` | `pip install -e ".[mlx-whisper]"` |
| **Voxtral (MLX backend, Apple Silicon)** | `uv sync --extra voxtral-mlx` | `pip install -e ".[voxtral-mlx]"` |
| **CPU PyTorch stack** | `uv sync --extra cpu` | `pip install -e ".[cpu]"` |
| **CUDA 12.9 PyTorch stack** | `uv sync --extra cu129` | `pip install -e ".[cu129]"` |
| **Translation** | `uv sync --extra translation` | `pip install -e ".[translation]"` |
| **Sentence tokenizer** | `uv sync --extra sentence_tokenizer` | `pip install -e ".[sentence_tokenizer]"` |
| **Voxtral (HF backend)** | `uv sync --extra voxtral-hf` | `pip install -e ".[voxtral-hf]"` |
| **Speaker diarization (Sortformer / NeMo)** | `uv sync --extra diarization-sortformer` | `pip install -e ".[diarization-sortformer]"` |
| *[Not recommended]* Speaker diarization with Diart | `uv sync --extra diarization-diart` | `pip install -e ".[diarization-diart]"` |
| Optional | `pip install` |
|-----------|-------------|
| **Windows/Linux optimizations** | `faster-whisper` |
| **Apple Silicon optimizations** | `mlx-whisper` |
| **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` |
Supported GPU profiles:
```bash
# Profile A: Sortformer diarization
uv sync --extra cu129 --extra diarization-sortformer
# Profile B: Voxtral HF + translation
uv sync --extra cu129 --extra voxtral-hf --extra translation
```
`voxtral-hf` and `diarization-sortformer` are intentionally incompatible extras and must be installed in separate environments.
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!
See **Parameters & Configuration** below on how to use them.
### 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)
pip install -e ".[voxtral-mlx]"
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:
@@ -136,11 +92,8 @@ See [BENCHMARK.md](BENCHMARK.md) for performance numbers.
# Large model and translate from french to danish
wlk --model large-v3 --language fr --target-language da
# Diarization and server listening on */80
# 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
```
@@ -198,7 +151,7 @@ async def websocket_endpoint(websocket: WebSocket):
| `--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` |
| `--backend` | Whisper implementation selector. `auto` picks MLX on macOS (if installed), otherwise Faster-Whisper, otherwise vanilla Whisper. You can also force `mlx-whisper`, `faster-whisper`, `whisper`, or `openai-api` (LocalAgreement only) | `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` |
@@ -295,7 +248,7 @@ docker run --gpus all -p 8000:8000 --name wlk wlk
**CPU only:**
```bash
docker build -f Dockerfile.cpu -t wlk --build-arg EXTRAS="cpu" .
docker build -f Dockerfile.cpu -t wlk .
docker run -p 8000:8000 --name wlk wlk
```
@@ -307,18 +260,6 @@ docker run -p 8000:8000 --name wlk wlk
docker run --gpus all -p 8000:8000 --name wlk wlk --model large-v3 --language fr
```
**Compose (recommended for cache + token wiring):**
```bash
# GPU Sortformer profile
docker compose up --build wlk-gpu-sortformer
# GPU Voxtral profile
docker compose up --build wlk-gpu-voxtral
# CPU service
docker compose up --build wlk-cpu
```
### Memory Requirements
- **Large models**: Ensure your Docker runtime has sufficient memory allocated
@@ -326,34 +267,9 @@ docker compose up --build wlk-cpu
#### Customization
- `--build-arg` Options:
- `EXTRAS="cu129,diarization-sortformer"` - GPU Sortformer profile extras.
- `EXTRAS="cu129,voxtral-hf,translation"` - GPU Voxtral profile extras.
- `EXTRAS="cpu,diarization-diart,translation"` - CPU profile extras.
- Hugging Face cache + token are configured in `compose.yml` using a named volume and `HF_TKN_FILE` (default: `./token`).
- `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
## 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
## 🔮 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...

97
audio_tests/00_00_07_english_1_speaker.transcript.json
View File

@@ -1,97 +0,0 @@
[
{
"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
}
]

177
audio_tests/00_00_16_french_1_speaker.transcript.json
View File

@@ -1,177 +0,0 @@
[
{
"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
}
]

382
audio_tests/00_00_30_english_3_speakers.transcript.json
View File

@@ -1,382 +0,0 @@
[
{
"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
}
]

57
audio_tests/generate_transcripts.py
View File

@@ -1,57 +0,0 @@
#!/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()

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52
compose.yml
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@@ -1,52 +0,0 @@
services:
wlk-gpu-sortformer:
build:
context: .
dockerfile: Dockerfile
args:
EXTRAS: ${GPU_SORTFORMER_EXTRAS:-cu129,diarization-sortformer}
image: wlk:gpu-sortformer
gpus: all
ports:
- "8000:8000"
volumes:
- hf-cache:/root/.cache/huggingface/hub
# - ${HF_TKN_FILE:-./token}:/root/.cache/huggingface/token:ro
environment:
- HF_TOKEN
command: ["--model", "medium", "--diarization", "--pcm-input"]
wlk-gpu-voxtral:
build:
context: .
dockerfile: Dockerfile
args:
EXTRAS: ${GPU_VOXTRAL_EXTRAS:-cu129,voxtral-hf,translation}
image: wlk:gpu-voxtral
gpus: all
ports:
- "8001:8000"
volumes:
- hf-cache:/root/.cache/huggingface/hub
# - ${HF_TKN_FILE:-./token}:/root/.cache/huggingface/token:ro
environment:
- HF_TOKEN
command: ["--backend", "voxtral", "--pcm-input"]
wlk-cpu:
build:
context: .
dockerfile: Dockerfile.cpu
args:
EXTRAS: ${CPU_EXTRAS:-cpu,diarization-diart,translation}
image: wlk:cpu
ports:
- "8000:8000"
volumes:
- hf-cache:/root/.cache/huggingface/hub
# - ${HF_TKN_FILE:-./token}:/root/.cache/huggingface/token:ro
environment:
- HF_TOKEN
volumes:
hf-cache:

96
pyproject.toml
View File

@@ -4,21 +4,27 @@ build-backend = "setuptools.build_meta"
[project]
name = "whisperlivekit"
version = "0.2.19"
version = "0.2.18"
description = "Real-time speech-to-text with speaker diarization using Whisper"
readme = "README.md"
authors = [{ name = "Quentin Fuxa" }]
authors = [
{ name = "Quentin Fuxa" }
]
license = { file = "LICENSE" }
requires-python = ">=3.11, <3.14"
requires-python = ">=3.9"
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Programming Language :: Python :: 3.15",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Multimedia :: Sound/Audio :: Speech",
"Topic :: Multimedia :: Sound/Audio :: Speech"
]
dependencies = [
"fastapi",
@@ -26,91 +32,18 @@ dependencies = [
"soundfile",
"uvicorn",
"websockets",
"torchaudio>=2.0.0",
"torch>=2.0.0",
"huggingface-hub>=0.25.0",
"faster-whisper>=1.2.0",
"torch>=2.0.0",
"torchaudio>=2.0.0",
"tqdm",
"tiktoken",
'triton>=2.0.0; platform_machine == "x86_64" and (sys_platform == "linux" or sys_platform == "linux2")'
]
[project.optional-dependencies]
test = ["pytest>=7.0", "pytest-asyncio>=0.21"]
translation = ["nllw"]
sentence_tokenizer = ["mosestokenizer", "wtpsplit"]
mlx-whisper = [
'mlx>=0.11.0; sys_platform == "darwin" and platform_machine == "arm64"',
'mlx-whisper>=0.4.0; sys_platform == "darwin" and platform_machine == "arm64"',
]
voxtral-mlx = [
'mlx>=0.11.0; sys_platform == "darwin" and platform_machine == "arm64"',
'mlx-whisper>=0.4.0; sys_platform == "darwin" and platform_machine == "arm64"',
"mistral-common[audio]",
]
voxtral-hf = [
"transformers>=5.2.0; python_version >= '3.10'",
"mistral-common[audio]",
"accelerate>=0.12",
]
cpu = ["torch>=2.0.0", "torchaudio>=2.0.0"]
cu129 = [
"torch>=2.0.0",
"torchaudio>=2.0.0",
'triton>=2.0.0; platform_machine == "x86_64" and (sys_platform == "linux" or sys_platform == "linux2")',
]
diarization-sortformer = [
"nemo-toolkit[asr]>2.4; python_version >= '3.10' and python_version < '3.13'",
]
diarization-diart = [
"diart",
"torch<2.9.0",
"torchaudio<2.9.0",
"torchvision<0.24.0",
]
[dependency-groups]
dev = ["rich>=14.3.3"]
[tool.uv]
conflicts = [
[
{ extra = "cpu" },
{ extra = "cu129" },
],
[
{ extra = "diarization-diart" },
{ extra = "cu129" },
],
[
{ extra = "voxtral-hf" },
{ extra = "diarization-sortformer" },
],
]
[tool.uv.sources]
torch = [
{ index = "pytorch-cpu", extra = "cpu", marker = "platform_system != 'Darwin'" },
{ index = "pytorch-cpu", extra = "diarization-diart", marker = "platform_system != 'Darwin'" },
{ index = "pytorch-cu129", extra = "cu129", marker = "platform_system == 'Linux' and platform_machine == 'x86_64'" },
]
torchaudio = [
{ index = "pytorch-cpu", extra = "cpu", marker = "platform_system != 'Darwin'" },
{ index = "pytorch-cpu", extra = "diarization-diart", marker = "platform_system != 'Darwin'" },
{ index = "pytorch-cu129", extra = "cu129", marker = "platform_system == 'Linux' and platform_machine == 'x86_64'" },
]
torchvision = [
{ index = "pytorch-cpu", extra = "diarization-diart", marker = "platform_system != 'Darwin'" },
]
[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true
[[tool.uv.index]]
name = "pytorch-cu129"
url = "https://download.pytorch.org/whl/cu129"
explicit = true
[project.urls]
Homepage = "https://github.com/QuentinFuxa/WhisperLiveKit"
@@ -130,8 +63,7 @@ packages = [
"whisperlivekit.whisper.normalizers",
"whisperlivekit.web",
"whisperlivekit.local_agreement",
"whisperlivekit.voxtral_mlx",
"whisperlivekit.silero_vad_models",
"whisperlivekit.silero_vad_models"
]
[tool.setuptools.package-data]

291
run_benchmark.py
View File

@@ -1,291 +0,0 @@
#!/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()

580
scripts/python_support_matrix.py
View File

@@ -1,580 +0,0 @@
#!/usr/bin/env python3
"""Offline Python support matrix runner for WhisperLiveKit."""
from __future__ import annotations
import argparse
import os
import shlex
import shutil
import subprocess
import sys
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Literal
try:
from rich.console import Console
from rich.table import Table
HAS_RICH = True
except Exception:
HAS_RICH = False
SAMPLE_URL = (
"https://github.com/pyannote/pyannote-audio/raw/develop/tutorials/assets/sample.wav"
)
SAMPLE_PATH = Path("audio_tests/support-matrix-sample.wav")
DEFAULT_LOGS_DIR = Path("outputs/python-matrix/logs")
PYTHON_VERSIONS = ("3.11", "3.12", "3.13")
CONSOLE = Console() if HAS_RICH else None
@dataclass(frozen=True)
class MatrixRow:
row_id: str
extras: tuple[str, ...]
backend: str
policy: str
diarization_backend: str
requires_gpu: bool = False
CASES = (
MatrixRow(
row_id="fw-diart-cpu",
extras=("test", "cpu", "diarization-diart"),
backend="faster-whisper",
policy="simulstreaming",
diarization_backend="diart",
),
MatrixRow(
row_id="fw-sortformer-cpu",
extras=("test", "cpu", "diarization-sortformer"),
backend="faster-whisper",
policy="simulstreaming",
diarization_backend="sortformer",
),
MatrixRow(
row_id="fw-sortformer-gpu",
extras=("test", "cu129", "diarization-sortformer"),
backend="faster-whisper",
policy="simulstreaming",
diarization_backend="sortformer",
requires_gpu=True,
),
MatrixRow(
row_id="voxtral-diart-cpu",
extras=("test", "cpu", "voxtral-hf", "diarization-diart"),
backend="voxtral",
policy="voxtral",
diarization_backend="diart",
),
)
EXPECTED_FAILURE_CASES = {
("3.11", "voxtral-diart-cpu"): "known_unstable_voxtral_diart_cpu",
("3.12", "voxtral-diart-cpu"): "known_unstable_voxtral_diart_cpu",
}
UNSUPPORTED_CASES = {
("3.13", "fw-sortformer-cpu"): "unsupported_py313_sortformer_protobuf",
("3.13", "fw-sortformer-gpu"): "unsupported_py313_sortformer_protobuf",
}
@dataclass(frozen=True)
class CaseResult:
python_version: str
row_id: str
status: Literal["PASS", "FAIL", "N/A"]
reason: str
duration_sec: float
hint: str = ""
log_path: str = ""
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Minimal WhisperLiveKit offline support matrix"
)
parser.add_argument(
"--timeout-sec",
type=int,
default=300,
help="Per-case timeout in seconds (default: 300)",
)
parser.add_argument(
"--logs-dir",
default=str(DEFAULT_LOGS_DIR),
help="Directory where per-case logs are written (default: outputs/python-matrix/logs)",
)
return parser.parse_args()
def safe_slug(text: str) -> str:
return text.replace("=", "-").replace("|", "__").replace("/", "-").replace(" ", "-")
def status_style(status: str) -> str:
if status == "PASS":
return "green"
if status == "FAIL":
return "bold red"
if status == "N/A":
return "yellow"
return "white"
def print_line(message: str, style: str | None = None) -> None:
if CONSOLE is None:
print(message)
return
if style:
CONSOLE.print(message, style=style, highlight=False)
else:
CONSOLE.print(message, highlight=False)
def tail_text(text: str | None, max_chars: int = 220) -> str:
if not text:
return ""
normalized = " ".join(text.split())
if len(normalized) <= max_chars:
return normalized
return normalized[-max_chars:]
def run_command(
cmd: list[str],
cwd: Path,
env: dict[str, str],
timeout: int | None = None,
log_path: Path | None = None,
log_section: str | None = None,
) -> subprocess.CompletedProcess[str]:
def _append_log(
*,
command: list[str],
section: str,
returncode: int | None,
stdout: str | None,
stderr: str | None,
timed_out: bool = False,
) -> None:
if log_path is None:
return
log_path.parent.mkdir(parents=True, exist_ok=True)
with log_path.open("a", encoding="utf-8") as f:
f.write(f"\n=== {section} ===\n")
f.write(f"$ {shlex.join(command)}\n")
if timed_out:
f.write("status: timeout\n")
else:
f.write(f"status: exit_code={returncode}\n")
if stdout:
f.write("--- stdout ---\n")
f.write(stdout)
if not stdout.endswith("\n"):
f.write("\n")
if stderr:
f.write("--- stderr ---\n")
f.write(stderr)
if not stderr.endswith("\n"):
f.write("\n")
section = log_section or "command"
try:
proc = subprocess.run(
cmd,
cwd=str(cwd),
env=env,
text=True,
capture_output=True,
check=False,
timeout=timeout,
)
except subprocess.TimeoutExpired as exc:
_append_log(
command=cmd,
section=section,
returncode=None,
stdout=exc.stdout if isinstance(exc.stdout, str) else None,
stderr=exc.stderr if isinstance(exc.stderr, str) else None,
timed_out=True,
)
raise
_append_log(
command=cmd,
section=section,
returncode=proc.returncode,
stdout=proc.stdout,
stderr=proc.stderr,
)
return proc
def detect_gpu_available() -> bool:
try:
proc = subprocess.run(
["nvidia-smi", "-L"],
text=True,
capture_output=True,
check=False,
timeout=10,
)
except (FileNotFoundError, subprocess.TimeoutExpired):
return False
return proc.returncode == 0
def download_sample(repo_root: Path) -> Path:
target = repo_root / SAMPLE_PATH
target.parent.mkdir(parents=True, exist_ok=True)
cmd = [
"curl",
"--fail",
"--location",
"--silent",
"--show-error",
SAMPLE_URL,
"--output",
str(target),
]
proc = run_command(cmd, cwd=repo_root, env=os.environ.copy())
if proc.returncode != 0:
hint = tail_text(proc.stderr or proc.stdout)
raise RuntimeError(f"sample_download_failed: {hint}")
return target
def sync_case_environment(
repo_root: Path,
python_version: str,
row: MatrixRow,
env_dir: Path,
log_path: Path,
) -> tuple[bool, str]:
cmd = ["uv", "sync", "--python", python_version, "--no-dev"]
for extra in row.extras:
cmd.extend(["--extra", extra])
env = os.environ.copy()
env["UV_PROJECT_ENVIRONMENT"] = str(env_dir)
proc = run_command(
cmd,
cwd=repo_root,
env=env,
log_path=log_path,
log_section="sync",
)
if proc.returncode != 0:
return False, tail_text(proc.stderr or proc.stdout)
return True, ""
def apply_expected_failure_policy(result: CaseResult) -> CaseResult:
expected_reason = EXPECTED_FAILURE_CASES.get((result.python_version, result.row_id))
if result.status != "FAIL" or not expected_reason:
return result
override_hint = result.hint
if result.reason:
override_hint = (
f"expected_failure_override original_reason={result.reason}; {override_hint}"
if override_hint
else f"expected_failure_override original_reason={result.reason}"
)
return CaseResult(
python_version=result.python_version,
row_id=result.row_id,
status="N/A",
reason=expected_reason,
duration_sec=result.duration_sec,
hint=override_hint,
log_path=result.log_path,
)
def build_offline_command(
python_version: str,
row: MatrixRow,
sample_audio: Path,
timeout_sec: int,
) -> tuple[list[str], int | None]:
base_cmd = [
"uv",
"run",
"--python",
python_version,
"--no-sync",
"python",
"test_backend_offline.py",
"--backend",
row.backend,
"--policy",
row.policy,
"--audio",
str(sample_audio),
"--model",
"tiny",
"--diarization",
"--diarization-backend",
row.diarization_backend,
"--lan",
"en",
"--no-realtime",
]
if shutil.which("timeout"):
return ["timeout", str(timeout_sec), *base_cmd], None
return base_cmd, timeout_sec
def run_case(
repo_root: Path,
python_version: str,
row: MatrixRow,
sample_audio: Path,
timeout_sec: int,
gpu_available: bool,
logs_dir: Path,
) -> CaseResult:
start = time.monotonic()
case_slug = safe_slug(f"py{python_version}-{row.row_id}")
log_path = logs_dir / f"run-{case_slug}.log"
log_path.parent.mkdir(parents=True, exist_ok=True)
log_path.write_text("", encoding="utf-8")
unsupported_reason = UNSUPPORTED_CASES.get((python_version, row.row_id))
if unsupported_reason:
log_path.write_text(
f"[matrix] precheck_short_circuit status=N/A reason={unsupported_reason}\n",
encoding="utf-8",
)
return CaseResult(
python_version=python_version,
row_id=row.row_id,
status="N/A",
reason=unsupported_reason,
duration_sec=0.0,
hint="unsupported_case_precheck",
log_path=str(log_path),
)
if row.requires_gpu and not gpu_available:
return CaseResult(
python_version=python_version,
row_id=row.row_id,
status="N/A",
reason="gpu_unavailable",
duration_sec=0.0,
hint="nvidia-smi unavailable or failed",
log_path=str(log_path),
)
env_dir = repo_root / ".matrix-envs" / safe_slug(f"py{python_version}-{row.row_id}")
sync_ok, sync_hint = sync_case_environment(
repo_root,
python_version,
row,
env_dir,
log_path=log_path,
)
if not sync_ok:
return CaseResult(
python_version=python_version,
row_id=row.row_id,
status="FAIL",
reason="dependency_sync_failed",
duration_sec=round(time.monotonic() - start, 3),
hint=sync_hint,
log_path=str(log_path),
)
cmd, process_timeout = build_offline_command(
python_version, row, sample_audio, timeout_sec
)
env = os.environ.copy()
env["UV_PROJECT_ENVIRONMENT"] = str(env_dir)
if row.requires_gpu:
env.pop("CUDA_VISIBLE_DEVICES", None)
else:
env["CUDA_VISIBLE_DEVICES"] = ""
try:
proc = run_command(
cmd,
cwd=repo_root,
env=env,
timeout=process_timeout,
log_path=log_path,
log_section="offline",
)
except subprocess.TimeoutExpired as exc:
return CaseResult(
python_version=python_version,
row_id=row.row_id,
status="FAIL",
reason="offline_timeout",
duration_sec=round(time.monotonic() - start, 3),
hint=tail_text((exc.stderr or "") if isinstance(exc.stderr, str) else ""),
log_path=str(log_path),
)
hint = tail_text(proc.stderr or proc.stdout)
if proc.returncode == 0:
return CaseResult(
python_version=python_version,
row_id=row.row_id,
status="PASS",
reason="ok",
duration_sec=round(time.monotonic() - start, 3),
hint=hint,
log_path=str(log_path),
)
reason = "offline_timeout" if proc.returncode == 124 else "offline_run_failed"
return CaseResult(
python_version=python_version,
row_id=row.row_id,
status="FAIL",
reason=reason,
duration_sec=round(time.monotonic() - start, 3),
hint=hint,
log_path=str(log_path),
)
def print_summary(results: list[CaseResult]) -> None:
pass_count = sum(1 for row in results if row.status == "PASS")
fail_count = sum(1 for row in results if row.status == "FAIL")
na_count = sum(1 for row in results if row.status == "N/A")
if CONSOLE is None:
print("\n[matrix] results")
print("python | row | status | reason | duration_s")
print("---|---|---|---|---")
for result in results:
print(
f"{result.python_version} | {result.row_id} | {result.status} | "
f"{result.reason} | {result.duration_sec:.3f}"
)
print(
f"\n[matrix] summary pass={pass_count} fail={fail_count} "
f"na={na_count} total={len(results)}"
)
else:
table = Table(title="Support Matrix Results")
table.add_column("Python", style="cyan", no_wrap=True)
table.add_column("Row", style="white")
table.add_column("Status", no_wrap=True)
table.add_column("Reason")
table.add_column("Duration (s)", justify="right", no_wrap=True)
for result in results:
table.add_row(
result.python_version,
result.row_id,
f"[{status_style(result.status)}]{result.status}[/{status_style(result.status)}]",
result.reason,
f"{result.duration_sec:.3f}",
)
CONSOLE.print()
CONSOLE.print(table)
CONSOLE.print(
f"[bold]Summary[/bold] "
f"pass=[green]{pass_count}[/green] "
f"fail=[bold red]{fail_count}[/bold red] "
f"na=[yellow]{na_count}[/yellow] "
f"total={len(results)}"
)
diagnostics = [row for row in results if row.status in {"FAIL", "N/A"} and row.hint]
if diagnostics:
if CONSOLE is None:
print("\n[matrix] diagnostics (failed/n-a cases)")
for row in diagnostics:
print(
f"- py={row.python_version} row={row.row_id} "
f"status={row.status} reason={row.reason}"
)
print(f" hint: {row.hint}")
if row.log_path:
print(f" log: {row.log_path}")
else:
diagnostics_table = Table(title="Diagnostics (FAIL / N/A)")
diagnostics_table.add_column("Case", style="cyan")
diagnostics_table.add_column("Status", no_wrap=True)
diagnostics_table.add_column("Reason")
diagnostics_table.add_column("Hint")
diagnostics_table.add_column("Log")
for row in diagnostics:
diagnostics_table.add_row(
f"py={row.python_version} {row.row_id}",
f"[{status_style(row.status)}]{row.status}[/{status_style(row.status)}]",
row.reason,
row.hint,
row.log_path,
)
CONSOLE.print()
CONSOLE.print(diagnostics_table)
def main() -> int:
args = parse_args()
if args.timeout_sec <= 0:
print("[matrix] error: --timeout-sec must be > 0", file=sys.stderr)
return 1
repo_root = Path(__file__).resolve().parents[1]
logs_dir = (repo_root / args.logs_dir).resolve()
logs_dir.mkdir(parents=True, exist_ok=True)
print_line(f"[matrix] repo_root={repo_root}", style="cyan")
print_line(f"[matrix] timeout_sec={args.timeout_sec}", style="cyan")
print_line(f"[matrix] logs_dir={logs_dir}", style="cyan")
try:
sample_audio = download_sample(repo_root)
except Exception as exc: # pragma: no cover - straightforward failure path
if CONSOLE is None:
print(f"[matrix] sample_download_failed: {exc}", file=sys.stderr)
else:
CONSOLE.print(
f"[matrix] sample_download_failed: {exc}",
style="bold red",
highlight=False,
)
return 1
print_line(f"[matrix] sample_audio={sample_audio}", style="cyan")
gpu_available = detect_gpu_available()
print_line(f"[matrix] gpu_available={gpu_available}", style="cyan")
results: list[CaseResult] = []
for python_version in PYTHON_VERSIONS:
for row in CASES:
print_line(
f"\n[matrix] running py={python_version} row={row.row_id}", style="blue"
)
result = run_case(
repo_root=repo_root,
python_version=python_version,
row=row,
sample_audio=sample_audio,
timeout_sec=args.timeout_sec,
gpu_available=gpu_available,
logs_dir=logs_dir,
)
result = apply_expected_failure_policy(result)
results.append(result)
print_line(
f"[matrix] {result.status} py={result.python_version} "
f"row={result.row_id} reason={result.reason} duration={result.duration_sec:.3f}s",
style=status_style(result.status),
)
if result.log_path:
print_line(f"[matrix] log={result.log_path}", style="dim")
print_summary(results)
fail_count = sum(1 for row in results if row.status == "FAIL")
return 1 if fail_count else 0
if __name__ == "__main__":
raise SystemExit(main())

803
test_backend_offline.py
View File

@@ -1,803 +0,0 @@
#!/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,
diarization_backend: str = "",
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 diarization_backend:
kwargs["diarization_backend"] = diarization_backend
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."""
def _strip_or_empty(value: object) -> str:
return value.strip() if isinstance(value, str) else ""
segments = response_dict.get("lines", [])
full_text = " ".join(
text
for seg in segments
if isinstance(seg, dict)
for text in [_strip_or_empty(seg.get("text"))]
if text
)
buf = _strip_or_empty(response_dict.get("buffer_transcription"))
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")
buf = buf.strip() if isinstance(buf, str) else ""
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(
"--diarization-backend",
default="",
choices=["diart", "sortformer"],
help="Diarization backend when --diarization is enabled.",
)
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,
diarization_backend=args.diarization_backend,
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()

0
tests/__init__.py
View File

58
tests/conftest.py
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@@ -1,58 +0,0 @@
"""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

209
tests/test_audio_processor.py
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@@ -1,209 +0,0 @@
"""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()

99
tests/test_config.py
View File

@@ -1,99 +0,0 @@
"""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"

172
tests/test_hypothesis_buffer.py
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@@ -1,172 +0,0 @@
"""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

183
tests/test_metrics.py
View File

@@ -1,183 +0,0 @@
"""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)

99
tests/test_silence_handling.py
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@@ -1,99 +0,0 @@
"""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)

185
tests/test_timed_objects.py
View File

@@ -1,185 +0,0 @@
"""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."

6575
uv.lock generated
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File diff suppressed because one or more lines are too long

114
whisperlivekit/audio_processor.py
View File

@@ -9,7 +9,6 @@ import numpy as np
from whisperlivekit.core import (TranscriptionEngine,
online_diarization_factory, online_factory,
online_translation_factory)
from whisperlivekit.metrics_collector import SessionMetrics
from whisperlivekit.ffmpeg_manager import FFmpegManager, FFmpegState
from whisperlivekit.silero_vad_iterator import FixedVADIterator, OnnxWrapper, load_jit_vad
from whisperlivekit.timed_objects import (ASRToken, ChangeSpeaker, FrontData,
@@ -67,8 +66,7 @@ class AudioProcessor:
self.args = models.args
self.sample_rate = 16000
self.channels = 1
chunk_seconds = self.args.vac_chunk_size if self.args.vac else self.args.min_chunk_size
self.samples_per_sec = int(self.sample_rate * chunk_seconds)
self.samples_per_sec = int(self.sample_rate * self.args.min_chunk_size)
self.bytes_per_sample = 2
self.bytes_per_sec = self.samples_per_sec * self.bytes_per_sample
self.max_bytes_per_sec = 32000 * 5 # 5 seconds of audio at 32 kHz
@@ -119,7 +117,6 @@ class AudioProcessor:
self.translation_task: Optional[asyncio.Task] = None
self.watchdog_task: Optional[asyncio.Task] = None
self.all_tasks_for_cleanup: List[asyncio.Task] = []
self.metrics: SessionMetrics = SessionMetrics()
self.transcription: Optional[Any] = None
self.translation: Optional[Any] = None
@@ -141,43 +138,25 @@ class AudioProcessor:
if self.translation_queue:
await self.translation_queue.put(self.current_silence)
async def _begin_silence(self, at_sample: Optional[int] = None) -> None:
async def _begin_silence(self) -> None:
if self.current_silence:
return
# Use audio stream time (sample-precise) for accurate silence duration
if at_sample is not None:
audio_t = at_sample / self.sample_rate
else:
audio_t = self.total_pcm_samples / self.sample_rate if self.sample_rate else 0.0
now = time() - self.beg_loop
self.current_silence = Silence(
is_starting=True, start=audio_t
is_starting=True, start=now
)
# Push a separate start-only event so _end_silence won't mutate it
start_event = Silence(is_starting=True, start=audio_t)
if self.transcription_queue:
await self.transcription_queue.put(start_event)
if self.args.diarization and self.diarization_queue:
await self.diarization_queue.put(start_event)
if self.translation_queue:
await self.translation_queue.put(start_event)
await self._push_silence_event()
async def _end_silence(self, at_sample: Optional[int] = None) -> None:
async def _end_silence(self) -> None:
if not self.current_silence:
return
if at_sample is not None:
audio_t = at_sample / self.sample_rate
else:
audio_t = self.total_pcm_samples / self.sample_rate if self.sample_rate else 0.0
self.current_silence.end = audio_t
self.current_silence.is_starting = False
self.current_silence.has_ended = True
now = time() - self.beg_loop
self.current_silence.end = now
self.current_silence.is_starting=False
self.current_silence.has_ended=True
self.current_silence.compute_duration()
self.metrics.n_silence_events += 1
if self.current_silence.duration is not None:
self.metrics.total_silence_duration_s += self.current_silence.duration
if self.current_silence.duration > MIN_DURATION_REAL_SILENCE:
self.state.new_tokens.append(self.current_silence)
# Push the completed silence as the end event (separate from the start event)
await self._push_silence_event()
self.current_silence = None
@@ -273,34 +252,6 @@ class AudioProcessor:
if self.translation:
await self.translation_queue.put(SENTINEL)
async def _finish_transcription(self) -> None:
"""Call finish() on the online processor to flush remaining tokens."""
if not self.transcription:
return
try:
if hasattr(self.transcription, 'finish'):
final_tokens, end_time = await asyncio.to_thread(self.transcription.finish)
else:
# SimulStreamingOnlineProcessor uses start_silence() → process_iter(is_last=True)
final_tokens, end_time = await asyncio.to_thread(self.transcription.start_silence)
final_tokens = final_tokens or []
if final_tokens:
logger.info(f"Finish flushed {len(final_tokens)} tokens")
_buffer_transcript = self.transcription.get_buffer()
async with self.lock:
self.state.tokens.extend(final_tokens)
self.state.buffer_transcription = _buffer_transcript
self.state.end_buffer = max(self.state.end_buffer, end_time)
self.state.new_tokens.extend(final_tokens)
self.state.new_tokens_buffer = _buffer_transcript
if self.translation_queue:
for token in final_tokens:
await self.translation_queue.put(token)
except Exception as e:
logger.warning(f"Error finishing transcription: {e}")
logger.debug(f"Traceback: {traceback.format_exc()}")
async def transcription_processor(self) -> None:
"""Process audio chunks for transcription."""
cumulative_pcm_duration_stream_time = 0.0
@@ -311,7 +262,6 @@ class AudioProcessor:
item = await get_all_from_queue(self.transcription_queue)
if item is SENTINEL:
logger.debug("Transcription processor received sentinel. Finishing.")
await self._finish_transcription()
break
asr_internal_buffer_duration_s = len(getattr(self.transcription, 'audio_buffer', [])) / self.transcription.SAMPLING_RATE
@@ -346,13 +296,8 @@ class AudioProcessor:
cumulative_pcm_duration_stream_time += len(pcm_array) / self.sample_rate
stream_time_end_of_current_pcm = cumulative_pcm_duration_stream_time
self.transcription.insert_audio_chunk(pcm_array, stream_time_end_of_current_pcm)
_t0 = time()
new_tokens, current_audio_processed_upto = await asyncio.to_thread(self.transcription.process_iter)
_dur = time() - _t0
self.metrics.transcription_durations.append(_dur)
self.metrics.n_transcription_calls += 1
new_tokens = new_tokens or []
self.metrics.n_tokens_produced += len(new_tokens)
_buffer_transcript = self.transcription.get_buffer()
buffer_text = _buffer_transcript.text
@@ -487,7 +432,6 @@ class AudioProcessor:
should_push = (response != self.last_response_content)
if should_push:
self.metrics.n_responses_sent += 1
yield response
self.last_response_content = response
@@ -590,10 +534,6 @@ class AudioProcessor:
logger.warning(f"Error stopping FFmpeg manager: {e}")
if self.diarization:
self.diarization.close()
# Finalize session metrics
self.metrics.total_audio_duration_s = self.total_pcm_samples / self.sample_rate
self.metrics.log_summary()
logger.info("AudioProcessor cleanup complete.")
def _processing_tasks_done(self) -> bool:
@@ -612,7 +552,6 @@ class AudioProcessor:
if not self.beg_loop:
self.beg_loop = time()
self.metrics.session_start = self.beg_loop
self.current_silence = Silence(start=0.0, is_starting=True)
self.tokens_alignment.beg_loop = self.beg_loop
@@ -620,10 +559,6 @@ class AudioProcessor:
logger.info("Empty audio message received, initiating stop sequence.")
self.is_stopping = True
# Flush any remaining PCM data before signaling end-of-stream
if self.is_pcm_input and self.pcm_buffer:
await self._flush_remaining_pcm()
if self.transcription_queue:
await self.transcription_queue.put(SENTINEL)
@@ -636,8 +571,6 @@ class AudioProcessor:
logger.warning("AudioProcessor is stopping. Ignoring incoming audio.")
return
self.metrics.n_chunks_received += 1
if self.is_pcm_input:
self.pcm_buffer.extend(message)
await self.handle_pcm_data()
@@ -654,11 +587,6 @@ class AudioProcessor:
logger.warning("Failed to write audio data to FFmpeg")
async def handle_pcm_data(self) -> None:
# Without VAC, there's no speech detector to end the initial silence.
# Clear it on the first audio chunk so audio actually gets enqueued.
if not self.args.vac and self.current_silence:
await self._end_silence()
# Process when enough data
if len(self.pcm_buffer) < self.bytes_per_sec:
return
@@ -687,7 +615,7 @@ class AudioProcessor:
if res is not None:
if "start" in res and self.current_silence:
await self._end_silence(at_sample=res.get("start"))
await self._end_silence()
if "end" in res and not self.current_silence:
pre_silence_chunk = self._slice_before_silence(
@@ -695,7 +623,7 @@ class AudioProcessor:
)
if pre_silence_chunk is not None and pre_silence_chunk.size > 0:
await self._enqueue_active_audio(pre_silence_chunk)
await self._begin_silence(at_sample=res.get("end"))
await self._begin_silence()
if not self.current_silence:
await self._enqueue_active_audio(pcm_array)
@@ -704,21 +632,3 @@ class AudioProcessor:
if not self.args.transcription and not self.args.diarization:
await asyncio.sleep(0.1)
async def _flush_remaining_pcm(self) -> None:
"""Flush whatever PCM data remains in the buffer, regardless of size threshold."""
if not self.pcm_buffer:
return
aligned_size = (len(self.pcm_buffer) // self.bytes_per_sample) * self.bytes_per_sample
if aligned_size == 0:
return
pcm_array = self.convert_pcm_to_float(self.pcm_buffer[:aligned_size])
self.pcm_buffer = self.pcm_buffer[aligned_size:]
# End any active silence so the audio gets enqueued
if self.current_silence:
await self._end_silence(at_sample=self.total_pcm_samples)
await self._enqueue_active_audio(pcm_array)
self.total_pcm_samples += len(pcm_array)
logger.info(f"Flushed remaining PCM buffer: {len(pcm_array)} samples ({len(pcm_array)/self.sample_rate:.2f}s)")

9
whisperlivekit/backend_support.py
View File

@@ -29,10 +29,11 @@ def mlx_backend_available(warn_on_missing = False):
return available
def voxtral_hf_backend_available():
"""Return True if HF Transformers Voxtral backend is available."""
return module_available("transformers")
def voxmlx_backend_available():
"""Return True if voxmlx (Voxtral MLX backend) is available."""
is_macos = platform.system() == "Darwin"
is_arm = platform.machine() == "arm64"
return is_macos and is_arm and module_available("voxmlx")
def faster_backend_available(warn_on_missing = False):

28
whisperlivekit/basic_server.py
View File

@@ -14,13 +14,15 @@ logging.getLogger().setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
config = parse_args()
args = parse_args()
transcription_engine = None
@asynccontextmanager
async def lifespan(app: FastAPI):
async def lifespan(app: FastAPI):
global transcription_engine
transcription_engine = TranscriptionEngine(config=config)
transcription_engine = TranscriptionEngine(
**vars(args),
)
yield
app = FastAPI(lifespan=lifespan)
@@ -61,7 +63,7 @@ async def websocket_endpoint(websocket: WebSocket):
logger.info("WebSocket connection opened.")
try:
await websocket.send_json({"type": "config", "useAudioWorklet": bool(config.pcm_input)})
await websocket.send_json({"type": "config", "useAudioWorklet": bool(args.pcm_input)})
except Exception as e:
logger.warning(f"Failed to send config to client: {e}")
@@ -101,26 +103,26 @@ def main():
uvicorn_kwargs = {
"app": "whisperlivekit.basic_server:app",
"host": config.host,
"port": config.port,
"host":args.host,
"port":args.port,
"reload": False,
"log_level": "info",
"lifespan": "on",
}
ssl_kwargs = {}
if config.ssl_certfile or config.ssl_keyfile:
if not (config.ssl_certfile and config.ssl_keyfile):
if args.ssl_certfile or args.ssl_keyfile:
if not (args.ssl_certfile and args.ssl_keyfile):
raise ValueError("Both --ssl-certfile and --ssl-keyfile must be specified together.")
ssl_kwargs = {
"ssl_certfile": config.ssl_certfile,
"ssl_keyfile": config.ssl_keyfile,
"ssl_certfile": args.ssl_certfile,
"ssl_keyfile": args.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}
if args.forwarded_allow_ips:
uvicorn_kwargs = { **uvicorn_kwargs, "forwarded_allow_ips" : args.forwarded_allow_ips }
uvicorn.run(**uvicorn_kwargs)

102
whisperlivekit/config.py
View File

@@ -1,102 +0,0 @@
"""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})

219
whisperlivekit/core.py
View File

@@ -2,13 +2,19 @@ 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
def update_with_kwargs(_dict, kwargs):
_dict.update({
k: v for k, v in kwargs.items() if k in _dict
})
return _dict
logger = logging.getLogger(__name__)
class TranscriptionEngine:
@@ -25,51 +31,69 @@ class TranscriptionEngine:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, config=None, **kwargs):
def __init__(self, **kwargs):
# Thread-safe initialization check
with TranscriptionEngine._lock:
if TranscriptionEngine._initialized:
return
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
with TranscriptionEngine._lock:
# Set flag immediately to prevent re-initialization
TranscriptionEngine._initialized = True
def _do_init(self, config=None, **kwargs):
# Handle negated kwargs from programmatic API
# Perform initialization outside lock to avoid holding lock during slow operations
global_params = {
"host": "localhost",
"port": 8000,
"diarization": False,
"punctuation_split": False,
"target_language": "",
"vac": True,
"vac_chunk_size": 0.04,
"log_level": "DEBUG",
"ssl_certfile": None,
"ssl_keyfile": None,
"forwarded_allow_ips": None,
"transcription": True,
"vad": True,
"pcm_input": False,
"disable_punctuation_split" : False,
"diarization_backend": "sortformer",
"backend_policy": "simulstreaming",
"backend": "auto",
}
global_params = update_with_kwargs(global_params, kwargs)
transcription_common_params = {
"warmup_file": None,
"min_chunk_size": 0.1,
"model_size": "base",
"model_cache_dir": None,
"model_dir": None,
"model_path": None,
"lora_path": None,
"lan": "auto",
"direct_english_translation": False,
}
transcription_common_params = update_with_kwargs(transcription_common_params, kwargs)
if transcription_common_params['model_size'].endswith(".en"):
transcription_common_params["lan"] = "en"
if 'no_transcription' in kwargs:
kwargs['transcription'] = not kwargs.pop('no_transcription')
global_params['transcription'] = not global_params['no_transcription']
if 'no_vad' in kwargs:
kwargs['vad'] = not kwargs.pop('no_vad')
global_params['vad'] = not kwargs['no_vad']
if 'no_vac' in kwargs:
kwargs['vac'] = not kwargs.pop('no_vac')
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))
global_params['vac'] = not kwargs['no_vac']
self.args = Namespace(**{**global_params, **transcription_common_params})
self.asr = None
self.tokenizer = None
self.diarization = None
self.vac_session = None
if config.vac:
if self.args.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()
@@ -78,65 +102,51 @@ class TranscriptionEngine:
"onnxruntime not installed. VAC will use JIT model which is loaded per-session. "
"For multi-user scenarios, install onnxruntime: pip install onnxruntime"
)
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 config.transcription:
if config.backend == "voxtral-mlx":
from whisperlivekit.voxtral_mlx_asr import VoxtralMLXASR
backend_policy = self.args.backend_policy
if self.args.transcription:
if self.args.backend == "voxtral-mlx":
from whisperlivekit.voxtral_streaming import VoxtralStreamingASR
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":
self.asr = VoxtralStreamingASR(**transcription_common_params)
logger.info("Using Voxtral MLX streaming backend")
elif 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,
"disable_fast_encoder": False,
"custom_alignment_heads": None,
"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,
}
self.tokenizer = None
simulstreaming_params = update_with_kwargs(simulstreaming_params, kwargs)
self.tokenizer = None
self.asr = SimulStreamingASR(
**transcription_common_params,
**simulstreaming_params,
backend=config.backend,
backend=self.args.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,
"buffer_trimming": "segment",
"confidence_validation": False,
"buffer_trimming_sec": 15,
}
whisperstreaming_params = update_with_kwargs(whisperstreaming_params, kwargs)
self.asr = backend_factory(
backend=config.backend,
backend=self.args.backend,
**transcription_common_params,
**whisperstreaming_params,
)
@@ -145,41 +155,45 @@ class TranscriptionEngine:
getattr(self.asr, "backend_choice", self.asr.__class__.__name__),
)
if config.diarization:
if config.diarization_backend == "diart":
from whisperlivekit.diarization.diart_backend import DiartDiarization
if self.args.diarization:
if self.args.diarization_backend == "diart":
from whisperlivekit.diarization.diart_backend import \
DiartDiarization
diart_params = {
"segmentation_model": "pyannote/segmentation-3.0",
"embedding_model": "pyannote/embedding",
}
diart_params = update_with_kwargs(diart_params, kwargs)
self.diarization_model = DiartDiarization(
block_duration=config.min_chunk_size,
segmentation_model=config.segmentation_model,
embedding_model=config.embedding_model,
block_duration=self.args.min_chunk_size,
**diart_params
)
elif config.diarization_backend == "sortformer":
from whisperlivekit.diarization.sortformer_backend import SortformerDiarization
elif self.args.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')
if self.args.target_language:
if self.args.lan == 'auto' and backend_policy != "simulstreaming":
raise Exception('Translation cannot be set with language auto when transcription backend is not simulstreaming')
else:
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,
)
except:
raise Exception('To use translation, you must install nllw: `pip install nllw`')
translation_params = {
"nllb_backend": "transformers",
"nllb_size": "600M"
}
translation_params = update_with_kwargs(translation_params, kwargs)
self.translation_model = load_model([self.args.lan], **translation_params) #in the future we want to handle different languages for different speakers
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)
from whisperlivekit.voxtral_streaming import VoxtralStreamingOnlineProcessor
return VoxtralStreamingOnlineProcessor(asr)
if args.backend_policy == "simulstreaming":
from whisperlivekit.simul_whisper import SimulStreamingOnlineProcessor
return SimulStreamingOnlineProcessor(asr)
@@ -190,12 +204,11 @@ 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 recommended
elif args.diarization_backend == "sortformer":
if 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

6
whisperlivekit/diarization/diart_backend.py
View File

@@ -1,5 +1,6 @@
import asyncio
import logging
import re
import threading
import time
from queue import Empty, SimpleQueue
@@ -13,11 +14,14 @@ from diart.sources import AudioSource, MicrophoneAudioSource
from pyannote.core import Annotation
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."""

6
whisperlivekit/diarization/sortformer_backend.py
View File

@@ -287,7 +287,11 @@ class SortformerDiarizationOnline:
logger.info(f"Saved {len(concatenated_audio)} samples to diarization_audio.wav")
from whisperlivekit.diarization.utils import extract_number
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
if __name__ == '__main__':

7
whisperlivekit/diarization/utils.py
View File

@@ -1,7 +0,0 @@
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

27
whisperlivekit/local_agreement/backends.py
View File

@@ -26,6 +26,13 @@ class ASRBase:
self.original_language = lan
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, model_size, cache_dir, model_dir):
raise NotImplementedError("must be implemented in the child class")
@@ -180,8 +187,22 @@ class MLXWhisper(ASRBase):
return transcribe
def translate_model_name(self, model_name):
from whisperlivekit.model_mapping import MLX_MODEL_MAPPING
mlx_model_path = MLX_MODEL_MAPPING.get(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)
if mlx_model_path:
return mlx_model_path
else:
@@ -206,6 +227,7 @@ class MLXWhisper(ASRBase):
if segment.get("no_speech_prob", 0) > 0.9:
continue
for word in segment.get("words", []):
probability=word["probability"]
token = ASRToken(word["start"], word["end"], word["word"])
tokens.append(token)
return tokens
@@ -216,7 +238,6 @@ class MLXWhisper(ASRBase):
def use_vad(self):
self.transcribe_kargs["vad_filter"] = True
class OpenaiApiASR(ASRBase):
"""Uses OpenAI's Whisper API for transcription."""
def __init__(self, lan=None, temperature=0, logfile=sys.stderr):

5
whisperlivekit/local_agreement/online_asr.py
View File

@@ -136,11 +136,6 @@ 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)

5
whisperlivekit/local_agreement/whisper_online.py
View File

@@ -1,7 +1,12 @@
#!/usr/bin/env python3
import logging
import platform
import sys
import time
from functools import lru_cache
import librosa
import numpy as np
from whisperlivekit.backend_support import (faster_backend_available,
mlx_backend_available)

156
whisperlivekit/metrics.py
View File

@@ -1,156 +0,0 @@
"""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),
}

84
whisperlivekit/metrics_collector.py
View File

@@ -1,84 +0,0 @@
"""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}")

17
whisperlivekit/model_mapping.py
View File

@@ -1,17 +0,0 @@
"""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",
}

17
whisperlivekit/parse_args.py
View File

@@ -147,8 +147,8 @@ def parse_args():
"--backend",
type=str,
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.",
choices=["auto", "mlx-whisper", "faster-whisper", "whisper", "openai-api", "voxtral-mlx"],
help="Select the Whisper backend implementation (auto: prefer MLX on macOS, otherwise Faster-Whisper, else Whisper). Use 'openai-api' with --backend-policy localagreement to call OpenAI's API. Use 'voxtral-mlx' for Voxtral streaming on Apple Silicon.",
)
parser.add_argument(
"--no-vac",
@@ -318,12 +318,15 @@ def parse_args():
)
args = parser.parse_args()
args.transcription = not args.no_transcription
args.vad = not args.no_vad
args.vac = not args.no_vac
args.vad = not args.no_vad
delattr(args, 'no_transcription')
delattr(args, 'no_vad')
delattr(args, 'no_vac')
from whisperlivekit.config import WhisperLiveKitConfig
return WhisperLiveKitConfig.from_namespace(args)
if args.backend_policy == "1":
args.backend_policy = "simulstreaming"
elif args.backend_policy == "2":
args.backend_policy = "localagreement"
return args

8
whisperlivekit/silero_vad_iterator.py
View File

@@ -115,7 +115,7 @@ class OnnxWrapper():
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)")
raise ValueError()
self._context = x[..., -context_size:]
self._last_sr = sr
@@ -129,7 +129,7 @@ def _get_onnx_model_path(model_path: str = None, opset_version: int = 16) -> Pat
"""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}')
raise Exception(f'Available ONNX opset_version: {available_ops}')
if model_path is None:
current_dir = Path(__file__).parent
@@ -255,8 +255,8 @@ class VADIterator:
if not torch.is_tensor(x):
try:
x = torch.Tensor(x)
except (ValueError, TypeError, RuntimeError) as exc:
raise TypeError("Audio cannot be cast to tensor. Cast it manually") from exc
except:
raise TypeError("Audio cannot be casted to tensor. Cast it manually")
window_size_samples = len(x[0]) if x.dim() == 2 else len(x)
self.current_sample += window_size_samples

552
whisperlivekit/simul_whisper/align_att_base.py
View File

@@ -1,552 +0,0 @@
"""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 = []
self.state.pending_retries = 0
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)
logits = self._apply_dry_penalty(logits, current_tokens)
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:
cleaned = word.replace(replacement_char, "")
if not cleaned.strip():
logger.debug(f"[UTF-8 Filter] Skipping: {repr(word)}")
timestamp_idx += len(word_tokens)
continue
logger.debug(f"[UTF-8 Filter] Cleaned {repr(word)} -> {repr(cleaned)}")
word = cleaned
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."""
MAX_PENDING_TOKENS = 10
MAX_PENDING_RETRIES = 2
replacement_char = "\ufffd"
if split_words and replacement_char in split_words[-1]:
self.state.pending_retries += 1
if self.state.pending_retries > MAX_PENDING_RETRIES:
logger.warning(
f"[UTF-8 Fix] Dropping {len(split_tokens[-1])} incomplete tokens "
f"after {MAX_PENDING_RETRIES} retries (won't resolve)"
)
self.state.pending_incomplete_tokens = []
self.state.pending_retries = 0
elif 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 (retry {self.state.pending_retries})"
)
else:
logger.warning(
f"[UTF-8 Fix] Skipping {len(split_tokens[-1])} tokens "
f"(exceeds limit of {MAX_PENDING_TOKENS}, likely hallucination)"
)
self.state.pending_incomplete_tokens = []
self.state.pending_retries = 0
else:
self.state.pending_incomplete_tokens = []
self.state.pending_retries = 0
# === Repetition penalty ===
def _apply_dry_penalty(self, logits, current_tokens):
"""DRY penalty v0: penalize tokens that would extend a verbatim repetition.
See https://github.com/oobabooga/text-generation-webui/pull/5677
Scans the decoded sequence for positions where the current suffix already
appeared --> for each such match, the token that followed it in the past is
penalised exponentially with the match length
"""
eot = self.tokenizer.eot
seq = current_tokens[0].tolist()
if len(seq) < 5:
return logits
last = seq[-1]
if last >= eot:
return logits
penalties = {}
for i in range(len(seq) - 2, -1, -1):
if seq[i] != last:
continue
next_tok = seq[i + 1]
if next_tok >= eot:
continue
length = 1
while length < 50:
j, k = i - length, len(seq) - 1 - length
if j < 0 or k <= i:
break
if seq[j] != seq[k] or seq[j] >= eot:
break
length += 1
if next_tok not in penalties or length > penalties[next_tok]:
penalties[next_tok] = length
if penalties:
max_len = max(penalties.values())
if max_len >= 4:
logger.debug(f"[DRY] penalising {len(penalties)} tokens (longest match: {max_len})")
for tok, length in penalties.items():
if length >= 2:
logits[:, tok] = logits[:, tok] - 1.0 * 2.0 ** (length - 2)
return logits
# === 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)."""
...

11
whisperlivekit/simul_whisper/backend.py
View File

@@ -200,12 +200,9 @@ class SimulStreamingASR:
if self.encoder_backend == "whisper":
self.disable_fast_encoder = True
# MLX full decoder disabled by default — MLXAlignAtt has known issues
# with token generation after punctuation. Users can opt-in with
# --use-full-mlx if they want to test it.
# if self.encoder_backend == "mlx-whisper" and platform.system() == "Darwin":
# if not hasattr(self, '_full_mlx_disabled'):
# self.use_full_mlx = 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(
tokenizer_is_multilingual= is_multilingual,
@@ -353,7 +350,7 @@ class SimulStreamingASR:
def set_translate_task(self):
"""Set up translation task."""
if self.cfg.language == 'auto':
raise ValueError('Translation cannot be done with language = auto')
raise Exception('Translation cannot be done with language = auto')
return tokenizer.get_tokenizer(
multilingual=True,
language=self.cfg.language,

6
whisperlivekit/simul_whisper/decoder_state.py
View File

@@ -25,8 +25,7 @@ class DecoderState:
context: Any = None
pending_incomplete_tokens: List[int] = field(default_factory=list)
pending_retries: int = 0
global_time_offset: float = 0.0
cumulative_time_offset: float = 0.0
first_timestamp: Optional[float] = None
@@ -79,9 +78,8 @@ class DecoderState:
self.last_attend_frame = -rewind_threshold
self.cumulative_time_offset = 0.0
self.pending_incomplete_tokens = []
self.pending_retries = 0
self.log_segments += 1
def full_reset(self, rewind_threshold: int = 200):
"""
Full reset including audio segments and tokens.

6
whisperlivekit/simul_whisper/mlx/decoder_state.py
View File

@@ -29,8 +29,7 @@ class MLXDecoderState:
context: Any = None
pending_incomplete_tokens: List[int] = field(default_factory=list)
pending_retries: int = 0
global_time_offset: float = 0.0
cumulative_time_offset: float = 0.0
first_timestamp: Optional[float] = None
@@ -60,9 +59,8 @@ class MLXDecoderState:
self.last_attend_frame = -rewind_threshold
self.cumulative_time_offset = 0.0
self.pending_incomplete_tokens = []
self.pending_retries = 0
self.log_segments += 1
def full_reset(self, rewind_threshold: int = 200):
"""
Full reset including audio segments and tokens.

609
whisperlivekit/simul_whisper/mlx/simul_whisper.py
View File

@@ -1,6 +1,9 @@
"""MLX whisper AlignAtt streaming decoder."""
"""
MLX whisper AlignAtt streaming decoder
"""
import logging
from typing import Any, List, Tuple
from time import time
from typing import Any, List, Optional, Tuple
import mlx.core as mx
import numpy as np
@@ -8,18 +11,19 @@ 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.timed_objects import ASRToken
from whisperlivekit.whisper import DecodingOptions, tokenizer
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
DEC_PAD = 50257
logger = logging.getLogger(__name__)
class MLXTokenBuffer:
class MLXTokenBuffer: #should try to make it heritate from classic simul whisper class
"""Token buffer for MLX-based decoding."""
def __init__(self, text="", tokenizer=None, prefix_token_ids=None):
@@ -36,10 +40,12 @@ class MLXTokenBuffer:
return self.prefix_token_ids + tokenizer.encode(self.text)
def as_mlx_array(self) -> mx.array:
"""Return tokens as MLX 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:
"""Return tokens as MLX array repeated for beam search."""
t = self.as_mlx_array()
return mx.repeat(t, beam, axis=0)
@@ -58,8 +64,10 @@ class MLXTokenBuffer:
return self.text is None or self.text == ""
def trim_words(self, num=1, after=0):
"""Trim words from the beginning of the context."""
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:
@@ -68,11 +76,14 @@ class MLXTokenBuffer:
return sum(len(wi) for wi in wids[:num])
def append_token_ids(self, token_ids):
"""Append token IDs to the buffer, handling incomplete UTF-8."""
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])
@@ -89,47 +100,106 @@ class MLXTokenBuffer:
def mlx_median_filter(x: mx.array, filter_width: int) -> mx.array:
"""Apply median filter along the last axis."""
"""
Apply median filter along the last axis.
Args:
x: Input array of shape (..., T)
filter_width: Width of the median filter (should be odd)
Returns:
Filtered array of same shape
"""
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_shape = list(shape)
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):
class MLXAlignAtt:
"""
MLX-native Alignment-based Attention decoder for SimulStreaming.
Runs entirely on MLX, with no PyTorch dependencies for inference.
This class runs entirely on MLX, with no PyTorch dependencies for inference.
"""
@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
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)
"""
Initialize MLX AlignAtt decoder.
Args:
cfg: AlignAtt configuration
mlx_model: MLX Whisper model (full model, not just encoder)
"""
self.model = mlx_model
self.cfg = cfg
logger.info(f"MLX Model dimensions: {self.model.dims}")
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 = self.model.dims.n_text_ctx
self.num_decoder_layers = len(self.model.decoder.blocks)
# Per-session state
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
# Initialize per-session state
self.state = MLXDecoderState()
self._init_state(cfg)
def _init_state(self, cfg: AlignAttConfig):
self._init_state_common(cfg)
"""Initialize the per-session decoder 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
# 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
@@ -138,20 +208,19 @@ class MLXAlignAtt(AlignAttBase):
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"
)
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,
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)
@@ -161,34 +230,35 @@ class MLXAlignAtt(AlignAttBase):
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.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,
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)
@@ -197,23 +267,31 @@ class MLXAlignAtt(AlignAttBase):
self.state.align_source[layer_rank] = heads
self.state.num_align_heads += 1
# === Abstract method implementations ===
def warmup(self, audio: np.ndarray):
"""Warmup the model with sample audio."""
try:
self.insert_audio(audio)
self.infer(is_last=True)
self.refresh_segment(complete=True)
logger.info("MLX model warmed up successfully")
except Exception as e:
logger.exception(f"MLX model warmup failed: {e}")
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,
def create_tokenizer(self, language=None):
"""Create tokenizer for the given language."""
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.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]
self.state.tokenizer = self.tokenizer
def init_context(self):
"""Initialize context buffer."""
kw = {
'tokenizer': self.tokenizer,
'prefix_token_ids': [self.tokenizer.sot_prev],
'prefix_token_ids': [self.tokenizer.sot_prev]
}
self.state.context = MLXTokenBuffer.empty(**kw)
if self.cfg.static_init_prompt is not None:
@@ -221,138 +299,409 @@ class MLXAlignAtt(AlignAttBase):
if self.cfg.init_prompt is not None:
self.state.context.text += self.cfg.init_prompt
def insert_audio(self, segment=None):
def init_tokens(self):
"""Initialize token sequence."""
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 trim_context(self):
"""Trim context if too long."""
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
if self.cfg.static_init_prompt is None:
after = 0
else:
after = 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):
"""Refresh segment state."""
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 fire_at_boundary(self, chunked_encoder_feature: mx.array) -> bool:
"""Check if we should fire at word boundary (CIF-based)."""
if self.state.always_fire:
return True
if self.state.never_fire:
return False
return True
def _current_tokens(self) -> mx.array:
"""Get current token sequence for decoding."""
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
# Concatenate all tokens
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 debug_print_tokens(self, tokens: mx.array):
"""Debug print token sequences."""
tokens_np = np.array(tokens)
for i in range(min(self.cfg.beam_size, tokens_np.shape[0])):
logger.debug(self.tokenizer.decode_with_timestamps(tokens_np[i].tolist()))
def segments_len(self) -> float:
"""Get total length of audio segments in seconds."""
return sum(s.shape[0] for s in self.state.segments) / 16000
def _apply_minseglen(self) -> bool:
"""Check if we have enough audio to process."""
segments_len = self.segments_len()
if segments_len < self.cfg.audio_min_len:
logger.debug("waiting for next segment")
return False
return True
def insert_audio(self, segment: np.ndarray = None):
"""Insert audio segment into buffer."""
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"
)
logger.debug(f"remove segments: {len(self.state.segments)} {len(self.state.tokens)}, cumulative offset: {self.state.cumulative_time_offset:.2f}s")
if len(self.state.tokens) > 1:
# Convert MLX array to list for context
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 _clean_cache(self):
"""Clean the kv_cache after each inference step."""
self.state.clean_cache()
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 _suppress_tokens(self, logits: mx.array) -> mx.array:
"""Apply token suppression to 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 lang_id(self, encoder_features: mx.array) -> Tuple[mx.array, List[dict]]:
"""Language detection from encoder features."""
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,
)
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 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 infer(self, is_last: bool = False) -> List[ASRToken]:
"""
Main inference method.
Args:
is_last: Whether this is the final chunk
Returns:
List of timestamped ASR tokens
"""
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 []
def _encode(self, input_segments):
if len(self.state.segments) > 1:
input_segments = np.concatenate(self.state.segments, axis=0)
else:
input_segments = self.state.segments[0]
beg_encode = time()
mlx_mel_padded = mlx_log_mel_spectrogram(
audio=input_segments,
n_mels=self.model.dims.n_mels,
padding=N_SAMPLES,
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
mx.eval(encoder_feature)
end_encode = time()
logger.debug(f'MLX Encoder duration: {end_encode - beg_encode:.3f}s')
def _init_sum_logprobs(self):
return mx.zeros((self.cfg.beam_size,), dtype=mx.float32)
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}")
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,
self.trim_context()
current_tokens = self._current_tokens()
fire_detected = self.fire_at_boundary(encoder_feature[:, :content_mel_len, :])
sum_logprobs = mx.zeros((self.cfg.beam_size,), dtype=mx.float32)
completed = False
attn_of_alignment_heads = None
most_attended_frame = None
token_len_before_decoding = current_tokens.shape[1]
l_absolute_timestamps = []
accumulated_cross_attns = []
audio_duration_s = self.segments_len()
# ~15 text tokens/s is a generous upper bound for speech; TOKENS_PER_SECOND (50)
# is the mel-frame rate and was causing 10-40x over-allocation on repetition loops.
max_tokens_per_chunk = max(50, int(audio_duration_s * 15 * 1.5))
tokens_produced_this_chunk = 0
while not completed and current_tokens.shape[1] < self.max_text_len:
tokens_produced_this_chunk += 1
if tokens_produced_this_chunk > max_tokens_per_chunk:
logger.warning(f"[Loop Detection] Too many tokens ({tokens_produced_this_chunk}) for {audio_duration_s:.2f}s audio. Breaking.")
current_tokens = current_tokens[:, :token_len_before_decoding]
break
if new_segment:
tokens_for_logits = current_tokens
else:
tokens_for_logits = current_tokens[:, -1:]
if self.state.decoder_type == "greedy":
logits, self.state.kv_cache, cross_qk = self.model.decoder(
tokens_for_logits, encoder_feature, kv_cache=self.state.kv_cache
)
else:
logits, cross_qk = self.state.inference.logits(tokens_for_logits, encoder_feature)
mx.eval(logits)
accumulated_cross_attns.append(cross_qk)
if len(accumulated_cross_attns) > 16:
accumulated_cross_attns = accumulated_cross_attns[-16:]
if new_segment and 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")
break
logits = logits[:, -1, :] # Last token logits
# Suppress tokens at segment start
if new_segment:
blank_tokens = self.tokenizer.encode(" ") + [self.tokenizer.eot]
logits = logits.at[:, blank_tokens].add(-float('inf'))
new_segment = False
logits = self._suppress_tokens(logits)
current_tokens, completed = self.state.token_decoder.update(
current_tokens, logits, sum_logprobs
)
return logits, cross_qk
mx.eval(current_tokens)
logger.debug(f"Decoding completed: {completed}")
self.debug_print_tokens(current_tokens)
attn_of_alignment_heads = self._process_cross_attention(
accumulated_cross_attns, content_mel_len
)
most_attended_frames = mx.argmax(attn_of_alignment_heads[:, -1, :], axis=-1)
most_attended_frames_np = np.array(most_attended_frames)
absolute_timestamps = [
(frame * 0.02 + self.state.cumulative_time_offset)
for frame in most_attended_frames_np.tolist()
]
logger.debug(str(most_attended_frames_np.tolist()) + " most att frames")
logger.debug(f"Absolute timestamps: {absolute_timestamps}")
most_attended_frame = int(most_attended_frames_np[0])
l_absolute_timestamps.append(absolute_timestamps[0])
if completed:
current_tokens = current_tokens[:, :-1]
break
if not is_last and self.state.last_attend_frame - most_attended_frame > self.cfg.rewind_threshold:
current_tokens_np = np.array(current_tokens)
if current_tokens.shape[1] > 1 and current_tokens_np[0, -2] >= DEC_PAD:
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}, last: {self.state.last_attend_frame}")
self.state.last_attend_frame = -self.cfg.rewind_threshold
current_tokens = mx.concatenate(self.state.tokens, axis=1) if len(self.state.tokens) > 0 else self.state.tokens[0]
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
tokens_to_split = np.array(current_tokens[0, token_len_before_decoding:]).tolist()
if self.state.pending_incomplete_tokens:
logger.debug(f"[UTF-8 Fix] Prepending pending tokens: {self.state.pending_incomplete_tokens}")
tokens_to_split = self.state.pending_incomplete_tokens + tokens_to_split
if fire_detected or is_last:
new_hypothesis = tokens_to_split
split_words, split_tokens = self.tokenizer.split_to_word_tokens(new_hypothesis)
else:
return self.state.inference.logits(tokens, encoder_feature)
split_words, split_tokens = self.tokenizer.split_to_word_tokens(tokens_to_split)
if len(split_words) > 1:
new_hypothesis = [i for sublist in split_tokens[:-1] for i in sublist]
else:
new_hypothesis = []
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
logger.debug(f"new_hypothesis: {new_hypothesis}")
new_tokens = mx.array([new_hypothesis], dtype=mx.int32)
new_tokens = mx.repeat(new_tokens, self.cfg.beam_size, axis=0)
self.state.tokens.append(new_tokens)
def _suppress_blank_tokens(self, logits):
blank_tokens = self.tokenizer.encode(" ") + [self.tokenizer.eot]
logits = logits.at[:, blank_tokens].add(-float('inf'))
return logits
logger.info(f"Output: {self.tokenizer.decode(new_hypothesis)}")
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
self._clean_cache()
def _update_tokens(self, current_tokens, logits, sum_logprobs):
return self.state.token_decoder.update(current_tokens, logits, sum_logprobs)
if len(l_absolute_timestamps) >= 2 and self.state.first_timestamp is None:
self.state.first_timestamp = l_absolute_timestamps[0]
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:
pass
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)
self.state.pending_incomplete_tokens = []
MAX_PENDING_TOKENS = 10
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 incomplete tokens")
else:
logger.warning(f"[UTF-8 Fix] Skipping too many tokens")
return timestamped_words
def _process_cross_attention(
self, cross_attns: List, content_mel_len: int,
self,
cross_attns: List[List[mx.array]],
content_mel_len: int
) -> mx.array:
"""
Process cross-attention weights for alignment.
Args:
cross_attns: List of cross-attention from each forward pass
Each element is a list of mx.arrays per layer
content_mel_len: Length of actual audio content
Returns:
Processed attention tensor, shape (batch, seq_len, content_mel_len)
"""
attn_of_alignment_heads = [[] for _ in range(self.state.num_align_heads)]
num_decoder_layers = self.num_decoder_layers
@@ -364,11 +713,14 @@ class MLXAlignAtt(AlignAttBase):
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:
if len(align_heads_in_layer) == 0:
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:
@@ -379,43 +731,26 @@ class MLXAlignAtt(AlignAttBase):
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))
t = mx.concatenate(mat, axis=1)
tmp.append(t)
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)

18
whisperlivekit/simul_whisper/mlx_encoder.py
View File

@@ -7,9 +7,21 @@ from huggingface_hub import snapshot_download
from mlx.utils import tree_unflatten
from mlx_whisper import whisper
from whisperlivekit.model_mapping import MLX_MODEL_MAPPING
mlx_model_mapping = MLX_MODEL_MAPPING
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",
}
def load_mlx_encoder(
path_or_hf_repo: str,

702
whisperlivekit/simul_whisper/simul_whisper.py
View File

@@ -1,6 +1,7 @@
import logging
import os
from typing import List
from time import time
from typing import List, Optional, Tuple
import numpy as np
import torch
@@ -8,6 +9,8 @@ import torch.nn.functional as F
from whisperlivekit.backend_support import (faster_backend_available,
mlx_backend_available)
from whisperlivekit.timed_objects import ASRToken
from whisperlivekit.whisper import DecodingOptions, tokenizer
from whisperlivekit.whisper.audio import (N_FRAMES, N_SAMPLES,
TOKENS_PER_SECOND,
log_mel_spectrogram, pad_or_trim)
@@ -15,13 +18,14 @@ from whisperlivekit.whisper.decoding import (BeamSearchDecoder, GreedyDecoder,
SuppressTokens)
from whisperlivekit.whisper.timing import median_filter
from .align_att_base import DEC_PAD, AlignAttBase
from ..timed_objects import PUNCTUATION_MARKS
from .beam import BeamPyTorchInference
from .config import AlignAttConfig
from .decoder_state import DecoderState
from .eow_detection import fire_at_boundary, load_cif
from .token_buffer import TokenBuffer
DEC_PAD = 50257
logger = logging.getLogger(__name__)
if mlx_backend_available():
@@ -42,10 +46,7 @@ def load_coreml_encoder():
except ImportError:
logger.warning("coremltools is not installed")
return None
COREML_ENCODER_PATH = os.environ.get(
"MLCORE_ENCODER_PATH",
"whisperlivekit/whisper/whisper_encoder.mlpackage",
)
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"
@@ -53,50 +54,92 @@ def load_coreml_encoder():
return _coreml_encoder, _coreml_input_name, _coreml_output_name
class AlignAtt(AlignAttBase):
class AlignAtt:
"""
PyTorch Alignment-based Attention decoder for SimulStreaming.
Hookless — the model can be shared across multiple sessions,
with each session maintaining its own DecoderState.
Alignment-based Attention decoder for SimulStreaming.
This class is now hookless - the model can be shared across multiple
sessions, with each session maintaining its own DecoderState.
"""
# Property accessors for backward compatibility
@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
def __init__(
self,
cfg: AlignAttConfig,
loaded_model=None,
mlx_encoder=None,
fw_encoder=None,
) -> None:
self,
cfg: AlignAttConfig,
loaded_model=None,
mlx_encoder=None,
fw_encoder=None,
) -> None:
# Shared model reference (can be shared across sessions)
self.model = loaded_model
self.mlx_encoder = mlx_encoder
self.fw_encoder = fw_encoder
self.fw_encoder = fw_encoder
if fw_encoder:
self.fw_feature_extractor = FeatureExtractor(
feature_size=loaded_model.dims.n_mels,
)
self.fw_feature_extractor = FeatureExtractor(feature_size=self.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
)
self.tokenizer_is_multilingual = cfg.tokenizer_is_multilingual
self.max_text_len = self.model.dims.n_text_ctx
self.num_decoder_layers = len(self.model.decoder.blocks)
self.cfg = cfg
# Per-session state
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
# Initialize per-session state
self.state = DecoderState()
self._init_state(cfg)
def _init_state(self, cfg: AlignAttConfig):
self._init_state_common(cfg)
"""Initialize the per-session decoder state."""
# Create tokenizer
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
# Timing state
self.state.global_time_offset = 0.0
self.state.last_attend_frame = -cfg.rewind_threshold
self.state.speaker = -1
# 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,
cfg,
n_audio_state=self.model.dims.n_audio_state,
device=self.model.device
)
# Build alignment source mapping
# Build alignment source mapping from model's alignment_heads
self.state.align_source = {}
self.state.num_align_heads = 0
for layer_rank, head_id in self.model.alignment_heads.indices().T:
@@ -108,9 +151,12 @@ class AlignAtt(AlignAttBase):
# 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.no_timestamps,
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)
@@ -119,80 +165,138 @@ class AlignAtt(AlignAttBase):
sup_tokens = SuppressTokens(suppress_tokens)
self.state.suppress_tokens_fn = lambda logits: sup_tokens.apply(logits, None)
# Initialize tokens
self.init_tokens()
self.init_context()
# Decoder type
# Set up decoder type
self.state.decoder_type = cfg.decoder_type
if cfg.decoder_type == "greedy":
logger.info("Using greedy decoder")
self.state.token_decoder = GreedyDecoder(0.0, self.tokenizer.eot)
elif cfg.decoder_type == "beam":
logger.info("Using beam decoder")
self.state.inference = BeamPyTorchInference(
self.model, self.state.initial_token_length,
)
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,
inference=self.state.inference,
eot=self.tokenizer.eot,
beam_size=cfg.beam_size
)
# === Abstract method implementations ===
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 init_tokens(self):
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 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 init_context(self):
kw = {
'tokenizer': self.tokenizer,
'device': self.model.device,
'prefix_token_ids': [self.tokenizer.sot_prev],
}
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"
def init_tokens(self):
logger.debug(f"init tokens, {len(self.state.segments)}")
# init tokens (mandatory prompt)
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.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
if self.cfg.static_init_prompt is None:
after = 0
else:
after = 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 logits(
self,
tokens: torch.Tensor,
audio_features: torch.Tensor,
return_cross_attn: bool = False
):
"""Get logits from decoder, optionally returning cross-attention weights."""
if self.state.decoder_type == "greedy":
return self.model.decoder(
tokens, audio_features,
kv_cache=self.state.kv_cache,
return_cross_attn=return_cross_attn
)
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
else:
logger.debug(f"Logits shape: {tokens.shape}")
return self.state.inference.logits(
tokens, audio_features,
return_cross_attn=return_cross_attn
)
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 fire_at_boundary(self, chunked_encoder_feature: torch.Tensor):
if self.state.always_fire:
return True
if self.state.never_fire:
return False
return fire_at_boundary(chunked_encoder_feature, self.state.CIFLinear)
def _current_tokens(self):
toks = self.state.tokens
# very first infer: duplicate start of seq to beam_size
if toks[0].shape[0] == 1:
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,
)
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:
@@ -201,19 +305,60 @@ class AlignAtt(AlignAttBase):
self.debug_print_tokens(current_tokens)
return current_tokens
def fire_at_boundary(self, chunked_encoder_feature: torch.Tensor):
if self.state.always_fire:
return True
if self.state.never_fire:
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.state.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")
return False
return fire_at_boundary(chunked_encoder_feature, self.state.CIFLinear)
return True
def insert_audio(self, segment=None):
if segment is not None:
self.state.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.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 # Track cumulative time removed
self.state.segments = self.state.segments[1:]
logger.debug(f"remove segments: {len(self.state.segments)} {len(self.state.tokens)}, 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 _clean_cache(self):
"""Clean the kv_cache after each inference step."""
self.state.clean_cache()
@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)
x = torch.tensor([[self.tokenizer.sot]] * n_audio).to(self.model.device) # [n_audio, 1]
# Note: don't use kv_cache for language detection
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
@@ -222,31 +367,45 @@ class AlignAtt(AlignAttBase):
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
def _concat_segments(self):
if len(self.state.segments) > 1:
return torch.cat(self.state.segments, dim=0)
return self.state.segments[0]
### transcription / translation
def _encode(self, input_segments):
@torch.no_grad()
def infer(self, is_last=False):
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 is concatenation of audio, it's one array
if len(self.state.segments) > 1:
input_segments = torch.cat(self.state.segments, dim=0)
else:
input_segments = self.state.segments[0]
beg_encode = time()
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",
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)
@@ -258,151 +417,310 @@ class AlignAtt(AlignAttBase):
else:
encoder_feature_np = next(iter(coreml_outputs.values()))
encoder_feature = torch.as_tensor(
np.array(encoder_feature_np), device=self.device,
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_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)
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, :]
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':
if self.device == 'cpu': #it seems that on gpu, passing StorageView to torch.as_tensor fails and wrapping in the array works
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)
except TypeError: # Normally the cpu condition should prevent having exceptions, but just in case:
encoder_feature = torch.as_tensor(np.array(encoder_feature_ctranslate), 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 + padding to 30s
mel_padded = log_mel_spectrogram(input_segments, n_mels=self.model.dims.n_mels, padding=N_SAMPLES,
device=self.device).unsqueeze(0)
# trim to 3000
mel = pad_or_trim(mel_padded, N_FRAMES)
content_mel_len = int((mel_padded.shape[2] - mel.shape[2]) / 2)
# the len of actual audio
content_mel_len = int((mel_padded.shape[2] - mel.shape[2])/2)
encoder_feature = self.model.encoder(mel)
return encoder_feature, content_mel_len
end_encode = time()
# print('Encoder duration:', end_encode-beg_encode)
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}, {self.tokenizer.sot_sequence_including_notimestamps}")
def _init_sum_logprobs(self):
return torch.zeros(self.cfg.beam_size, device=self.device)
self.trim_context()
current_tokens = self._current_tokens()
fire_detected = self.fire_at_boundary(encoder_feature[:, :content_mel_len, :])
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,
)
sum_logprobs = torch.zeros(self.cfg.beam_size, device=self.device)
completed = False
# punctuation_stop = False
attn_of_alignment_heads = None
most_attended_frame = None
token_len_before_decoding = current_tokens.shape[1]
l_absolute_timestamps = []
accumulated_cross_attns = []
audio_duration_s = self.segments_len()
# ~15 text tokens/s is a generous upper bound for speech; TOKENS_PER_SECOND (50)
# is the mel-frame rate and was causing 10-40x over-allocation on repetition loops.
max_tokens_per_chunk = max(50, int(audio_duration_s * 15 * 1.5))
tokens_produced_this_chunk = 0
while not completed and current_tokens.shape[1] < self.max_text_len: # bos is 3 tokens
tokens_produced_this_chunk += 1
if tokens_produced_this_chunk > max_tokens_per_chunk:
logger.warning(f"[Loop Detection] Too many tokens ({tokens_produced_this_chunk}) for {audio_duration_s:.2f}s audio. Breaking.")
current_tokens = current_tokens[:, :token_len_before_decoding] # Discard all new tokens
break
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:]
# Get logits and cross-attention weights from decoder
result = self.logits(tokens_for_logits, encoder_feature, return_cross_attn=True)
logits, cross_attns = result
# Accumulate cross-attention from this forward pass (rolling window to
# bound VRAM — only the last entry matters for alignment, and the
# median_filter kernel is 7, so 16 entries is more than enough).
accumulated_cross_attns.append(cross_attns)
if len(accumulated_cross_attns) > 16:
accumulated_cross_attns = accumulated_cross_attns[-16:]
if new_segment and 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")
break
logits = logits[:, -1, :] # logits for the last token
# suppress 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.state.suppress_tokens_fn(logits)
current_tokens, completed = self.state.token_decoder.update(current_tokens, logits, sum_logprobs)
logger.debug(f"Decoding completed: {completed}, sum_logprobs: {sum_logprobs.tolist()}, tokens: ")
self.debug_print_tokens(current_tokens)
# Process accumulated cross-attention weights for alignment
attn_of_alignment_heads = self._process_cross_attention(accumulated_cross_attns, content_mel_len)
# for each beam, the most attended frame is:
most_attended_frames = torch.argmax(attn_of_alignment_heads[:, -1, :], dim=-1)
# Calculate absolute timestamps accounting for cumulative offset
absolute_timestamps = [
(frame * 0.02 + self.state.cumulative_time_offset)
for frame in most_attended_frames.tolist()
]
logger.debug(str(most_attended_frames.tolist()) + " most att frames")
logger.debug(f"Absolute timestamps: {absolute_timestamps} (offset: {self.state.cumulative_time_offset:.2f}s)")
most_attended_frame = most_attended_frames[0].item()
l_absolute_timestamps.append(absolute_timestamps[0])
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.state.last_attend_frame - most_attended_frame > self.cfg.rewind_threshold:
if current_tokens.shape[1] > 1 and current_tokens[0, -2] >= DEC_PAD:
logger.debug("omit rewinding from special tokens")
self.state.last_attend_frame = most_attended_frame
else:
logger.debug(
f"[rewind detected] current attention pos: {most_attended_frame}, "
f"last attention pos: {self.state.last_attend_frame}; omit this segment")
self.state.last_attend_frame = -self.cfg.rewind_threshold
current_tokens = torch.cat(self.state.tokens, dim=1) if len(self.state.tokens) > 0 else self.state.tokens[0]
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}")
# 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()])
))
tokens_to_split = current_tokens[0, token_len_before_decoding:]
# Prepend pending tokens from previous chunk if any
if self.state.pending_incomplete_tokens:
logger.debug(f"[UTF-8 Fix] Prepending {len(self.state.pending_incomplete_tokens)} pending tokens: {self.state.pending_incomplete_tokens}")
pending_tensor = torch.tensor(self.state.pending_incomplete_tokens, dtype=torch.long, device=self.device)
tokens_to_split = torch.cat([pending_tensor, tokens_to_split])
if fire_detected or is_last:
new_hypothesis = tokens_to_split.flatten().tolist()
split_words, split_tokens = self.tokenizer.split_to_word_tokens(new_hypothesis)
else:
logger.debug(f"Logits shape: {tokens.shape}")
return self.state.inference.logits(
tokens, encoder_feature, return_cross_attn=True,
# going to truncate the tokens after the last space
split_words, split_tokens = self.tokenizer.split_to_word_tokens(tokens_to_split.tolist())
if len(split_words) > 1:
new_hypothesis = [i for sublist in split_tokens[:-1] for i in sublist]
else:
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.device,
)
self.state.tokens.append(new_tokens)
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 = []
timestamp_idx = 0
replacement_char = "\ufffd"
for word, word_tokens in zip(split_words, split_tokens):
# Skip words containing incomplete UTF-8 from client output
if replacement_char in word:
logger.warning(f"[UTF-8 Filter] Skipping incomplete word from client output: {repr(word)}")
timestamp_idx += len(word_tokens)
continue
try:
current_timestamp = l_absolute_timestamps[timestamp_idx]
except IndexError:
# Use last timestamp if index out of range
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)
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
# Hold incomplete tokens for next chunk (with limit to prevent hallucination accumulation)
self.state.pending_incomplete_tokens = []
MAX_PENDING_TOKENS = 10 # Real incomplete UTF-8 chars are at most a few tokens
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)} incomplete tokens for next chunk")
else:
logger.warning(f"[UTF-8 Fix] Skipping {len(split_tokens[-1])} tokens (exceeds limit of {MAX_PENDING_TOKENS}, likely hallucination)")
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)
return timestamped_words
def _process_cross_attention(
self, cross_attns: List, content_mel_len: int,
self,
cross_attns: List[torch.Tensor],
content_mel_len: int
) -> torch.Tensor:
"""
Process cross-attention weights from decoder layers for alignment.
Args:
cross_attns: List of cross-attention tensors from each decoder layer.
Each tensor has shape (batch, n_head, seq_len, audio_len)
content_mel_len: Length of actual audio content in mel frames
Returns processed attention tensor for alignment, shape (batch, seq_len, content_mel_len)
"""
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]
flattened_attns: List[torch.Tensor] = [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
# attn_mat shape: (batch, n_head, seq_len, audio_len) or (n_head, seq_len, audio_len) for batch=1
align_heads_in_layer = self.state.align_source.get(layer_rank, [])
if not align_heads_in_layer:
if len(align_heads_in_layer) == 0:
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:
# (n_head, seq_len, audio_len) when squeezed
if attn_mat.dim() == 4:
a = attn_mat[0, head_id, :, :]
a = attn_mat[0, head_id, :, :] # (seq_len, audio_len)
else:
a = attn_mat[head_id, :, :]
a = a.unsqueeze(0)
a = a.unsqueeze(0) # (1, seq_len, audio_len)
else:
a = attn_mat[:, head_id, :, :]
# attn_mat: (batch, n_head, seq_len, audio_len)
a = attn_mat[:, head_id, :, :] # (batch, seq_len, audio_len)
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))
t = torch.cat(mat, dim=1) # (batch, total_seq_len, audio_len)
tmp.append(t)
if not tmp:
return torch.zeros(self.cfg.beam_size, 1, content_mel_len, device=self.device)
# stck al heads: (batch, num_align_heads, seq_len, audio_len)
attn_of_alignment_heads = torch.stack(tmp, dim=1)
std, mean = torch.std_mean(
attn_of_alignment_heads, dim=-2, keepdim=True, unbiased=False,
)
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):
return super().infer(is_last)
return attn_of_alignment_heads

395
whisperlivekit/voxtral_hf_streaming.py
View File

@@ -1,395 +0,0 @@
"""
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

6
whisperlivekit/voxtral_mlx/__init__.py
View File

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

282
whisperlivekit/voxtral_mlx/loader.py
View File

@@ -1,282 +0,0 @@
"""
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

534
whisperlivekit/voxtral_mlx/model.py
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@@ -1,534 +0,0 @@
"""
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)

202
whisperlivekit/voxtral_mlx/spectrogram.py
View File

@@ -1,202 +0,0 @@
"""
Mel spectrogram computation for Voxtral Realtime.
Provides both a full-audio function and an incremental streaming variant
that maintains overlap state between calls. The DFT is computed via
matrix multiplication in MLX — no external FFT dependency required.
"""
import math
import mlx.core as mx
import numpy as np
# Audio / mel constants matching the Voxtral Realtime model expectations.
SAMPLE_RATE = 16_000
WINDOW_SIZE = 400 # n_fft
HOP = 160
MEL_BANDS = 128
MEL_MAX = 1.5 # global log-mel normalisation ceiling
# Each output audio token spans: hop * conv_stride(2) * downsample_factor(4)
SAMPLES_PER_TOKEN = HOP * 2 * 4 # = 1280 samples = 80 ms
# Padding tokens used by the model prompt structure.
LEFT_PAD_TOKENS = 32
RIGHT_PAD_TOKENS = 17
# ---------------------------------------------------------------------------
# Slaney mel filterbank
# ---------------------------------------------------------------------------
def _build_slaney_filterbank(
sr: int = SAMPLE_RATE,
n_fft: int = WINDOW_SIZE,
n_mels: int = MEL_BANDS,
lo_hz: float = 0.0,
hi_hz: float = 8000.0,
) -> np.ndarray:
"""Compute a Slaney-normalised triangular mel filterbank.
Returns an array of shape ``[n_mels, n_fft//2 + 1]``.
"""
def _hz2mel(f):
threshold = 1000.0
base_mel = 15.0
log_coeff = 27.0 / np.log(6.4)
mel = 3.0 * f / 200.0
if isinstance(f, np.ndarray):
above = f >= threshold
mel[above] = base_mel + np.log(f[above] / threshold) * log_coeff
elif f >= threshold:
mel = base_mel + np.log(f / threshold) * log_coeff
return mel
def _mel2hz(m):
threshold = 1000.0
base_mel = 15.0
log_coeff = np.log(6.4) / 27.0
hz = 200.0 * m / 3.0
above = m >= base_mel
hz[above] = threshold * np.exp(log_coeff * (m[above] - base_mel))
return hz
n_bins = n_fft // 2 + 1
fft_hz = np.linspace(0, sr / 2, n_bins)
mel_lo, mel_hi = _hz2mel(lo_hz), _hz2mel(hi_hz)
mel_pts = np.linspace(mel_lo, mel_hi, n_mels + 2)
hz_pts = _mel2hz(mel_pts)
diffs = np.diff(hz_pts)
slopes = np.expand_dims(hz_pts, 0) - np.expand_dims(fft_hz, 1)
rising = -slopes[:, :-2] / diffs[:-1]
falling = slopes[:, 2:] / diffs[1:]
fb = np.maximum(0.0, np.minimum(rising, falling))
# Slaney area normalisation
widths = 2.0 / (hz_pts[2 : n_mels + 2] - hz_pts[:n_mels])
fb *= np.expand_dims(widths, 0)
return fb.T.astype(np.float32)
_CACHED_FILTERS: mx.array | None = None
def _mel_filters() -> mx.array:
global _CACHED_FILTERS
if _CACHED_FILTERS is None:
_CACHED_FILTERS = mx.array(_build_slaney_filterbank())
return _CACHED_FILTERS
# ---------------------------------------------------------------------------
# DFT helpers
# ---------------------------------------------------------------------------
def _hann_window() -> mx.array:
return mx.array(np.hanning(WINDOW_SIZE + 1)[:-1].astype(np.float32))
def _dft_matrices():
"""Pre-compute the real / imaginary DFT basis matrices."""
n_bins = WINDOW_SIZE // 2 + 1
k = mx.arange(n_bins, dtype=mx.float32)[:, None]
n = mx.arange(WINDOW_SIZE, dtype=mx.float32)[None, :]
phase = -2.0 * math.pi * (k @ n) / WINDOW_SIZE
return mx.cos(phase), mx.sin(phase)
def _stft_frames(audio: mx.array, window: mx.array) -> mx.array:
"""Frame *audio* using the Hann window and compute power spectrogram."""
n_bins = WINDOW_SIZE // 2 + 1
n_frames = 1 + (audio.shape[0] - WINDOW_SIZE) // HOP
if n_frames <= 0:
return mx.zeros((0, n_bins))
offsets = (mx.arange(n_frames) * HOP)[:, None]
indices = offsets + mx.arange(WINDOW_SIZE)[None, :]
windowed = audio[indices] * window[None, :]
dft_re, dft_im = _dft_matrices()
real_part = windowed @ dft_re.T
imag_part = windowed @ dft_im.T
return real_part ** 2 + imag_part ** 2
def _apply_mel_and_log(power: mx.array) -> mx.array:
"""Convert a power spectrogram to log-mel and normalise."""
mel = power @ _mel_filters().T
log_mel = mx.log10(mx.maximum(mel, 1e-10))
log_mel = mx.maximum(log_mel, MEL_MAX - 8.0)
return (log_mel + 4.0) / 4.0
# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------
def compute_mel(audio: np.ndarray) -> mx.array:
"""Compute log-mel spectrogram for a complete audio signal.
Args:
audio: 1-D float32 numpy array at ``SAMPLE_RATE``.
Returns:
``[MEL_BANDS, T]`` MLX array.
"""
x = mx.array(audio)
pad = WINDOW_SIZE // 2
x = mx.pad(x, [(pad, pad)])
window = _hann_window()
power = _stft_frames(x, window)
# Drop last frame to match reference STFT behaviour
power = power[:-1]
return _apply_mel_and_log(power).T
def compute_mel_streaming(
chunk: np.ndarray,
overlap: np.ndarray | None,
) -> tuple[mx.array, np.ndarray]:
"""Incrementally compute log-mel for a new audio chunk.
Args:
chunk: New audio samples (float32 numpy).
overlap: The last ``WINDOW_SIZE - HOP`` = 240 samples from the
previous call, or *None* on the first call (uses zero-padding).
Returns:
``(mel, new_overlap)`` where *mel* is ``[MEL_BANDS, N]`` and
*new_overlap* is the 240-sample tail for the next call.
"""
tail_len = WINDOW_SIZE - HOP # 240
if overlap is not None:
combined = np.concatenate([overlap, chunk])
else:
combined = np.concatenate([np.zeros(WINDOW_SIZE // 2, dtype=np.float32), chunk])
new_overlap = combined[-tail_len:].copy()
x = mx.array(combined)
window = _hann_window()
power = _stft_frames(x, window)
if power.shape[0] == 0:
return mx.zeros((MEL_BANDS, 0)), new_overlap
return _apply_mel_and_log(power).T, new_overlap
def pad_audio(
audio: np.ndarray,
n_left: int = LEFT_PAD_TOKENS,
n_right: int = RIGHT_PAD_TOKENS,
) -> np.ndarray:
"""Pad audio with silence for batch (non-streaming) inference."""
left = n_left * SAMPLES_PER_TOKEN
align = (SAMPLES_PER_TOKEN - (len(audio) % SAMPLES_PER_TOKEN)) % SAMPLES_PER_TOKEN
right = align + n_right * SAMPLES_PER_TOKEN
return np.pad(audio, (left, right))

521
whisperlivekit/voxtral_mlx_asr.py
View File

@@ -1,521 +0,0 @@
"""
Pure-MLX Voxtral Realtime ASR backend for WhisperLiveKit.
Provides ``VoxtralMLXASR`` (model holder) and ``VoxtralMLXOnlineProcessor``
(streaming processor) that plug into WhisperLiveKit's audio processing
pipeline via ``insert_audio_chunk`` / ``process_iter`` / ``get_buffer`` etc.
Unlike the HuggingFace backend, this runs the full inference loop in-process
(no background thread / queue) — MLX operations on Apple Silicon are fast
enough to run synchronously inside ``asyncio.to_thread(process_iter)``.
"""
import logging
import sys
import time
from typing import List, Optional, Tuple
import mlx.core as mx
import numpy as np
from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy
from whisperlivekit.timed_objects import ASRToken, Transcript
from whisperlivekit.voxtral_mlx.loader import load_voxtral_model, DEFAULT_MODEL_ID
from whisperlivekit.voxtral_mlx.model import SlidingKVCache
from whisperlivekit.voxtral_mlx.spectrogram import (
SAMPLES_PER_TOKEN,
LEFT_PAD_TOKENS,
RIGHT_PAD_TOKENS,
compute_mel_streaming,
)
logger = logging.getLogger(__name__)
# Decoder sliding-window size (matches the model's training configuration).
_DECODER_WINDOW = 8192
def _prompt_tokens(tokenizer, n_left_pad=LEFT_PAD_TOKENS, n_delay=6):
"""Build the prompt token sequence and return ``(token_ids, n_delay)``."""
pad_id = tokenizer.get_special_token("[STREAMING_PAD]")
ids = [tokenizer.bos_id] + [pad_id] * (n_left_pad + n_delay)
return ids, n_delay
# ---------------------------------------------------------------------------
# Model holder
# ---------------------------------------------------------------------------
class VoxtralMLXASR:
"""Lightweight model holder — loads the MLX Voxtral model once and keeps
it alive for the lifetime of the server."""
sep = " "
SAMPLING_RATE = 16_000
def __init__(self, logfile=sys.stderr, **kwargs):
self.logfile = logfile
self.transcribe_kargs = {}
lan = kwargs.get("lan", "auto")
self.original_language = None if lan == "auto" else lan
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_ID
t0 = time.time()
logger.info("Loading Voxtral MLX model '%s' ...", model_path)
self.model, self.tokenizer, self.config = load_voxtral_model(model_path)
logger.info("Voxtral MLX model loaded in %.2fs", time.time() - t0)
self.backend_choice = "voxtral-mlx"
def transcribe(self, audio):
pass # all work happens in the online processor
# ---------------------------------------------------------------------------
# Online processor
# ---------------------------------------------------------------------------
class VoxtralMLXOnlineProcessor:
"""Streaming processor that incrementally encodes audio and decodes text
using the MLX Voxtral model.
Lifecycle (called by ``AudioProcessor.transcription_processor``):
insert_audio_chunk(pcm, time) → process_iter() → get_buffer()
... repeat ...
start_silence() / end_silence()
finish()
"""
SAMPLING_RATE = 16_000
def __init__(self, asr: VoxtralMLXASR, logfile=sys.stderr):
self.asr = asr
self.logfile = logfile
self.end = 0.0
self.buffer: list = []
self.audio_buffer = np.array([], dtype=np.float32)
self._model = asr.model
self._tokenizer = asr.tokenizer
# Pre-compute prompt tokens and delay conditioning (constant across utterances).
self._prompt_ids, self._n_delay = _prompt_tokens(self._tokenizer)
self._prefix_len = len(self._prompt_ids)
self._delay_cond = self._model.delay_embedding(
mx.array([self._n_delay], dtype=mx.float32)
)
mx.eval(self._delay_cond)
self._prompt_embeds = self._model.decoder.embed(
mx.array([self._prompt_ids])
)[0] # [prefix_len, dim]
mx.eval(self._prompt_embeds)
self._eos_id = self._tokenizer.eos_id
self._secs_per_token = SAMPLES_PER_TOKEN / self.SAMPLING_RATE
# The streaming model has an inherent delay: text for audio at position P
# is generated at decoder position P + n_delay. Compensate timestamps.
self._delay_secs = self._n_delay * self._secs_per_token
self._reset_state()
# -- state management --
def _reset_state(self):
"""Reset all incremental state for a fresh utterance."""
# Audio accumulation
self._pending = np.zeros(0, dtype=np.float32)
# Mel overlap
self._mel_overlap: np.ndarray | None = None
# Encoder incremental state
self._conv_tail1 = None
self._conv_tail2 = None
self._enc_cache = None
self._ds_remainder = None
# Audio embeddings not yet decoded
self._audio_embeds: mx.array | None = None
# Decoder state
self._dec_cache: list[SlidingKVCache] | None = None
self._last_token: mx.array | None = None
# Bookkeeping
self._samples_encoded = 0
self._positions_decoded = 0
self._prefilled = False
self._first_chunk = True
# Text state
self._full_text = ""
self._n_text_tokens = 0
self._n_committed_words = 0
self._time_offset = 0.0
# Per-word audio position tracking: decoder position (relative to prefix)
# where each word in _full_text started and ended
self._word_audio_starts: list[int] = [] # audio pos where word i started
self._word_audio_ends: list[int] = [] # audio pos where word i last produced a token
self._current_word_pos: Optional[int] = None # audio pos of current (incomplete) word's first token
# -- audio ingestion --
def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time: float):
self.end = audio_stream_end_time
self._pending = np.append(self._pending, audio)
self.audio_buffer = self._pending
# -- core processing --
def process_iter(self, is_last=False) -> Tuple[List[ASRToken], float]:
try:
return self._step(is_last)
except Exception as e:
logger.warning("[voxtral-mlx] process_iter error: %s", e, exc_info=True)
return [], self.end
def _step(self, is_last: bool) -> Tuple[List[ASRToken], float]:
# 1. Encode any new audio
self._encode_pending()
if self._audio_embeds is None:
return [], self.end
# 2. Compute how many positions we can safely decode
total_safe = LEFT_PAD_TOKENS + self._samples_encoded // SAMPLES_PER_TOKEN
n_available = self._audio_embeds.shape[0]
n_decodable = min(n_available, total_safe - self._positions_decoded)
if n_decodable <= 0:
return [], self.end
# 3. Prefill if needed
if not self._prefilled:
if self._positions_decoded + n_available < self._prefix_len:
return [], self.end
self._do_prefill()
# Re-check after consuming prefix embeddings
n_available = self._audio_embeds.shape[0] if self._audio_embeds is not None else 0
n_decodable = min(n_available, total_safe - self._positions_decoded)
if n_decodable <= 0 or self._audio_embeds is None:
return [], self.end
# 4. Decode available positions
hit_eos = self._decode_positions(n_decodable)
if hit_eos:
# Flush words, reset for next utterance
words = self._flush_all_words()
logger.debug(
"[voxtral-mlx] EOS hit during stream: flushed %d words, "
"samples_encoded=%d (%.2fs), text='%s'",
len(words), self._samples_encoded,
self._samples_encoded / self.SAMPLING_RATE,
self._full_text[-60:] if self._full_text else "",
)
saved_offset = self._time_offset
self._reset_state()
self._time_offset = saved_offset
return words, self.end
# 5. Extract committed words (all but the last, which may still grow)
return self._extract_committed_words(), self.end
def _encode_pending(self):
"""Feed pending audio through the incremental encoder."""
available = len(self._pending)
if available < SAMPLES_PER_TOKEN:
return
if self._first_chunk:
# First chunk: prepend silence for left-padding
n_take = (available // SAMPLES_PER_TOKEN) * SAMPLES_PER_TOKEN
left_pad = np.zeros(LEFT_PAD_TOKENS * SAMPLES_PER_TOKEN, dtype=np.float32)
chunk = np.concatenate([left_pad, self._pending[:n_take]])
self._pending = self._pending[n_take:]
self._samples_encoded += n_take
self._first_chunk = False
else:
n_take = (available // SAMPLES_PER_TOKEN) * SAMPLES_PER_TOKEN
chunk = self._pending[:n_take]
self._pending = self._pending[n_take:]
self._samples_encoded += n_take
mel, self._mel_overlap = compute_mel_streaming(chunk, self._mel_overlap)
embeds, self._conv_tail1, self._conv_tail2, self._enc_cache, self._ds_remainder = (
self._model.encode_incremental(
mel, self._conv_tail1, self._conv_tail2, self._enc_cache, self._ds_remainder
)
)
if embeds is not None:
mx.eval(embeds)
if self._audio_embeds is not None:
self._audio_embeds = mx.concatenate([self._audio_embeds, embeds])
else:
self._audio_embeds = embeds
self.audio_buffer = self._pending
def _do_prefill(self):
"""Run the decoder prefill pass over the prompt + first audio embeddings."""
n_dec_layers = len(self._model.decoder.blocks)
self._dec_cache = [SlidingKVCache(_DECODER_WINDOW) for _ in range(n_dec_layers)]
prefix_embeds = self._prompt_embeds + self._audio_embeds[: self._prefix_len]
prefix_embeds = prefix_embeds[None, :, :] # [1, prefix_len, dim]
logits = self._model.decode(prefix_embeds, self._delay_cond, "causal", self._dec_cache)
mx.eval(logits, *[x for c in self._dec_cache for x in (c.keys, c.values)])
self._last_token = self._sample(logits)
mx.async_eval(self._last_token)
# Remove consumed prefix embeddings
self._audio_embeds = self._audio_embeds[self._prefix_len :]
if self._audio_embeds.shape[0] == 0:
self._audio_embeds = None
self._positions_decoded = self._prefix_len
self._prefilled = True
def _decode_positions(self, n: int) -> bool:
"""Autoregressively decode *n* positions. Returns True on EOS."""
base_pos = self._positions_decoded # absolute position before this batch
for i in range(n):
tok_embed = self._model.decoder.embed(self._last_token.reshape(1, 1))[0, 0]
combined = (self._audio_embeds[i] + tok_embed)[None, None, :]
logits = self._model.decode(combined, self._delay_cond, mask=None, cache=self._dec_cache)
next_tok = self._sample(logits)
mx.async_eval(next_tok)
token_id = self._last_token.item()
if token_id == self._eos_id:
# Close the current word if one is being built
if self._current_word_pos is not None:
self._word_audio_ends.append(base_pos + i - self._prefix_len)
self._current_word_pos = None
self._trim_embeds(i)
self._positions_decoded += i
return True
text = self._tokenizer.decode(
[token_id], special_token_policy=SpecialTokenPolicy.IGNORE
)
if text:
audio_pos = base_pos + i - self._prefix_len
# Detect word boundary: new word starts with space or is the very first text
if text.lstrip() != text or not self._full_text:
# Close previous word if exists
if self._current_word_pos is not None:
self._word_audio_ends.append(audio_pos)
# Start new word
self._word_audio_starts.append(audio_pos)
self._current_word_pos = audio_pos
elif self._current_word_pos is None:
# First token of first word (no leading space)
self._word_audio_starts.append(audio_pos)
self._current_word_pos = audio_pos
self._full_text += text
self._n_text_tokens += 1
if i > 0 and i % 256 == 0:
mx.clear_cache()
self._last_token = next_tok
self._positions_decoded += n
self._trim_embeds(n)
return False
def _trim_embeds(self, n_consumed: int):
if self._audio_embeds is not None and self._audio_embeds.shape[0] > n_consumed:
self._audio_embeds = self._audio_embeds[n_consumed:]
else:
self._audio_embeds = None
def _sample(self, logits: mx.array) -> mx.array:
return mx.argmax(logits[0, -1:], axis=-1).squeeze()
# -- word extraction --
def _audio_pos_to_time(self, pos: int) -> float:
"""Convert an audio position (relative to prefix end) to seconds."""
return max(0.0, pos * self._secs_per_token - self._delay_secs + self._time_offset)
def _word_time_range(self, word_idx: int, n_words: int) -> Tuple[float, float]:
"""Compute (start, end) time for a word using tracked word positions."""
starts = self._word_audio_starts
ends = self._word_audio_ends
if not starts:
return self._time_offset, self._time_offset
# Get start position for this word
if word_idx < len(starts):
t0 = self._audio_pos_to_time(starts[word_idx])
else:
# Fallback: estimate from last known position
last_pos = ends[-1] if ends else starts[-1]
t0 = self._audio_pos_to_time(last_pos + 1)
# Get end position: use the start of the next word, or the end of this word
if word_idx + 1 < len(starts):
t1 = self._audio_pos_to_time(starts[word_idx + 1])
elif word_idx < len(ends):
t1 = self._audio_pos_to_time(ends[word_idx] + 1)
else:
# Last word, still being built: use last known position + 1 token
last_pos = starts[word_idx] if word_idx < len(starts) else (ends[-1] if ends else 0)
t1 = self._audio_pos_to_time(last_pos + 1)
return t0, t1
def _extract_committed_words(self) -> List[ASRToken]:
"""Return complete words (all except the last which may still grow)."""
if not self._full_text:
return []
words = self._full_text.split()
tokens: List[ASRToken] = []
n_total = max(len(words), 1)
while len(words) > self._n_committed_words + 1:
w = words[self._n_committed_words]
idx = self._n_committed_words
t0, t1 = self._word_time_range(idx, n_total)
label = w if idx == 0 else " " + w
tokens.append(ASRToken(start=t0, end=t1, text=label))
self._n_committed_words += 1
return tokens
def _flush_all_words(self) -> List[ASRToken]:
"""Flush every word including the last partial one."""
if not self._full_text:
return []
words = self._full_text.split()
tokens: List[ASRToken] = []
n_total = max(len(words), 1)
while self._n_committed_words < len(words):
w = words[self._n_committed_words]
idx = self._n_committed_words
t0, t1 = self._word_time_range(idx, n_total)
label = w if idx == 0 else " " + w
tokens.append(ASRToken(start=t0, end=t1, text=label))
self._n_committed_words += 1
return tokens
# -- interface methods --
def get_buffer(self) -> Transcript:
if not self._full_text:
return Transcript(start=None, end=None, text="")
words = self._full_text.split()
remaining = words[self._n_committed_words :]
if remaining:
return Transcript(start=self.end, end=self.end, text=" ".join(remaining))
return Transcript(start=None, end=None, text="")
def start_silence(self) -> Tuple[List[ASRToken], float]:
words = self._flush_all_words()
logger.info("[voxtral-mlx] start_silence: flushed %d words", len(words))
return words, self.end
def end_silence(self, silence_duration: float, offset: float):
self._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]:
logger.debug(
"[voxtral-mlx] finish: pending=%d samples, audio_embeds=%s, "
"samples_encoded=%d, positions_decoded=%d, prefilled=%s, text so far='%s'",
len(self._pending),
self._audio_embeds.shape if self._audio_embeds is not None else None,
self._samples_encoded,
self._positions_decoded,
self._prefilled,
self._full_text[-80:] if self._full_text else "",
)
# Align pending audio to SAMPLES_PER_TOKEN boundary so nothing is lost
remainder = len(self._pending) % SAMPLES_PER_TOKEN
if remainder > 0:
align_pad = SAMPLES_PER_TOKEN - remainder
else:
align_pad = 0
# Add alignment + right-padding silence
total_pad = align_pad + RIGHT_PAD_TOKENS * SAMPLES_PER_TOKEN
if total_pad > 0:
self._pending = np.append(
self._pending, np.zeros(total_pad, dtype=np.float32)
)
# Encode remaining audio (including right-padding)
self._encode_pending()
logger.debug(
"[voxtral-mlx] finish after encode: audio_embeds=%s, pending=%d",
self._audio_embeds.shape if self._audio_embeds is not None else None,
len(self._pending),
)
hit_eos = False
# Decode everything that's left from right-padding
if self._audio_embeds is not None and self._prefilled:
hit_eos = self._decode_positions(self._audio_embeds.shape[0])
logger.debug(
"[voxtral-mlx] finish decode: hit_eos=%s, text='%s'",
hit_eos, self._full_text[-80:] if self._full_text else "",
)
# Flush last token if it wasn't EOS
if self._last_token is not None:
tid = self._last_token.item()
if tid != self._eos_id:
text = self._tokenizer.decode(
[tid], special_token_policy=SpecialTokenPolicy.IGNORE
)
if text:
last_pos = self._positions_decoded - self._prefix_len
# Check if this starts a new word
if text.lstrip() != text or not self._full_text:
if self._current_word_pos is not None:
self._word_audio_ends.append(last_pos)
self._word_audio_starts.append(last_pos)
self._current_word_pos = last_pos
elif self._current_word_pos is None:
self._word_audio_starts.append(last_pos)
self._current_word_pos = last_pos
self._full_text += text
self._n_text_tokens += 1
# Close the last word if still open
if self._current_word_pos is not None:
last_pos = self._positions_decoded - self._prefix_len
self._word_audio_ends.append(last_pos)
self._current_word_pos = None
words = self._flush_all_words()
logger.info("[voxtral-mlx] finish: flushed %d words", len(words))
return words, self.end

484
whisperlivekit/voxtral_streaming.py Normal file
View File

@@ -0,0 +1,484 @@
"""
Voxtral Mini Realtime streaming backend using voxmlx's incremental encode/decode.
Uses model.encode_step() for incremental audio encoding and token-by-token
autoregressive decoding, matching voxmlx's native streaming pipeline.
"""
import logging
import sys
import time
from typing import List, Optional, Tuple
import numpy as np
from whisperlivekit.timed_objects import ASRToken, Transcript
logger = logging.getLogger(__name__)
N_LEFT_PAD_TOKENS = 32
N_RIGHT_PAD_TOKENS = 17
class VoxtralStreamingASR:
"""Voxtral model holder for the streaming pipeline."""
sep = " "
def __init__(self, logfile=sys.stderr, **kwargs):
from voxmlx import _build_prompt_tokens
from voxmlx import load_model as vox_load_model
self.logfile = logfile
self.transcribe_kargs = {}
lan = kwargs.get("lan", "auto")
self.original_language = None if lan == "auto" else lan
DEFAULT_MODEL = "mlx-community/Voxtral-Mini-4B-Realtime-6bit"
model_path = kwargs.get("model_dir") or kwargs.get("model_path")
if not model_path:
model_size = kwargs.get("model_size", "")
# Only use model_size if it looks like a HF repo or a path, not a Whisper size name
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 voxmlx...")
self.model, self._tokenizer, self._config = vox_load_model(model_path)
self._prompt_tokens, self._n_delay_tokens = _build_prompt_tokens(
self._tokenizer
)
logger.info(f"Voxtral model loaded in {time.time() - t:.2f}s")
self.backend_choice = "voxtral-mlx"
self.tokenizer = None # sentence tokenizer — not needed for streaming
def transcribe(self, audio):
pass
class VoxtralStreamingOnlineProcessor:
"""
Online processor for Voxtral streaming ASR.
Uses voxmlx's incremental encoding (encode_step) and token-by-token
autoregressive decoding. Each decode step corresponds to 80ms of audio.
"""
SAMPLING_RATE = 16000
def __init__(self, asr: VoxtralStreamingASR, logfile=sys.stderr):
from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy
self.asr = asr
self.logfile = logfile
self.end = 0.0
self.buffer = []
self.audio_buffer = np.array([], dtype=np.float32) # for logging compat
self._special_token_policy = SpecialTokenPolicy.IGNORE
self._reset_state()
logger.info(
f"[voxtral] Initialized. eos_id={asr._tokenizer.eos_id}, "
f"prefix_len={len(asr._prompt_tokens)}, "
f"n_delay={asr._n_delay_tokens}"
)
def _reset_state(self):
from voxmlx.audio import SAMPLES_PER_TOKEN
self._samples_per_token = SAMPLES_PER_TOKEN
# Incremental encoder state
self._audio_tail = None
self._conv1_tail = None
self._conv2_tail = None
self._encoder_cache = None
self._ds_buf = None
# Decoder state
self._decoder_cache = None
self._y = None # last sampled token (mx.array scalar)
self._t_cond = None
self._text_embeds = None
# Audio / decode tracking
self._pending_audio = np.zeros(0, dtype=np.float32)
self._audio_embeds = None
self._n_audio_samples_fed = 0
self._n_total_decoded = 0
self._first_cycle = True
self._prefilled = False
# Word extraction: accumulate token IDs, full-sequence decode for correct spacing
self._output_token_ids: List[int] = []
self._token_positions: List[int] = [] # decode position for each token
self._n_committed_words = 0
self._global_time_offset = 0.0
self._y_flushed_to_output = False # True after start_silence flushes pending _y
# ── Interface methods (same as SimulStreamingOnlineProcessor) ──
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 # for logging compat
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] process_iter exception: {e}", exc_info=True)
return [], self.end
def _get_full_text(self) -> str:
"""Decode all accumulated token IDs at once for correct spacing."""
if not self._output_token_ids:
return ""
sp = self.asr._tokenizer
return sp.decode(self._output_token_ids, special_token_policy=self._special_token_policy)
def get_buffer(self) -> Transcript:
"""Return all uncommitted text as buffer, including pending _y token."""
# Temporarily include pending _y for buffer display
ids = list(self._output_token_ids)
if self._y is not None and not self._y_flushed_to_output:
sp = self.asr._tokenizer
token_id = self._y.item()
if token_id != sp.eos_id:
ids.append(token_id)
if not ids:
return Transcript(start=None, end=None, text="")
sp = self.asr._tokenizer
full_text = sp.decode(ids, special_token_policy=self._special_token_policy)
words = full_text.split()
uncommitted = words[self._n_committed_words:]
if uncommitted:
text = " ".join(uncommitted)
return Transcript(start=self.end, end=self.end, text=text)
return Transcript(start=None, end=None, text="")
def start_silence(self) -> Tuple[List[ASRToken], float]:
"""Flush all uncommitted words when silence starts."""
self._flush_last_y() # Include the pending _y token before flushing
words = self._flush_all_pending_words()
logger.info(f"[voxtral] 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 to let the model finish decoding."""
right_pad = np.zeros(
N_RIGHT_PAD_TOKENS * self._samples_per_token, dtype=np.float32
)
self._pending_audio = np.append(self._pending_audio, right_pad)
self._n_audio_samples_fed += len(right_pad)
final_words, _ = self._process_iter_inner(is_last=True)
# Flush the last pending self._y token (like voxmlx's finally block)
self._flush_last_y()
final_words.extend(self._flush_all_pending_words())
return final_words, self.end
# ── Word extraction ──
def _pos_to_time(self, pos: int) -> float:
"""Convert a decode position to seconds relative to audio start."""
SPT = self._samples_per_token
return max(0.0, (pos - N_LEFT_PAD_TOKENS) * SPT / self.SAMPLING_RATE)
def _flush_last_y(self):
"""Flush the last pending self._y token that hasn't been processed yet."""
if self._y is None or self._y_flushed_to_output:
return
sp = self.asr._tokenizer
token_id = self._y.item()
if token_id != sp.eos_id:
self._output_token_ids.append(token_id)
self._token_positions.append(self._n_total_decoded)
self._y_flushed_to_output = True
def _extract_new_words(self) -> List[ASRToken]:
"""
Split accumulated text into words and return new complete words
(all but the last, which may still be growing).
"""
if not self._output_token_ids:
return []
full_text = self._get_full_text()
words = full_text.split()
new_words: List[ASRToken] = []
n_tokens = len(self._output_token_ids)
# All words except the last are guaranteed complete
while len(words) > self._n_committed_words + 1:
word = words[self._n_committed_words]
word_idx = self._n_committed_words
n_words_total = len(words)
# Approximate: assign token range proportionally
tok_start = int(word_idx / n_words_total * n_tokens)
tok_end = int((word_idx + 1) / n_words_total * n_tokens)
tok_start = min(tok_start, len(self._token_positions) - 1)
tok_end = min(tok_end, len(self._token_positions) - 1)
start_time = self._pos_to_time(self._token_positions[tok_start]) + self._global_time_offset
end_time = self._pos_to_time(self._token_positions[tok_end]) + self._global_time_offset
# Prepend space to match Whisper convention (Segment.from_tokens joins with '')
text = word if self._n_committed_words == 0 else " " + word
new_words.append(ASRToken(start=start_time, end=end_time, text=text))
self._n_committed_words += 1
return new_words
def _flush_all_pending_words(self) -> List[ASRToken]:
"""Flush ALL words including the last partial one."""
if not self._output_token_ids:
return []
full_text = self._get_full_text()
words = full_text.split()
new_words: List[ASRToken] = []
n_tokens = len(self._output_token_ids)
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)
tok_start = min(tok_start, max(len(self._token_positions) - 1, 0))
tok_end = min(tok_end, max(len(self._token_positions) - 1, 0))
if self._token_positions:
start_time = self._pos_to_time(self._token_positions[tok_start]) + self._global_time_offset
end_time = self._pos_to_time(self._token_positions[tok_end]) + self._global_time_offset
else:
start_time = self._global_time_offset
end_time = self._global_time_offset
# Prepend space to match Whisper convention (Segment.from_tokens joins with '')
text = word if self._n_committed_words == 0 else " " + word
new_words.append(ASRToken(start=start_time, end=end_time, text=text))
self._n_committed_words += 1
return new_words
# ── Core streaming logic ──
def _process_iter_inner(self, is_last: bool) -> Tuple[List[ASRToken], float]:
import mlx.core as mx
from voxmlx.audio import log_mel_spectrogram_step
from voxmlx.cache import RotatingKVCache
model = self.asr.model
sp = self.asr._tokenizer
prompt_tokens = self.asr._prompt_tokens
prefix_len = len(prompt_tokens)
SPT = self._samples_per_token
# ── Phase 1: Encode new audio ──
if self._first_cycle and len(self._pending_audio) >= SPT:
left_pad = np.zeros(N_LEFT_PAD_TOKENS * SPT, dtype=np.float32)
n_feed = (len(self._pending_audio) // SPT) * SPT
chunk = np.concatenate([left_pad, self._pending_audio[:n_feed]])
self._pending_audio = self._pending_audio[n_feed:]
self._n_audio_samples_fed += n_feed
mel, self._audio_tail = log_mel_spectrogram_step(
chunk, self._audio_tail
)
(
new_embeds,
self._conv1_tail,
self._conv2_tail,
self._encoder_cache,
self._ds_buf,
) = model.encode_step(
mel,
self._conv1_tail,
self._conv2_tail,
self._encoder_cache,
self._ds_buf,
)
if new_embeds is not None:
mx.eval(new_embeds)
self._audio_embeds = new_embeds
logger.info(f"[voxtral] first encode: {new_embeds.shape[0]} embeds from {n_feed} samples")
else:
logger.info(f"[voxtral] first encode: no embeds from {n_feed} samples")
self._first_cycle = False
elif not self._first_cycle and len(self._pending_audio) >= SPT:
n_feed = (len(self._pending_audio) // SPT) * SPT
chunk = self._pending_audio[:n_feed]
self._pending_audio = self._pending_audio[n_feed:]
self._n_audio_samples_fed += n_feed
mel, self._audio_tail = log_mel_spectrogram_step(
chunk, self._audio_tail
)
(
new_embeds,
self._conv1_tail,
self._conv2_tail,
self._encoder_cache,
self._ds_buf,
) = model.encode_step(
mel,
self._conv1_tail,
self._conv2_tail,
self._encoder_cache,
self._ds_buf,
)
if new_embeds is not None:
mx.eval(new_embeds)
if self._audio_embeds is not None:
self._audio_embeds = mx.concatenate(
[self._audio_embeds, new_embeds]
)
else:
self._audio_embeds = new_embeds
self.audio_buffer = self._pending_audio # for logging compat
if self._audio_embeds is None:
return [], self.end
# Safety: don't decode ahead of encoded audio
safe_total = (
N_LEFT_PAD_TOKENS + self._n_audio_samples_fed // SPT
)
n_decodable = min(
self._audio_embeds.shape[0], safe_total - self._n_total_decoded
)
if n_decodable <= 0:
return [], self.end
# ── Phase 2: Prefill (once per utterance) ──
if not self._prefilled:
if self._n_total_decoded + self._audio_embeds.shape[0] < prefix_len:
logger.info(
f"[voxtral] waiting for prefill: have {self._audio_embeds.shape[0]} embeds, need {prefix_len}"
)
return [], self.end
n_layers = len(model.language_model.layers)
self._decoder_cache = [RotatingKVCache(8192) for _ in range(n_layers)]
self._t_cond = model.time_embedding(
mx.array([self.asr._n_delay_tokens], dtype=mx.float32)
)
prompt_ids = mx.array([prompt_tokens])
self._text_embeds = model.language_model.embed(prompt_ids)[0]
prefix_embeds = (
self._text_embeds + self._audio_embeds[:prefix_len]
)[None, :, :]
logits = model.decode(
prefix_embeds, self._t_cond, "causal", self._decoder_cache
)
mx.eval(
logits,
*[x for c in self._decoder_cache for x in (c.keys, c.values)],
)
self._y = mx.argmax(logits[0, -1:], axis=-1).squeeze()
mx.async_eval(self._y)
self._audio_embeds = self._audio_embeds[prefix_len:]
self._n_total_decoded = prefix_len
self._prefilled = True
logger.info(f"[voxtral] prefill done, first token y={self._y.item()}")
n_decodable = min(
self._audio_embeds.shape[0], safe_total - self._n_total_decoded
)
if n_decodable <= 0:
return [], self.end
# ── Phase 3: Decode new positions ──
eos_id = sp.eos_id
hit_eos = False
n_consumed = 0
for i in range(n_decodable):
token_embed = model.language_model.embed(self._y.reshape(1, 1))[0, 0]
step_embed = (self._audio_embeds[i] + token_embed)[None, None, :]
logits = model.decode(
step_embed, self._t_cond, mask=None, cache=self._decoder_cache
)
next_y = mx.argmax(logits[0, -1:], axis=-1).squeeze()
mx.async_eval(next_y)
token_id = self._y.item()
n_consumed = i + 1
if token_id == eos_id:
hit_eos = True
logger.info("[voxtral] hit EOS")
break
# Accumulate token ID — full-sequence decode produces correct spacing
# Skip if this _y was already flushed by start_silence()
if self._y_flushed_to_output:
self._y_flushed_to_output = False
else:
self._output_token_ids.append(token_id)
# Track position for timestamp estimation
pos = self._n_total_decoded + i
self._token_positions.append(pos)
if i > 0 and i % 256 == 0:
mx.clear_cache()
self._y = next_y
self._n_total_decoded += n_consumed
# Trim consumed embeddings
if self._audio_embeds.shape[0] > n_consumed:
self._audio_embeds = self._audio_embeds[n_consumed:]
else:
self._audio_embeds = None
# Log decode results
full_text = self._get_full_text()
logger.info(
f"[voxtral] decoded {n_consumed} tokens | "
f"total_decoded={self._n_total_decoded} | "
f"text='{full_text[-80:]}' | "
f"n_words={len(full_text.split())} committed={self._n_committed_words}"
)
# Extract complete words from the decoded token sequence
new_words = self._extract_new_words()
if hit_eos:
new_words.extend(self._flush_all_pending_words())
self._reset_state()
if new_words:
logger.info(f"[voxtral] returning {len(new_words)} words: {[w.text for w in new_words]}")
self.buffer = []
return new_words, self.end

13
whisperlivekit/whisper/tokenizer.py
View File

@@ -296,15 +296,10 @@ class Tokenizer:
current_tokens.append(token)
decoded = self.decode_with_timestamps(current_tokens)
try:
replacement_char_index = decoded.index(replacement_char)
replacement_char_index += unicode_offset
except ValueError:
replacement_char_index = None
if replacement_char_index is None or (
replacement_char_index < len(decoded_full)
and decoded_full[replacement_char_index] == replacement_char
if (
replacement_char not in decoded
or decoded_full[unicode_offset + decoded.index(replacement_char)]
== replacement_char
):
words.append(decoded)
word_tokens.append(current_tokens)

200
whisperlivekit/whisper/val.py
View File

@@ -1,200 +0,0 @@
"""
The most atomic way to train and inference a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp
import random # random.seed, random.choices, random.gauss, random.shuffle
random.seed(42) # Let there be order among chaos
# Let there be an input dataset `docs`: list[str] of documents (e.g. a dataset of names)
if not os.path.exists('input.txt'):
import urllib.request
names_url = 'https://raw.githubusercontent.com/karpathy/makemore/refs/heads/master/names.txt'
urllib.request.urlretrieve(names_url, 'input.txt')
docs = [l.strip() for l in open('input.txt').read().strip().split('\n') if l.strip()] # list[str] of documents
random.shuffle(docs)
print(f"num docs: {len(docs)}")
# Let there be a Tokenizer to translate strings to discrete symbols and back
uchars = sorted(set(''.join(docs))) # unique characters in the dataset become token ids 0..n-1
BOS = len(uchars) # token id for the special Beginning of Sequence (BOS) token
vocab_size = len(uchars) + 1 # total number of unique tokens, +1 is for BOS
print(f"vocab size: {vocab_size}")
# Let there be Autograd, to recursively apply the chain rule through a computation graph
class Value:
__slots__ = ('data', 'grad', '_children', '_local_grads') # Python optimization for memory usage
def __init__(self, data, children=(), local_grads=()):
self.data = data # scalar value of this node calculated during forward pass
self.grad = 0 # derivative of the loss w.r.t. this node, calculated in backward pass
self._children = children # children of this node in the computation graph
self._local_grads = local_grads # local derivative of this node w.r.t. its children
def __add__(self, other):
other = other if isinstance(other, Value) else Value(other)
return Value(self.data + other.data, (self, other), (1, 1))
def __mul__(self, other):
other = other if isinstance(other, Value) else Value(other)
return Value(self.data * other.data, (self, other), (other.data, self.data))
def __pow__(self, other): return Value(self.data**other, (self,), (other * self.data**(other-1),))
def log(self): return Value(math.log(self.data), (self,), (1/self.data,))
def exp(self): return Value(math.exp(self.data), (self,), (math.exp(self.data),))
def relu(self): return Value(max(0, self.data), (self,), (float(self.data > 0),))
def __neg__(self): return self * -1
def __radd__(self, other): return self + other
def __sub__(self, other): return self + (-other)
def __rsub__(self, other): return other + (-self)
def __rmul__(self, other): return self * other
def __truediv__(self, other): return self * other**-1
def __rtruediv__(self, other): return other * self**-1
def backward(self):
topo = []
visited = set()
def build_topo(v):
if v not in visited:
visited.add(v)
for child in v._children:
build_topo(child)
topo.append(v)
build_topo(self)
self.grad = 1
for v in reversed(topo):
for child, local_grad in zip(v._children, v._local_grads):
child.grad += local_grad * v.grad
# Initialize the parameters, to store the knowledge of the model.
n_embd = 16 # embedding dimension
n_head = 4 # number of attention heads
n_layer = 1 # number of layers
block_size = 16 # maximum sequence length
head_dim = n_embd // n_head # dimension of each head
matrix = lambda nout, nin, std=0.08: [[Value(random.gauss(0, std)) for _ in range(nin)] for _ in range(nout)]
state_dict = {'wte': matrix(vocab_size, n_embd), 'wpe': matrix(block_size, n_embd), 'lm_head': matrix(vocab_size, n_embd)}
for i in range(n_layer):
state_dict[f'layer{i}.attn_wq'] = matrix(n_embd, n_embd)
state_dict[f'layer{i}.attn_wk'] = matrix(n_embd, n_embd)
state_dict[f'layer{i}.attn_wv'] = matrix(n_embd, n_embd)
state_dict[f'layer{i}.attn_wo'] = matrix(n_embd, n_embd)
state_dict[f'layer{i}.mlp_fc1'] = matrix(4 * n_embd, n_embd)
state_dict[f'layer{i}.mlp_fc2'] = matrix(n_embd, 4 * n_embd)
params = [p for mat in state_dict.values() for row in mat for p in row] # flatten params into a single list[Value]
print(f"num params: {len(params)}")
# Define the model architecture: a stateless function mapping token sequence and parameters to logits over what comes next.
# Follow GPT-2, blessed among the GPTs, with minor differences: layernorm -> rmsnorm, no biases, GeLU -> ReLU
def linear(x, w):
return [sum(wi * xi for wi, xi in zip(wo, x)) for wo in w]
def softmax(logits):
max_val = max(val.data for val in logits)
exps = [(val - max_val).exp() for val in logits]
total = sum(exps)
return [e / total for e in exps]
def rmsnorm(x):
ms = sum(xi * xi for xi in x) / len(x)
scale = (ms + 1e-5) ** -0.5
return [xi * scale for xi in x]
def gpt(token_id, pos_id, keys, values):
tok_emb = state_dict['wte'][token_id] # token embedding
pos_emb = state_dict['wpe'][pos_id] # position embedding
x = [t + p for t, p in zip(tok_emb, pos_emb)] # joint token and position embedding
x = rmsnorm(x)
for li in range(n_layer):
# 1) Multi-head attention block
x_residual = x
x = rmsnorm(x)
q = linear(x, state_dict[f'layer{li}.attn_wq'])
k = linear(x, state_dict[f'layer{li}.attn_wk'])
v = linear(x, state_dict[f'layer{li}.attn_wv'])
keys[li].append(k)
values[li].append(v)
x_attn = []
for h in range(n_head):
hs = h * head_dim
q_h = q[hs:hs+head_dim]
k_h = [ki[hs:hs+head_dim] for ki in keys[li]]
v_h = [vi[hs:hs+head_dim] for vi in values[li]]
attn_logits = [sum(q_h[j] * k_h[t][j] for j in range(head_dim)) / head_dim**0.5 for t in range(len(k_h))]
attn_weights = softmax(attn_logits)
head_out = [sum(attn_weights[t] * v_h[t][j] for t in range(len(v_h))) for j in range(head_dim)]
x_attn.extend(head_out)
x = linear(x_attn, state_dict[f'layer{li}.attn_wo'])
x = [a + b for a, b in zip(x, x_residual)]
# 2) MLP block
x_residual = x
x = rmsnorm(x)
x = linear(x, state_dict[f'layer{li}.mlp_fc1'])
x = [xi.relu() for xi in x]
x = linear(x, state_dict[f'layer{li}.mlp_fc2'])
x = [a + b for a, b in zip(x, x_residual)]
logits = linear(x, state_dict['lm_head'])
return logits
# Let there be Adam, the blessed optimizer and its buffers
learning_rate, beta1, beta2, eps_adam = 0.01, 0.85, 0.99, 1e-8
m = [0.0] * len(params) # first moment buffer
v = [0.0] * len(params) # second moment buffer
# Repeat in sequence
num_steps = 1000 # number of training steps
for step in range(num_steps):
# Take single document, tokenize it, surround it with BOS special token on both sides
doc = docs[step % len(docs)]
tokens = [BOS] + [uchars.index(ch) for ch in doc] + [BOS]
n = min(block_size, len(tokens) - 1)
# Forward the token sequence through the model, building up the computation graph all the way to the loss.
keys, values = [[] for _ in range(n_layer)], [[] for _ in range(n_layer)]
losses = []
for pos_id in range(n):
token_id, target_id = tokens[pos_id], tokens[pos_id + 1]
logits = gpt(token_id, pos_id, keys, values)
probs = softmax(logits)
loss_t = -probs[target_id].log()
losses.append(loss_t)
loss = (1 / n) * sum(losses) # final average loss over the document sequence. May yours be low.
# Backward the loss, calculating the gradients with respect to all model parameters.
loss.backward()
# Adam optimizer update: update the model parameters based on the corresponding gradients.
lr_t = learning_rate * (1 - step / num_steps) # linear learning rate decay
for i, p in enumerate(params):
m[i] = beta1 * m[i] + (1 - beta1) * p.grad
v[i] = beta2 * v[i] + (1 - beta2) * p.grad ** 2
m_hat = m[i] / (1 - beta1 ** (step + 1))
v_hat = v[i] / (1 - beta2 ** (step + 1))
p.data -= lr_t * m_hat / (v_hat ** 0.5 + eps_adam)
p.grad = 0
print(f"step {step+1:4d} / {num_steps:4d} | loss {loss.data:.4f}")
# Inference: may the model babble back to us
temperature = 0.5 # in (0, 1], control the "creativity" of generated text, low to high
print("\n--- inference (new, hallucinated names) ---")
for sample_idx in range(20):
keys, values = [[] for _ in range(n_layer)], [[] for _ in range(n_layer)]
token_id = BOS
sample = []
for pos_id in range(block_size):
logits = gpt(token_id, pos_id, keys, values)
probs = softmax([l / temperature for l in logits])
token_id = random.choices(range(vocab_size), weights=[p.data for p in probs])[0]
if token_id == BOS:
break
sample.append(uchars[token_id])
print(f"sample {sample_idx+1:2d}: {''.join(sample)}")