bump version ro 0.2.5

frontend: Keep screen awake and scroll down when transcribing.

Dockerfile

@@ -21,10 +21,12 @@ RUN apt-get update && \
         python3 \
         python3-pip \
         ffmpeg \
-        git && \
+        git \
+        build-essential \
+        python3-dev && \
     rm -rf /var/lib/apt/lists/*
 
-RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
 
 COPY . .
 

README.md

@@ -13,23 +13,16 @@
 <a href="https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-MIT/Dual Licensed-dark_green"></a>
 </p>
 
-## Overview
 
-This project is based on [WhisperStreaming](https://github.com/ufal/whisper_streaming) and [SimulStreaming](https://github.com/ufal/SimulStreaming), allowing you to transcribe audio directly from your browser. WhisperLiveKit provides a complete backend solution for real-time speech transcription with a functional, simple and customizable frontend. Everything runs locally on your machine ✨
+WhisperLiveKit brings real-time speech transcription directly to your browser, with a ready-to-use backend+server and a simple frontend. ✨
 
-### Architecture
-
-WhisperLiveKit consists of three main components:
-
-- **Frontend**: A basic html + JS interface that captures microphone audio and streams it to the backend via WebSockets. You can use and adapt the [provided template](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/web/live_transcription.html).
-- **Backend (Web Server)**: A FastAPI-based WebSocket server that receives streamed audio data, processes it in real time, and returns transcriptions to the frontend. This is where the WebSocket logic and routing live.
-- **Core Backend (Library Logic)**: A server-agnostic core that handles audio processing, ASR, and diarization. It exposes reusable components that take in audio bytes and return transcriptions.
+Built on [SimulStreaming](https://github.com/ufal/SimulStreaming) (SOTA 2025) and [WhisperStreaming](https://github.com/ufal/whisper_streaming) (SOTA 2023) for transcription, plus [Streaming Sortformer](https://arxiv.org/abs/2507.18446) (SOTA 2025) and [Diart](https://github.com/juanmc2005/diart) (SOTA 2021) for diarization.
 
 
 ### Key Features
 
 - **Real-time Transcription** - Locally (or on-prem) convert speech to text instantly as you speak
-- **Speaker Diarization** - Identify different speakers in real-time using [Diart](https://github.com/juanmc2005/diart)
+- **Speaker Diarization** - Identify different speakers in real-time. (⚠️ backend Streaming Sortformer in developement)
 - **Multi-User Support** - Handle multiple users simultaneously with a single backend/server
 - **Automatic Silence Chunking** – Automatically chunks when no audio is detected to limit buffer size
 - **Confidence Validation** – Immediately validate high-confidence tokens for faster inference (WhisperStreaming only)
@@ -37,6 +30,11 @@ WhisperLiveKit consists of three main components:
 - **Punctuation-Based Speaker Splitting [BETA]** - Align speaker changes with natural sentence boundaries for more readable transcripts
 - **SimulStreaming Backend** - [Dual-licensed](https://github.com/ufal/SimulStreaming#-licence-and-contributions) - Ultra-low latency transcription using SOTA AlignAtt policy. 
 
+### Architecture
+
+<img alt="Architecture" src="architecture.png" />
+
+
 ## Quick Start
 
 ```bash
@@ -247,7 +245,7 @@ To deploy WhisperLiveKit in production:
    - Ensure WebSocket connection points to your server's address
 
 3. **Nginx Configuration** (recommended for production):
-```nginx
+    ```nginx    
    server {
        listen 80;
        server_name your-domain.com;
@@ -258,6 +256,7 @@ To deploy WhisperLiveKit in production:
         proxy_set_header Connection "upgrade";
         proxy_set_header Host $host;
     }}
+    ```
 
 4. **HTTPS Support**: For secure deployments, use "wss://" instead of "ws://" in WebSocket URL
 
@@ -268,21 +267,21 @@ A basic Dockerfile is provided which allows re-use of Python package installatio
 
 #### All defaults
 - Create a reusable image with only the basics and then run as a named container:
-```bash
-docker build -t whisperlivekit-defaults .
-docker create --gpus all --name whisperlivekit -p 8000:8000 whisperlivekit-defaults
-docker start -i whisperlivekit
-```
+    ```bash
+    docker build -t whisperlivekit-defaults .
+    docker create --gpus all --name whisperlivekit -p 8000:8000 whisperlivekit-defaults
+    docker start -i whisperlivekit
+    ```
 
-> **Note**: If you're running on a system without NVIDIA GPU support (such as Mac with Apple Silicon or any system without CUDA capabilities), you need to **remove the `--gpus all` flag** from the `docker create` command. Without GPU acceleration, transcription will use CPU only, which may be significantly slower. Consider using small models for better performance on CPU-only systems.
+    > **Note**: If you're running on a system without NVIDIA GPU support (such as Mac with Apple Silicon or any system without CUDA capabilities), you need to **remove the `--gpus all` flag** from the `docker create` command. Without GPU acceleration, transcription will use CPU only, which may be significantly slower. Consider using small models for better performance on CPU-only systems.
 
 #### Customization
 - Customize the container options:
-```bash
-docker build -t whisperlivekit-defaults .
-docker create --gpus all --name whisperlivekit-base -p 8000:8000 whisperlivekit-defaults --model base
-docker start -i whisperlivekit-base
-```
+    ```bash
+    docker build -t whisperlivekit-defaults .
+    docker create --gpus all --name whisperlivekit-base -p 8000:8000 whisperlivekit-defaults --model base
+    docker start -i whisperlivekit-base
+    ```
 
 - `--build-arg` Options:
   - `EXTRAS="whisper-timestamped"` - Add extras to the image's installation (no spaces). Remember to set necessary container options!

pyproject.toml

@@ -0,0 +1,59 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "whisperlivekit"
+version = "0.2.5"
+description = "Real-time, Fully Local Whisper's Speech-to-Text and Speaker Diarization"
+readme = "README.md"
+authors = [
+    { name = "Quentin Fuxa" }
+]
+license = { file = "LICENSE" }
+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",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Multimedia :: Sound/Audio :: Speech"
+]
+dependencies = [
+    "fastapi",
+    "librosa",
+    "soundfile",
+    "faster-whisper",
+    "uvicorn",
+    "websockets"
+]
+
+[project.optional-dependencies]
+diarization = ["diart"]
+vac = ["torch"]
+sentence = ["mosestokenizer", "wtpsplit"]
+whisper = ["whisper"]
+whisper-timestamped = ["whisper-timestamped"]
+mlx-whisper = ["mlx-whisper"]
+openai = ["openai"]
+simulstreaming = [
+    "torch",
+    "tqdm",
+    "tiktoken",
+    'triton>=2.0.0,<3; platform_machine == "x86_64" and (sys_platform == "linux" or sys_platform == "linux2")'
+]
+
+[project.urls]
+Homepage = "https://github.com/QuentinFuxa/WhisperLiveKit"
+
+[project.scripts]
+whisperlivekit-server = "whisperlivekit.basic_server:main"
+
+[tool.setuptools]
+packages = ["whisperlivekit", "whisperlivekit.diarization", "whisperlivekit.simul_whisper", "whisperlivekit.simul_whisper.whisper", "whisperlivekit.simul_whisper.whisper.assets", "whisperlivekit.simul_whisper.whisper.normalizers", "whisperlivekit.web", "whisperlivekit.whisper_streaming_custom"]
+
+[tool.setuptools.package-data]
+whisperlivekit = ["web/*.html"]
+"whisperlivekit.simul_whisper.whisper.assets" = ["*.tiktoken", "*.npz"]

setup.py

@@ -1,55 +0,0 @@
-from setuptools import setup, find_packages
-setup(
-    name="whisperlivekit",
-    version="0.2.1",
-    description="Real-time, Fully Local Whisper's Speech-to-Text and Speaker Diarization",
-    long_description=open("README.md", "r", encoding="utf-8").read(),
-    long_description_content_type="text/markdown",
-    author="Quentin Fuxa",
-    url="https://github.com/QuentinFuxa/WhisperLiveKit",
-    packages=find_packages(),
-    install_requires=[
-        "fastapi",
-        "librosa",
-        "soundfile",
-        "faster-whisper",
-        "uvicorn",
-        "websockets",
-    ],
-    extras_require={
-        "diarization": ["diart"],
-        "vac": ["torch"],
-        "sentence": ["mosestokenizer", "wtpsplit"],
-        "whisper": ["whisper"],
-        "whisper-timestamped": ["whisper-timestamped"],
-        "mlx-whisper": ["mlx-whisper"],
-        "openai": ["openai"],
-        "simulstreaming": [
-            "torch",
-            "tqdm",
-            "tiktoken",
-            "numpy<2.0.0",
-            "triton>=2.0.0,<3;platform_machine==\"x86_64\" and sys_platform==\"linux\" or sys_platform==\"linux2\"",
-        ],
-    },
-    package_data={
-        'whisperlivekit': ['web/*.html'],
-        'whisperlivekit.simul_whisper': ['dual_license_simulstreaming.md'],
-        'whisperlivekit.simul_whisper.whisper.assets': ['*.tiktoken', '*.npz'],
-    },
-    entry_points={
-        'console_scripts': [
-            'whisperlivekit-server=whisperlivekit.basic_server:main',
-        ],
-    },
-    classifiers=[
-        "Development Status :: 4 - Beta",
-        "Intended Audience :: Developers",
-        "License :: OSI Approved :: MIT License",
-        "Programming Language :: Python :: 3.9",
-        "Programming Language :: Python :: 3.10",
-        "Topic :: Scientific/Engineering :: Artificial Intelligence",
-        "Topic :: Multimedia :: Sound/Audio :: Speech",
-    ],
-    python_requires=">=3.9",
-)

whisperlivekit/__init__.py

@@ -1,4 +1,3 @@
-from .download_simulstreaming_backend import download_simulstreaming_backend
 from .audio_processor import AudioProcessor
 from .core import TranscriptionEngine
 from .parse_args import parse_args

whisperlivekit/audio_processor.py

@@ -6,10 +6,9 @@ import logging
 import traceback
 from datetime import timedelta
 from whisperlivekit.timed_objects import ASRToken
-from whisperlivekit.whisper_streaming_custom.whisper_online import online_factory
-from whisperlivekit.core import TranscriptionEngine
+from whisperlivekit.core import TranscriptionEngine, online_factory
 from whisperlivekit.ffmpeg_manager import FFmpegManager, FFmpegState
-
+from .remove_silences import handle_silences
 # Set up logging once
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)
@@ -52,7 +51,6 @@ class AudioProcessor:
         self.tokens = []
         self.buffer_transcription = ""
         self.buffer_diarization = ""
-        self.full_transcription = ""
         self.end_buffer = 0
         self.end_attributed_speaker = 0
         self.lock = asyncio.Lock()
@@ -96,13 +94,12 @@ class AudioProcessor:
         """Convert PCM buffer in s16le format to normalized NumPy array."""
         return np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32) / 32768.0
 
-    async def update_transcription(self, new_tokens, buffer, end_buffer, full_transcription, sep):
+    async def update_transcription(self, new_tokens, buffer, end_buffer, sep):
         """Thread-safe update of transcription with new data."""
         async with self.lock:
             self.tokens.extend(new_tokens)
             self.buffer_transcription = buffer
             self.end_buffer = end_buffer
-            self.full_transcription = full_transcription
             self.sep = sep
             
     async def update_diarization(self, end_attributed_speaker, buffer_diarization=""):
@@ -129,12 +126,12 @@ class AudioProcessor:
             # Calculate remaining times
             remaining_transcription = 0
             if self.end_buffer > 0:
-                remaining_transcription = max(0, round(current_time - self.beg_loop - self.end_buffer, 2))
+                remaining_transcription = max(0, round(current_time - self.beg_loop - self.end_buffer, 1))
                 
             remaining_diarization = 0
             if self.tokens:
                 latest_end = max(self.end_buffer, self.tokens[-1].end if self.tokens else 0)
-                remaining_diarization = max(0, round(latest_end - self.end_attributed_speaker, 2))
+                remaining_diarization = max(0, round(latest_end - self.end_attributed_speaker, 1))
                 
             return {
                 "tokens": self.tokens.copy(),
@@ -153,7 +150,6 @@ class AudioProcessor:
             self.tokens = []
             self.buffer_transcription = self.buffer_diarization = ""
             self.end_buffer = self.end_attributed_speaker = 0
-            self.full_transcription = self.last_response_content = ""
             self.beg_loop = time()
 
     async def ffmpeg_stdout_reader(self):
@@ -192,12 +188,6 @@ class AudioProcessor:
                         continue
                     
                 self.pcm_buffer.extend(chunk)
-                        
-                # Send to diarization if enabled
-                if self.args.diarization and self.diarization_queue:
-                    await self.diarization_queue.put(
-                        self.convert_pcm_to_float(self.pcm_buffer).copy()
-                    )
 
                 # Process when enough data
                 if len(self.pcm_buffer) >= self.bytes_per_sec:
@@ -214,7 +204,11 @@ class AudioProcessor:
                     # Send to transcription if enabled
                     if self.args.transcription and self.transcription_queue:
                         await self.transcription_queue.put(pcm_array.copy())
-                    
+
+                    # Send to diarization if enabled
+                    if self.args.diarization and self.diarization_queue:
+                        await self.diarization_queue.put(pcm_array.copy())
+
                     # Sleep if no processing is happening
                     if not self.args.transcription and not self.args.diarization:
                         await asyncio.sleep(0.1)
@@ -240,7 +234,6 @@ class AudioProcessor:
 
     async def transcription_processor(self):
         """Process audio chunks for transcription."""
-        self.full_transcription = ""
         self.sep = self.online.asr.sep
         cumulative_pcm_duration_stream_time = 0.0
         
@@ -252,7 +245,7 @@ class AudioProcessor:
                     self.transcription_queue.task_done()
                     break
                 
-                if not self.online: # Should not happen if queue is used
+                if not self.online:
                     logger.warning("Transcription processor: self.online not initialized.")
                     self.transcription_queue.task_done()
                     continue
@@ -279,8 +272,6 @@ class AudioProcessor:
 
                 if new_tokens:
                     validated_text = self.sep.join([t.text for t in new_tokens])
-                    self.full_transcription += validated_text
-                    
                     if buffer_text.startswith(validated_text):
                         buffer_text = buffer_text[len(validated_text):].lstrip()
 
@@ -297,7 +288,7 @@ class AudioProcessor:
                 new_end_buffer = max(candidate_end_times)
                 
                 await self.update_transcription(
-                    new_tokens, buffer_text, new_end_buffer, self.full_transcription, self.sep
+                    new_tokens, buffer_text, new_end_buffer, self.sep
                 )
                 self.transcription_queue.task_done()
                 
@@ -325,12 +316,12 @@ class AudioProcessor:
                 await diarization_obj.diarize(pcm_array)
                 
                 async with self.lock:
-                    new_end = diarization_obj.assign_speakers_to_tokens(
-                        self.end_attributed_speaker, 
+                    self.tokens = diarization_obj.assign_speakers_to_tokens(
                         self.tokens,
                         use_punctuation_split=self.args.punctuation_split
                     )
-                    self.end_attributed_speaker = new_end
+                    if len(self.tokens) > 0:
+                        self.end_attributed_speaker = max(self.tokens[-1].end, self.end_attributed_speaker)
                     if buffer_diarization:
                         self.buffer_diarization = buffer_diarization
                 
@@ -346,6 +337,8 @@ class AudioProcessor:
 
     async def results_formatter(self):
         """Format processing results for output."""
+        last_sent_trans = None
+        last_sent_diar = None
         while True:
             try:
                 ffmpeg_state = await self.ffmpeg_manager.get_state()
@@ -383,8 +376,8 @@ class AudioProcessor:
                 lines = []
                 last_end_diarized = 0
                 undiarized_text = []
-                
-                # Process each token
+                current_time = time() - self.beg_loop
+                tokens = handle_silences(tokens, current_time)
                 for token in tokens:
                     speaker = token.speaker
                     
@@ -449,10 +442,19 @@ class AudioProcessor:
                                            ' '.join([f"{line['speaker']} {line['text']}" for line in final_lines_for_response]) + \
                                            f" | {buffer_transcription} | {buffer_diarization}"
                 
-                if current_response_signature != self.last_response_content and \
-                   (final_lines_for_response or buffer_transcription or buffer_diarization or response_status == "no_audio_detected"):
+                trans = state["remaining_time_transcription"]
+                diar = state["remaining_time_diarization"]
+                should_push = (
+                    current_response_signature != self.last_response_content
+                    or last_sent_trans is None
+                    or round(trans, 1) != round(last_sent_trans, 1)
+                    or round(diar, 1) != round(last_sent_diar, 1)
+                )
+                if should_push and (final_lines_for_response or buffer_transcription or buffer_diarization or response_status == "no_audio_detected" or trans > 0 or diar > 0):
                     yield response
                     self.last_response_content = current_response_signature
+                    last_sent_trans = trans
+                    last_sent_diar = diar
                 
                 # Check for termination condition
                 if self.is_stopping:

whisperlivekit/core.py

@@ -1,9 +1,12 @@
 try:
-    from whisperlivekit.whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
+    from whisperlivekit.whisper_streaming_custom.whisper_online import backend_factory
+    from whisperlivekit.whisper_streaming_custom.online_asr import VACOnlineASRProcessor, OnlineASRProcessor
 except ImportError:
-    from .whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
+    from .whisper_streaming_custom.whisper_online import backend_factory
+    from .whisper_streaming_custom.online_asr import VACOnlineASRProcessor, OnlineASRProcessor
+from whisperlivekit.warmup import warmup_asr, warmup_online
 from argparse import Namespace
-
+import sys
 
 class TranscriptionEngine:
     _instance = None
@@ -22,7 +25,6 @@ class TranscriptionEngine:
             "host": "localhost",
             "port": 8000,
             "warmup_file": None,
-            "confidence_validation": False,
             "diarization": False,
             "punctuation_split": False,
             "min_chunk_size": 0.5,
@@ -34,15 +36,15 @@ class TranscriptionEngine:
             "backend": "faster-whisper",
             "vac": False,
             "vac_chunk_size": 0.04,
-            "buffer_trimming": "segment",
-            "buffer_trimming_sec": 15,
             "log_level": "DEBUG",
             "ssl_certfile": None,
             "ssl_keyfile": None,
             "transcription": True,
             "vad": True,
-            "segmentation_model": "pyannote/segmentation-3.0",
-            "embedding_model": "pyannote/embedding",
+            # whisperstreaming params:
+            "buffer_trimming": "segment",
+            "confidence_validation": False,
+            "buffer_trimming_sec": 15,
             # simulstreaming params:
             "frame_threshold": 25,
             "beams": 1,
@@ -55,6 +57,10 @@ class TranscriptionEngine:
             "static_init_prompt": None,
             "max_context_tokens": None,
             "model_path": './base.pt',
+            # diart params:
+            "segmentation_model": "pyannote/segmentation-3.0",
+            "embedding_model": "pyannote/embedding",
+
         }
 
         config_dict = {**defaults, **kwargs}
@@ -78,8 +84,32 @@ class TranscriptionEngine:
         self.diarization = None
         
         if self.args.transcription:
-            self.asr, self.tokenizer = backend_factory(self.args)
-            warmup_asr(self.asr, self.args.warmup_file)
+            if self.args.backend == "simulstreaming": 
+                from simul_whisper import SimulStreamingASR
+                self.tokenizer = None
+                simulstreaming_kwargs = {}
+                for attr in ['frame_threshold', 'beams', 'decoder_type', 'audio_max_len', 'audio_min_len', 
+                            'cif_ckpt_path', 'never_fire', 'init_prompt', 'static_init_prompt', 
+                            'max_context_tokens', 'model_path']:
+                    if hasattr(self.args, attr):
+                        simulstreaming_kwargs[attr] = getattr(self.args, attr)
+        
+                # Add segment_length from min_chunk_size
+                simulstreaming_kwargs['segment_length'] = getattr(self.args, 'min_chunk_size', 0.5)
+                simulstreaming_kwargs['task'] = self.args.task
+                
+                size = self.args.model
+                self.asr = SimulStreamingASR(
+                    modelsize=size,
+                    lan=self.args.lan,
+                    cache_dir=getattr(self.args, 'model_cache_dir', None),
+                    model_dir=getattr(self.args, 'model_dir', None),
+                    **simulstreaming_kwargs
+                )
+
+            else:
+                self.asr, self.tokenizer = backend_factory(self.args)
+            warmup_asr(self.asr, self.args.warmup_file) #for simulstreaming, warmup should be done in the online class not here
 
         if self.args.diarization:
             from whisperlivekit.diarization.diarization_online import DiartDiarization
@@ -90,3 +120,33 @@ class TranscriptionEngine:
             )
             
         TranscriptionEngine._initialized = True
+
+
+
+def online_factory(args, asr, tokenizer, logfile=sys.stderr):
+    if args.backend == "simulstreaming":    
+        from simul_whisper import SimulStreamingOnlineProcessor
+        online = SimulStreamingOnlineProcessor(
+            asr,
+            logfile=logfile,
+        )
+        # warmup_online(online, args.warmup_file)
+    elif args.vac:
+        online = VACOnlineASRProcessor(
+            args.min_chunk_size,
+            asr,
+            tokenizer,
+            logfile=logfile,
+            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
+            confidence_validation = args.confidence_validation
+        )
+    else:
+        online = OnlineASRProcessor(
+            asr,
+            tokenizer,
+            logfile=logfile,
+            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
+            confidence_validation = args.confidence_validation
+        )
+    return online
+  

whisperlivekit/diarization/diarization_online.py

@@ -165,7 +165,7 @@ class WebSocketAudioSource(AudioSource):
 
 
 class DiartDiarization:
-    def __init__(self, sample_rate: int = 16000, config : SpeakerDiarizationConfig = None, use_microphone: bool = False, block_duration: float = 0.5, segmentation_model_name: str = "pyannote/segmentation-3.0", embedding_model_name: str = "speechbrain/spkrec-ecapa-voxceleb"):
+    def __init__(self, sample_rate: int = 16000, config : SpeakerDiarizationConfig = None, use_microphone: bool = False, block_duration: float = 1.5, segmentation_model_name: str = "pyannote/segmentation-3.0", embedding_model_name: str = "pyannote/embedding"):
         segmentation_model = m.SegmentationModel.from_pretrained(segmentation_model_name)
         embedding_model = m.EmbeddingModel.from_pretrained(embedding_model_name)
         
@@ -206,15 +206,14 @@ class DiartDiarization:
         """
         if self.custom_source:
             self.custom_source.push_audio(pcm_array)            
-        self.observer.clear_old_segments()        
-        return self.observer.get_segments()
+        # self.observer.clear_old_segments()        
 
     def close(self):
         """Close the audio source."""
         if self.custom_source:
             self.custom_source.close()
 
-    def assign_speakers_to_tokens(self, end_attributed_speaker, tokens: list, use_punctuation_split: bool = False) -> float:
+    def assign_speakers_to_tokens(self, tokens: list, use_punctuation_split: bool = False) -> float:
         """
         Assign speakers to tokens based on timing overlap with speaker segments.
         Uses the segments collected by the observer.
@@ -231,85 +230,82 @@ class DiartDiarization:
         
         if not self.lag_diart and segments and tokens:
             self.lag_diart = segments[0].start - tokens[0].start
-        for token in tokens:
-            for segment in segments:
-                if not (segment.end <= token.start + self.lag_diart or segment.start >= token.end + self.lag_diart):
-                    token.speaker = extract_number(segment.speaker) + 1
-                    end_attributed_speaker = max(token.end, end_attributed_speaker)
         
-        if use_punctuation_split and len(tokens) > 1:
-            punctuation_marks = {'.', '!', '?'}
-            
-            print("Here are the tokens:", 
-                  [(t.text, t.start, t.end, t.speaker) for t in tokens[:10]])
-            
-            segment_map = []
-            for segment in segments:
-                speaker_num = extract_number(segment.speaker) + 1
-                segment_map.append((segment.start, segment.end, speaker_num))
-            segment_map.sort(key=lambda x: x[0])
-            
-            i = 0
-            while i < len(tokens):
-                current_token = tokens[i]
-                
-                is_sentence_end = False
-                if current_token.text and current_token.text.strip():
-                    text = current_token.text.strip()
-                    if text[-1] in punctuation_marks:
-                        is_sentence_end = True
-                        logger.debug(f"Token {i} ends sentence: '{current_token.text}' at {current_token.end:.2f}s")
-                
-                if is_sentence_end and current_token.speaker != -1:
-                    punctuation_time = current_token.end
-                    current_speaker = current_token.speaker
-                    
-                    j = i + 1
-                    next_sentence_tokens = []                    
-                    while j < len(tokens):
-                        next_token = tokens[j]
-                        next_sentence_tokens.append(j)
-                        
-                        # Check if this token ends the next sentence
-                        if next_token.text and next_token.text.strip():
-                            if next_token.text.strip()[-1] in punctuation_marks:
-                                break
-                        j += 1
-                    
-                    if next_sentence_tokens:
-                        speaker_times = {}
-                        
-                        for idx in next_sentence_tokens:
-                            token = tokens[idx]
-                            # Find which segments overlap with this token
-                            for seg_start, seg_end, seg_speaker in segment_map:
-                                if not (seg_end <= token.start or seg_start >= token.end):
-                                    # Calculate overlap duration
-                                    overlap_start = max(seg_start, token.start)
-                                    overlap_end = min(seg_end, token.end)
-                                    overlap_duration = overlap_end - overlap_start
-                                    
-                                    if seg_speaker not in speaker_times:
-                                        speaker_times[seg_speaker] = 0
-                                    speaker_times[seg_speaker] += overlap_duration
-                        
-                        if speaker_times:
-                            dominant_speaker = max(speaker_times.items(), key=lambda x: x[1])[0]
-                            
-                            if dominant_speaker != current_speaker:
-                                logger.debug(f"  Speaker change after punctuation: {current_speaker} → {dominant_speaker}")
-                                
-                                for idx in next_sentence_tokens:
-                                    if tokens[idx].speaker != dominant_speaker:
-                                        logger.debug(f"    Reassigning token {idx} ('{tokens[idx].text}') to Speaker {dominant_speaker}")
-                                        tokens[idx].speaker = dominant_speaker
-                                        end_attributed_speaker = max(tokens[idx].end, end_attributed_speaker)
-                            else:
-                                for idx in next_sentence_tokens:
-                                    if tokens[idx].speaker == -1:
-                                        tokens[idx].speaker = current_speaker
-                                        end_attributed_speaker = max(tokens[idx].end, end_attributed_speaker)
-                
-                i += 1         
+        if not use_punctuation_split:
+            for token in tokens:
+                for segment in segments:
+                    if not (segment.end <= token.start + self.lag_diart or segment.start >= token.end + self.lag_diart):
+                        token.speaker = extract_number(segment.speaker) + 1
+        else:
+            tokens = add_speaker_to_tokens(segments, tokens)
+        return tokens
         
-        return end_attributed_speaker
+def concatenate_speakers(segments):
+    segments_concatenated = [{"speaker": 1, "begin": 0.0, "end": 0.0}]
+    for segment in segments:
+        speaker = extract_number(segment.speaker) + 1
+        if segments_concatenated[-1]['speaker'] != speaker:
+            segments_concatenated.append({"speaker": speaker, "begin": segment.start, "end": segment.end})
+        else:
+            segments_concatenated[-1]['end'] = segment.end
+    # print("Segments concatenated:")
+    # for entry in segments_concatenated:
+    #     print(f"Speaker {entry['speaker']}: {entry['begin']:.2f}s - {entry['end']:.2f}s")   
+    return segments_concatenated
+
+
+def add_speaker_to_tokens(segments, tokens):
+    """
+    Assign speakers to tokens based on diarization segments, with punctuation-aware boundary adjustment.
+    """
+    punctuation_marks = {'.', '!', '?'}
+    punctuation_tokens = [token for token in tokens if token.text.strip() in punctuation_marks]
+    segments_concatenated = concatenate_speakers(segments)
+    for ind, segment in enumerate(segments_concatenated):
+            for i, punctuation_token in enumerate(punctuation_tokens):
+                if punctuation_token.start > segment['end']:
+                    after_length = punctuation_token.start - segment['end']
+                    before_length = segment['end'] - punctuation_tokens[i - 1].end
+                    if before_length > after_length:
+                        segment['end'] = punctuation_token.start
+                        if i < len(punctuation_tokens) - 1 and ind + 1 < len(segments_concatenated):
+                            segments_concatenated[ind + 1]['begin'] = punctuation_token.start
+                    else:
+                        segment['end'] = punctuation_tokens[i - 1].end
+                        if i < len(punctuation_tokens) - 1 and ind - 1 >= 0:
+                            segments_concatenated[ind - 1]['begin'] = punctuation_tokens[i - 1].end
+                    break
+
+    last_end = 0.0
+    for token in tokens:
+        start = max(last_end + 0.01, token.start)
+        token.start = start
+        token.end = max(start, token.end)
+        last_end = token.end
+
+    ind_last_speaker = 0
+    for segment in segments_concatenated:
+        for i, token in enumerate(tokens[ind_last_speaker:]):
+            if token.end <= segment['end']:
+                token.speaker = segment['speaker']
+                ind_last_speaker = i + 1
+                # print(
+                #     f"Token '{token.text}' ('begin': {token.start:.2f}, 'end': {token.end:.2f}) "
+                #     f"assigned to Speaker {segment['speaker']} ('segment': {segment['begin']:.2f}-{segment['end']:.2f})"
+                # )
+            elif token.start > segment['end']:
+                break
+    return tokens
+
+
+def visualize_tokens(tokens):
+    conversation = [{"speaker": -1, "text": ""}]
+    for token in tokens:
+        speaker = conversation[-1]['speaker']
+        if token.speaker != speaker:
+            conversation.append({"speaker": token.speaker, "text": token.text})
+        else:
+            conversation[-1]['text'] += token.text
+    print("Conversation:")
+    for entry in conversation:
+        print(f"Speaker {entry['speaker']}: {entry['text']}")

whisperlivekit/download_simulstreaming_backend.py

@@ -1,32 +0,0 @@
-import os
-import requests
-import inspect
-
-def get_module_path():
-    return os.path.dirname(inspect.getfile(inspect.currentframe()))
-
-GITHUB_API_URL = "https://api.github.com/repos/ufal/SimulStreaming/contents/simul_whisper/whisper"
-RAW_BASE_URL = "https://raw.githubusercontent.com/ufal/SimulStreaming/main/simul_whisper/whisper"
-TARGET_DIR = os.path.join(get_module_path(), "simul_whisper", "whisper")
-
-def download_files_from_github(api_url, local_dir):
-    os.makedirs(local_dir, exist_ok=True)
-    response = requests.get(api_url)
-    response.raise_for_status()
-    items = response.json()
-    for item in items:
-        if item['type'] == 'file':
-            download_url = item['download_url']
-            file_name = item['name']
-            file_response = requests.get(download_url)
-            file_response.raise_for_status()
-            with open(os.path.join(local_dir, file_name), 'wb') as f:
-                f.write(file_response.content)
-        elif item['type'] == 'dir':
-            # Recursive call for subdirectories
-            download_files_from_github(item['url'], os.path.join(local_dir, item['name']))
-
-def download_simulstreaming_backend():
-    print(f"Downloading files into {TARGET_DIR} ...")
-    download_files_from_github(GITHUB_API_URL, TARGET_DIR)
-    print("✅ Download of SimulStreaming backend files completed successfully.")

whisperlivekit/remove_silences.py

@@ -0,0 +1,103 @@
+from whisperlivekit.timed_objects import ASRToken
+import re
+
+MIN_SILENCE_DURATION = 4 #in seconds
+END_SILENCE_DURATION = 8 #in seconds. you should keep it important to not have false positive when the model lag is important
+
+def blank_to_silence(tokens):
+    full_string = ''.join([t.text for t in tokens])
+    patterns = [re.compile(r'(?:\s*\[BLANK_AUDIO\]\s*)+'), re.compile(r'(?:\s*\[typing\]\s*)+')]             
+    matches = []
+    for pattern in patterns:
+        for m in pattern.finditer(full_string):
+            matches.append({
+                'start': m.start(),
+                'end': m.end()
+            })
+    if matches:
+        # cleaned = pattern.sub(' ', full_string).strip()
+        # print("Cleaned:", cleaned)
+        cumulated_len = 0
+        silence_token = None
+        cleaned_tokens = []
+        for token in tokens:
+            if matches:
+                start = cumulated_len
+                end = cumulated_len + len(token.text)
+                cumulated_len = end
+                if start >= matches[0]['start'] and end <= matches[0]['end']:
+                    if silence_token: #previous token was already silence
+                        silence_token.start = min(silence_token.start, token.start)
+                        silence_token.end = max(silence_token.end, token.end)
+                    else: #new silence
+                        silence_token = ASRToken(
+                            start=token.start,
+                            end=token.end,
+                            speaker=-2,
+                            probability=0.95
+                        )
+                else:
+                    if silence_token: #there was silence but no more
+                        if silence_token.end - silence_token.start >= MIN_SILENCE_DURATION:
+                            cleaned_tokens.append(
+                                silence_token
+                            )
+                        silence_token = None
+                        matches.pop(0)
+                    cleaned_tokens.append(token)
+        # print(cleaned_tokens)    
+        return cleaned_tokens
+    return tokens
+
+def no_token_to_silence(tokens):
+    new_tokens = []
+    silence_token = None
+    for token in tokens:
+        if token.speaker == -2:
+            if new_tokens and new_tokens[-1].speaker == -2: #if token is silence and previous one too
+                new_tokens[-1].end = token.end
+            else:
+                new_tokens.append(token)
+            
+        last_end = new_tokens[-1].end if new_tokens else 0.0
+        if token.start - last_end >= MIN_SILENCE_DURATION: #if token is not silence but important gap
+            if new_tokens and new_tokens[-1].speaker == -2:
+                new_tokens[-1].end = token.start
+            else:
+                silence_token = ASRToken(
+                    start=last_end,
+                    end=token.start,
+                    speaker=-2,
+                    probability=0.95
+                    )
+                new_tokens.append(silence_token)
+        
+        if token.speaker != -2:
+            new_tokens.append(token)
+    return new_tokens
+            
+def ends_with_silence(tokens, current_time):
+    if not tokens:
+        return []
+    last_token = tokens[-1]
+    if tokens and current_time - last_token.end >= END_SILENCE_DURATION:
+        if last_token.speaker == -2:
+            last_token.end = current_time
+        else:
+            tokens.append(
+                ASRToken(
+                    start=tokens[-1].end,
+                    end=current_time,
+                    speaker=-2,
+                    probability=0.95
+                )
+            )
+    return tokens
+    
+
+def handle_silences(tokens, current_time):
+    tokens = blank_to_silence(tokens) #useful for simulstreaming backend which tends to generate [BLANK_AUDIO] text
+    tokens = no_token_to_silence(tokens)
+    tokens = ends_with_silence(tokens, current_time)
+    return tokens
+     

whisperlivekit/simul_whisper/__init__.py

@@ -0,0 +1,6 @@
+from .backend import SimulStreamingASR, SimulStreamingOnlineProcessor
+
+__all__ = [
+    "SimulStreamingASR",
+    "SimulStreamingOnlineProcessor",
+]

whisperlivekit/simul_whisper/backend.py

@@ -0,0 +1,223 @@
+import sys
+import numpy as np
+import logging
+from typing import List, Tuple, Optional
+import logging
+from whisperlivekit.timed_objects import ASRToken, Transcript
+from whisperlivekit.simul_whisper.license_simulstreaming import SIMULSTREAMING_LICENSE
+from .whisper import load_model, tokenizer
+import os
+logger = logging.getLogger(__name__)
+
+try:
+    import torch
+    from whisperlivekit.simul_whisper.config import AlignAttConfig
+    from whisperlivekit.simul_whisper.simul_whisper import PaddedAlignAttWhisper
+    from whisperlivekit.simul_whisper.whisper import tokenizer
+except ImportError as e:
+    raise ImportError(
+        """SimulStreaming dependencies are not available.
+        Please install WhisperLiveKit using pip install "whisperlivekit[simulstreaming]".""")
+
+class SimulStreamingOnlineProcessor:
+    SAMPLING_RATE = 16000
+
+    def __init__(
+        self,
+        asr,
+        logfile=sys.stderr,
+        warmup_file=None
+    ):        
+        self.asr = asr
+        self.logfile = logfile
+        self.is_last = False
+        self.beg = 0.0
+        self.end = 0.0
+        self.cumulative_audio_duration = 0.0
+        
+        self.committed: List[ASRToken] = []
+        self.last_result_tokens: List[ASRToken] = []        
+        self.model = PaddedAlignAttWhisper(
+            cfg=asr.cfg,
+            loaded_model=asr.whisper_model)
+        if asr.tokenizer:
+            self.model.tokenizer = asr.tokenizer
+
+    def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time: Optional[float] = None):
+        """Append an audio chunk to be processed by SimulStreaming."""
+            
+        # Convert numpy array to torch tensor
+        audio_tensor = torch.from_numpy(audio).float()
+        
+        # Update timing
+        chunk_duration = len(audio) / self.SAMPLING_RATE
+        self.cumulative_audio_duration += chunk_duration
+        
+        if audio_stream_end_time is not None:
+            self.end = audio_stream_end_time
+        else:
+            self.end = self.cumulative_audio_duration            
+        self.model.insert_audio(audio_tensor)
+
+    def get_buffer(self):
+        return Transcript(
+            start=None, 
+            end=None, 
+            text='', 
+            probability=None
+        )
+
+    def timestamped_text(self, tokens, generation):
+        # From the simulstreaming repo. self.model to self.asr.model
+        pr = generation["progress"]
+        if "result" not in generation:
+            split_words, split_tokens = self.model.tokenizer.split_to_word_tokens(tokens)
+        else:
+            split_words, split_tokens = generation["result"]["split_words"], generation["result"]["split_tokens"]
+
+        frames = [p["most_attended_frames"][0] for p in pr]
+        tokens = tokens.copy()
+        ret = []
+        for sw,st in zip(split_words,split_tokens):
+            b = None
+            for stt in st:
+                t,f = tokens.pop(0), frames.pop(0)
+                if t != stt:
+                    raise ValueError(f"Token mismatch: {t} != {stt} at frame {f}.")
+                if b is None:
+                    b = f
+            e = f
+            out = (b*0.02, e*0.02, sw)
+            ret.append(out)
+            logger.debug(f"TS-WORD:\t{' '.join(map(str, out))}")
+        return ret
+
+    def process_iter(self) -> Tuple[List[ASRToken], float]:
+        """
+        Process accumulated audio chunks using SimulStreaming.
+        
+        Returns a tuple: (list of committed ASRToken objects, float representing the audio processed up to time).
+        """
+        try:            
+            tokens, generation_progress = self.model.infer(is_last=self.is_last)
+            ts_words = self.timestamped_text(tokens, generation_progress)
+            
+            new_tokens = []
+            for ts_word in ts_words:
+                
+                start, end, word = ts_word
+                token = ASRToken(
+                    start=start,
+                    end=end,
+                    text=word,
+                    probability=0.95  # fake prob. Maybe we can extract it from the model?
+                )
+                new_tokens.append(token)
+                self.committed.extend(new_tokens)
+            
+            return new_tokens, self.end
+
+            
+        except Exception as e:
+            logger.exception(f"SimulStreaming processing error: {e}")
+            return [], self.end
+
+    def warmup(self, audio, init_prompt=""):
+        """Warmup the SimulStreaming model."""
+        try:
+            self.model.insert_audio(audio)
+            self.model.infer(True)
+            self.model.refresh_segment(complete=True)
+            logger.info("SimulStreaming model warmed up successfully")
+        except Exception as e:
+            logger.exception(f"SimulStreaming warmup failed: {e}")
+
+
+class SimulStreamingASR():
+    """SimulStreaming backend with AlignAtt policy."""
+    sep = ""
+
+    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr, **kwargs):
+        logger.warning(SIMULSTREAMING_LICENSE)
+        self.logfile = logfile
+        self.transcribe_kargs = {}
+        self.original_language = None if lan == "auto" else lan
+        
+        self.model_path = kwargs.get('model_path', './large-v3.pt')
+        self.frame_threshold = kwargs.get('frame_threshold', 25)
+        self.audio_max_len = kwargs.get('audio_max_len', 30.0)
+        self.audio_min_len = kwargs.get('audio_min_len', 0.0)
+        self.segment_length = kwargs.get('segment_length', 0.5)
+        self.beams = kwargs.get('beams', 1)
+        self.decoder_type = kwargs.get('decoder_type', 'greedy' if self.beams == 1 else 'beam')
+        self.task = kwargs.get('task', 'transcribe')
+        self.cif_ckpt_path = kwargs.get('cif_ckpt_path', None)
+        self.never_fire = kwargs.get('never_fire', False)
+        self.init_prompt = kwargs.get('init_prompt', None)
+        self.static_init_prompt = kwargs.get('static_init_prompt', None)
+        self.max_context_tokens = kwargs.get('max_context_tokens', None)
+        
+        if model_dir is not None:
+            self.model_path = model_dir
+        elif modelsize is not None:
+            model_mapping = {
+                'tiny': './tiny.pt',
+                'base': './base.pt',
+                'small': './small.pt',
+                'medium': './medium.pt',
+                'medium.en': './medium.en.pt',
+                'large-v1': './large-v1.pt',
+                'base.en': './base.en.pt',
+                'small.en': './small.en.pt',
+                'tiny.en': './tiny.en.pt',
+                'large-v2': './large-v2.pt',
+                'large-v3': './large-v3.pt',
+                'large': './large-v3.pt'
+            }
+            self.model_path = model_mapping.get(modelsize, f'./{modelsize}.pt')
+        
+        self.model = self.load_model(modelsize)
+        
+        # Set up tokenizer for translation if needed
+        if self.task == "translate":
+            self.tokenizer = self.set_translate_task()
+        else:
+            self.tokenizer = None
+
+
+    def load_model(self, modelsize):
+        self.cfg = AlignAttConfig(
+                model_path=self.model_path,
+                segment_length=self.segment_length,
+                frame_threshold=self.frame_threshold,
+                language=self.original_language,
+                audio_max_len=self.audio_max_len,
+                audio_min_len=self.audio_min_len,
+                cif_ckpt_path=self.cif_ckpt_path,
+                decoder_type="beam",
+                beam_size=self.beams,
+                task=self.task,
+                never_fire=self.never_fire,
+                init_prompt=self.init_prompt,
+                max_context_tokens=self.max_context_tokens,
+                static_init_prompt=self.static_init_prompt,
+        )   
+        model_name = os.path.basename(self.cfg.model_path).replace(".pt", "")
+        model_path = os.path.dirname(os.path.abspath(self.cfg.model_path))
+        self.whisper_model = load_model(name=model_name, download_root=model_path)
+        
+
+    def set_translate_task(self):
+        """Set up translation task."""
+        return tokenizer.get_tokenizer(
+            multilingual=True,
+            language=self.model.cfg.language,
+            num_languages=self.model.model.num_languages,
+            task="translate"
+        )
+
+    def transcribe(self, audio):
+        """
+        Only used for warmup. It's a direct whisper call, not a simulstreaming call
+        """
+        self.whisper_model.transcribe(audio, language=self.original_language)

whisperlivekit/simul_whisper/config.py

@@ -8,7 +8,7 @@ class SimulWhisperConfig:
     '''Options that are common for all simul policies that could be implemented in SimulWhisper.'''
     model_path: str
     language: str = field(default="zh")
-    nonspeech_prob: float = 1.0
+    nonspeech_prob: float = 0.5
     audio_min_len: float = 1.0
     decoder_type: Literal["greedy","beam"] = "greedy"
     beam_size: int = 5

whisperlivekit/simul_whisper/dual_license_simulstreaming.md

@@ -1,25 +0,0 @@
-📄 SimulStreaming (https://github.com/ufal/SimulStreaming) Licence
-
-SimulStreaming is dual-licensed:
-
-🔹 Non-Commercial Use
-
-You may use SimulStreaming under the **PolyForm Noncommercial License 1.0.0** if you
-obtain the code through the GitHub repository.  This license is **free of charge**
-and comes with **no obligations** for non-commercial users.
-
-🔸 Commercial Use
-
-Understanding who uses SimulStreaming commercially helps us improve and
-prioritize development. Therefore, we want to **require registration** of those who acquire a commercial licence.
-
-We plan to make the commercial licenceses **affordable** to SMEs and individuals. We
-are considering to provide commercial licenses either for free or for symbolic
-one-time fee, and maybe also provide additional support. You can share your preference via the [questionnaire](https://forms.cloud.microsoft/e/7tCxb4gJfB).
-
-You can also leave your contact [there](https://forms.cloud.microsoft/e/7tCxb4gJfB) to be notified when the commercial licenses become
-available.
-
-✉️ Contact
-
-[Dominik Macháček](https://ufal.mff.cuni.cz/dominik-machacek/), machacek@ufal.mff.cuni.cz

whisperlivekit/simul_whisper/generation_progress.py

@@ -25,6 +25,9 @@ class BeamTokens(Tokens):
     def __repr__(self):
         return self.__str__()
 
+    def as_text(self, tokenizer):
+        return tokenizer.decode(self.tokens)
+
 class Logits(Tokens):
     def __init__(self, logits):
         super().__init__(logits)

whisperlivekit/simul_whisper/license_simulstreaming.py

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

whisperlivekit/simul_whisper/simul_whisper.py

@@ -10,12 +10,12 @@ from .whisper import load_model, DecodingOptions, tokenizer
 from .config import AlignAttConfig
 from .whisper.audio import log_mel_spectrogram, TOKENS_PER_SECOND, pad_or_trim, N_SAMPLES, N_FRAMES
 from .whisper.timing import median_filter
-from .whisper.decoding import SuppressBlank, GreedyDecoder, BeamSearchDecoder, SuppressTokens
+from .whisper.decoding import GreedyDecoder, BeamSearchDecoder, SuppressTokens, detect_language
 from .beam import BeamPyTorchInference
 from .eow_detection import fire_at_boundary, load_cif
 import os
 
-from whisperlivekit.simul_whisper.token_buffer import TokenBuffer
+from .token_buffer import TokenBuffer
 
 import numpy as np
 from .generation_progress import *
@@ -24,6 +24,7 @@ DEC_PAD = 50257
 logger = logging.getLogger(__name__)
 
 import sys
+import wave
 
 # New features added to the original version of Simul-Whisper: 
 # - large-v3 model support
@@ -32,29 +33,30 @@ import sys
 # - prompt -- static vs. non-static
 # - context
 class PaddedAlignAttWhisper:
-    def __init__(self, cfg: AlignAttConfig) -> None:
+    def __init__(self, cfg: AlignAttConfig, loaded_model=None) -> None:
+        self.log_segments = 0
         model_name = os.path.basename(cfg.model_path).replace(".pt", "")
         model_path = os.path.dirname(os.path.abspath(cfg.model_path))
-        self.model = load_model(name=model_name, download_root=model_path)
+        if loaded_model:
+            self.model = loaded_model
+        else:
+            self.model = load_model(name=model_name, download_root=model_path)
 
         logger.info(f"Model dimensions: {self.model.dims}")
 
-        decode_options = DecodingOptions(
+        self.decode_options = DecodingOptions(
             language = cfg.language, 
             without_timestamps = True,
             task=cfg.task
         )
-        self.tokenizer = tokenizer.get_tokenizer(
-            multilingual=not model_name.endswith(".en"), 
-            language=cfg.language, 
-            num_languages=self.model.num_languages,
-            task=decode_options.task
-        )
+        self.tokenizer_is_multilingual = not model_name.endswith(".en")
+        self.create_tokenizer(cfg.language if cfg.language != "auto" else None)
+        self.detected_language = cfg.language if cfg.language != "auto" else None
+        
         self.max_text_len = self.model.dims.n_text_ctx
         self.num_decoder_layers = len(self.model.decoder.blocks)
         self.cfg = cfg
 
-
         # model to detect end-of-word boundary at the end of the segment
         self.CIFLinear, self.always_fire, self.never_fire = load_cif(cfg,
                                                                      n_audio_state=self.model.dims.n_audio_state,
@@ -95,14 +97,6 @@ class PaddedAlignAttWhisper:
             self.num_align_heads += 1
 
 
-        # init tokens (mandatory prompt)
-        self.initial_tokens = torch.tensor(
-            self.tokenizer.sot_sequence_including_notimestamps, 
-            dtype=torch.long, 
-            device=self.model.device).unsqueeze(0)
-        self.initial_token_length = self.initial_tokens.shape[1]
-        self.sot_index = self.tokenizer.sot_sequence.index(self.tokenizer.sot)
-
         # tokens to be suppressed from decoding, to prevent hallucinations
         suppress_tokens = [
                 self.tokenizer.transcribe,
@@ -121,6 +115,17 @@ class PaddedAlignAttWhisper:
         self.suppress_tokens = lambda logits: sup_tokens.apply(logits, None)
         # blank tokens are suppresed for new segments near the line 334
 
+        # it's going to be regenerated after lang id
+        self.segments = []
+        self.init_tokens()
+        
+        self.last_attend_frame = -self.cfg.rewind_threshold
+
+        if self.cfg.max_context_tokens is None:
+            self.max_context_tokens = self.max_text_len
+        else:
+            self.max_context_tokens = self.cfg.max_context_tokens
+        self.init_context()
 
         # decoder type: greedy or beam
         if cfg.decoder_type == "greedy":
@@ -135,16 +140,13 @@ class PaddedAlignAttWhisper:
 
             self.token_decoder = BeamSearchDecoder(inference=self.inference, eot=self.tokenizer.eot, beam_size=cfg.beam_size)
 
-        # init state
-        self.segments = []
-        self.tokens = [self.initial_tokens]
-        self.last_attend_frame = -self.cfg.rewind_threshold
-
-        if self.cfg.max_context_tokens is None:
-            self.max_context_tokens = self.max_text_len
-        else:
-            self.max_context_tokens = self.cfg.max_context_tokens
-        self.init_context()
+    def create_tokenizer(self, language=None):
+        self.tokenizer = tokenizer.get_tokenizer(
+            multilingual=self.tokenizer_is_multilingual,  
+            language=language,
+            num_languages=self.model.num_languages,
+            task=self.decode_options.task
+        )
 
     def init_context(self):
         kw = {'tokenizer': self.tokenizer, 
@@ -156,6 +158,19 @@ class PaddedAlignAttWhisper:
         if self.cfg.init_prompt is not None:
             self.context.text += self.cfg.init_prompt
 
+    def init_tokens(self):
+        logger.debug(f"init tokens, {len(self.segments)}")
+        # init tokens (mandatory prompt)
+        self.initial_tokens = torch.tensor(
+            self.tokenizer.sot_sequence_including_notimestamps, 
+            dtype=torch.long, 
+            device=self.model.device).unsqueeze(0)
+        self.initial_token_length = self.initial_tokens.shape[1]
+        self.sot_index = self.tokenizer.sot_sequence.index(self.tokenizer.sot)
+#        self.segments = []
+        logger.debug(f"init tokens after, {len(self.segments)}")
+        self.tokens = [self.initial_tokens]
+
     def trim_context(self):
         logger.info("Trimming context")
         c = len(self.context.as_token_ids()) - len(self.context.prefix_token_ids)
@@ -191,15 +206,19 @@ class PaddedAlignAttWhisper:
 
     def refresh_segment(self, complete=False):
 
-        logger.debug("Refreshing segment")
-        self.tokens = [self.initial_tokens] 
+        logger.debug("Refreshing segment:")
+        self.init_tokens()
         self.last_attend_frame = -self.cfg.rewind_threshold       
+        self.detected_language = None
         self.init_context()
         logger.debug(f"Context: {self.context}")
         if not complete and len(self.segments) > 2:
+            logger.debug("keeping last two segments because they are and it is not complete.")
             self.segments = self.segments[-2:]
         else:
+            logger.debug("removing all segments.")
             self.segments = []
+        self.log_segments += 1
 
 
     def fire_at_boundary(self, chunked_encoder_feature: torch.Tensor):
@@ -208,8 +227,6 @@ class PaddedAlignAttWhisper:
         return fire_at_boundary(chunked_encoder_feature, self.CIFLinear)
 
 
-
-
     def _current_tokens(self):
 
         toks = self.tokens
@@ -256,16 +273,59 @@ class PaddedAlignAttWhisper:
         removed_len = 0
         # len of audio is bigger than buffer_len. Going to remove the first segment
         segments_len = self.segments_len()
-        while segments_len > self.cfg.audio_max_len:
+        while len(self.segments) > 1 and segments_len > self.cfg.audio_max_len:
             removed_len = self.segments[0].shape[0] / 16000
             segments_len -= removed_len
             self.last_attend_frame -= int(TOKENS_PER_SECOND*removed_len)
             self.segments = self.segments[1:]
             logger.debug(f"remove segments: {len(self.segments)} {len(self.tokens)}")
-            self.context.append_token_ids(self.tokens[1][0,:])
-            self.tokens = [self.initial_tokens] + self.tokens[2:]
+            if len(self.tokens) > 1:
+                self.context.append_token_ids(self.tokens[1][0,:])
+                self.tokens = [self.initial_tokens] + self.tokens[2:]
         return removed_len
 
+    def _clean_cache(self):
+        '''clean the cache that stores the attention matrices and kv_cache.
+        It must be called every time after generation with the model.'''
+        # cleaning cache
+        self.dec_attns = []
+        self.kv_cache = {}
+        if self.decoder_type == "beam":
+            self.inference.kv_cache = self.kv_cache
+            self.token_decoder.reset()
+
+    @torch.no_grad()
+    def lang_id(self, encoder_features):
+        """Language detection from encoder features.
+        This code is trimmed and copy-pasted from whisper.decoding.detect_language .
+        """
+    
+        # forward pass using a single token, startoftranscript
+        n_audio = encoder_features.shape[0]
+        x = torch.tensor([[self.tokenizer.sot]] * n_audio).to(self.model.device)  # [n_audio, 1]
+        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
+        language_tokens = logits.argmax(dim=-1)
+        language_token_probs = logits.softmax(dim=-1).cpu()
+        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 i in range(n_audio)
+        ]
+
+        single = encoder_features.ndim == 2
+        if single:
+            language_tokens = language_tokens[0]
+            language_probs = language_probs[0]
+
+        self._clean_cache()
+        return language_tokens, language_probs
 
     ### transcription / translation
 
@@ -273,9 +333,12 @@ class PaddedAlignAttWhisper:
     def infer(self, is_last=False):
         new_segment = True
         if len(self.segments) == 0:
-            return []
+            logger.debug("No segments, nothing to do")
+            return [], {}
         if not self._apply_minseglen():
-            return []
+            logger.debug(f"applied minseglen {self.cfg.audio_min_len} > {self.segments_len()}.")
+            input_segments = torch.cat(self.segments, dim=0)
+            return [], {}
 
         # input_segments is concatenation of audio, it's one array
         if len(self.segments) > 1:
@@ -283,8 +346,7 @@ class PaddedAlignAttWhisper:
         else:
             input_segments = self.segments[0]
 
-        self.trim_context()
-        current_tokens = self._current_tokens()
+
         
         # mel + padding to 30s
         mel_padded = log_mel_spectrogram(input_segments, n_mels=self.model.dims.n_mels, padding=N_SAMPLES, 
@@ -295,18 +357,38 @@ class PaddedAlignAttWhisper:
         # the len of actual audio
         content_mel_len = int((mel_padded.shape[2] - mel.shape[2])/2)
 
+        # encode
         encoder_feature = self.model.encoder(mel)
-        sum_logprobs = torch.zeros(self.cfg.beam_size, device=mel.device)
-        completed = False
 
+#        logger.debug(f"Encoder feature shape: {encoder_feature.shape}")
+#        if mel.shape[-2:] != (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
+#            logger.debug("mel ")
+        if self.cfg.language == "auto" and self.detected_language is None:
+            language_tokens, language_probs = self.lang_id(encoder_feature) 
+            logger.debug(f"Language tokens: {language_tokens}, probs: {language_probs}")
+            top_lan, p = max(language_probs[0].items(), key=lambda x: x[1])
+            logger.info(f"Detected language: {top_lan} with p={p:.4f}")
+            #self.tokenizer.language = top_lan
+            #self.tokenizer.__post_init__()
+            self.create_tokenizer(top_lan)
+            self.detected_language = top_lan
+            self.init_tokens()
+            logger.info(f"Tokenizer language: {self.tokenizer.language}, {self.tokenizer.sot_sequence_including_notimestamps}")
+
+        self.trim_context()
+        current_tokens = self._current_tokens()
+#        
         fire_detected = self.fire_at_boundary(encoder_feature[:, :content_mel_len, :])
 
 
         ####################### Decoding loop
         logger.info("Decoding loop starts\n")
 
+        sum_logprobs = torch.zeros(self.cfg.beam_size, device=mel.device)
+        completed = False
+
         attn_of_alignment_heads = None
-        miost_attended_frame = None
+        most_attended_frame = None
 
         token_len_before_decoding = current_tokens.shape[1]
         
@@ -515,11 +597,6 @@ class PaddedAlignAttWhisper:
 
         logger.info(f"Output: {self.tokenizer.decode(new_hypothesis)}")
         
-        # cleaning cache
-        self.dec_attns = []
-        self.kv_cache = {}
-        if self.decoder_type == "beam":
-            self.inference.kv_cache = self.kv_cache
-            self.token_decoder.reset()
+        self._clean_cache()
 
-        return new_hypothesis, generation
+        return new_hypothesis, generation

whisperlivekit/simul_whisper/whisper/decoding.py

@@ -32,7 +32,9 @@ def detect_language(
         list of dictionaries containing the probability distribution over all languages.
     """
     if tokenizer is None:
-        tokenizer = get_tokenizer(model.is_multilingual)
+        tokenizer = get_tokenizer(
+            model.is_multilingual, num_languages=model.num_languages
+        )
     if (
         tokenizer.language is None
         or tokenizer.language_token not in tokenizer.sot_sequence
@@ -111,9 +113,6 @@ class DecodingOptions:
     # implementation details
     fp16: bool = True  # use fp16 for most of the calculation
 
-    # streaming
-    add_sot: Optional[bool] = True
-
 
 @dataclass(frozen=True)
 class DecodingResult:
@@ -513,19 +512,17 @@ class DecodingTask:
     logit_filters: List[LogitFilter]
 
     def __init__(self, model: "Whisper", options: DecodingOptions):
-        self.options: DecodingOptions = self._verify_options(options)
-        if self.options.fp16:
-            self.model = model.half()
-        else:
-            self.model = model
+        self.model = model
 
         language = options.language or "en"
         tokenizer = get_tokenizer(
-            model.is_multilingual, language=language, task=options.task
+            model.is_multilingual,
+            num_languages=model.num_languages,
+            language=language,
+            task=options.task,
         )
         self.tokenizer: Tokenizer = tokenizer
-       
-        # print(self.options)
+        self.options: DecodingOptions = self._verify_options(options)
 
         self.n_group: int = options.beam_size or options.best_of or 1
         self.n_ctx: int = model.dims.n_text_ctx
@@ -589,7 +586,7 @@ class DecodingTask:
 
     def _get_initial_tokens(self) -> Tuple[int]:
         tokens = list(self.sot_sequence)
-        # print("prefix", prefix)
+
         if prefix := self.options.prefix:
             prefix_tokens = (
                 self.tokenizer.encode(" " + prefix.strip())
@@ -607,15 +604,12 @@ class DecodingTask:
                 if isinstance(prompt, str)
                 else prompt
             )
-            # if self.options.add_sot:
             tokens = (
                 [self.tokenizer.sot_prev]
                 + prompt_tokens[-(self.n_ctx // 2 - 1) :]
                 + tokens
             )
-            #else:
-            #    tokens = ([self.tokenizer.sot_prev] + tokens + prompt_tokens[-(self.n_ctx // 2 - 1) :])
-        # print("return", tokens)
+
         return tuple(tokens)
 
     def _get_suppress_tokens(self) -> Tuple[int]:
@@ -663,7 +657,7 @@ class DecodingTask:
         if audio_features.dtype != (
             torch.float16 if self.options.fp16 else torch.float32
         ):
-            raise TypeError(
+            return TypeError(
                 f"audio_features has an incorrect dtype: {audio_features.dtype}"
             )
 
@@ -689,10 +683,9 @@ class DecodingTask:
         no_speech_probs = [np.nan] * n_batch
 
         try:
-            for i in range(self.sample_len): # 最多循环448次
-                # print("in decode main loop", i , tokens[0].tolist())
+            for i in range(self.sample_len):
                 logits = self.inference.logits(tokens, audio_features)
-                # print(logits)
+
                 if (
                     i == 0 and self.tokenizer.no_speech is not None
                 ):  # save no_speech_probs
@@ -724,7 +717,7 @@ class DecodingTask:
 
         audio_features: Tensor = self._get_audio_features(mel)  # encoder forward pass
         tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
-        # print("initial_tokens", self.initial_tokens)
+
         # detect language if requested, overwriting the language token
         languages, language_probs = self._detect_language(audio_features, tokens)
         if self.options.task == "lang_id":

whisperlivekit/simul_whisper/whisper/model.py

@@ -13,7 +13,6 @@ from .decoding import decode as decode_function
 from .decoding import detect_language as detect_language_function
 from .transcribe import transcribe as transcribe_function
 
-
 try:
     from torch.nn.functional import scaled_dot_product_attention
 
@@ -37,26 +36,27 @@ class ModelDimensions:
     n_text_layer: int
 
 
-# class LayerNorm(nn.LayerNorm):
-#     def forward(self, x: Tensor) -> Tensor:
-#         return super().forward(x.float()).type(x.dtype)
-
-# class Linear(nn.Linear):
-#     def forward(self, x: Tensor) -> Tensor:
-#         return F.linear(
-#             x,
-#             self.weight.to(x.dtype),
-#             None if self.bias is None else self.bias.to(x.dtype),
-#         )
+class LayerNorm(nn.LayerNorm):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(x.float()).type(x.dtype)
 
 
-# class Conv1d(nn.Conv1d):
-#     def _conv_forward(
-#         self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
-#     ) -> Tensor:
-#         return super()._conv_forward(
-#             x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
-#         )
+class Linear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        return F.linear(
+            x,
+            self.weight.to(x.dtype),
+            None if self.bias is None else self.bias.to(x.dtype),
+        )
+
+
+class Conv1d(nn.Conv1d):
+    def _conv_forward(
+        self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
+    ) -> Tensor:
+        return super()._conv_forward(
+            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
+        )
 
 
 def sinusoids(length, channels, max_timescale=10000):
@@ -67,21 +67,30 @@ def sinusoids(length, channels, max_timescale=10000):
     scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
     return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
 
-import sys  ## this is mine, for debugging
+
+@contextmanager
+def disable_sdpa():
+    prev_state = MultiHeadAttention.use_sdpa
+    try:
+        MultiHeadAttention.use_sdpa = False
+        yield
+    finally:
+        MultiHeadAttention.use_sdpa = prev_state
+
+
 class MultiHeadAttention(nn.Module):
+    use_sdpa = False  # Disable SDPA to ensure qk is always computed for hooks
 
-    use_sdpa = False  # disabling: https://github.com/linto-ai/whisper-timestamped/issues/212
-
-    def __init__(self, n_state: int, n_head: int, cache_id: str):
+    def __init__(self, n_state: int, n_head: int, cache_id: str = ""):
         super().__init__()
         self.n_head = n_head
-        self.query = nn.Linear(n_state, n_state)
-        self.key = nn.Linear(n_state, n_state, bias=False)
-        self.key.cache_id = f"{cache_id}_key"
-        self.value = nn.Linear(n_state, n_state)
-        self.value.cache_id = f"{cache_id}_value"
-        self.out = nn.Linear(n_state, n_state)
+        self.query = Linear(n_state, n_state)
+        self.key = Linear(n_state, n_state, bias=False)
+        self.value = Linear(n_state, n_state)
+        self.out = Linear(n_state, n_state)
         self.cache_id = cache_id
+        self.key.cache_id = f"{cache_id}_key"
+        self.value.cache_id = f"{cache_id}_value"
 
     def forward(
         self,
@@ -90,45 +99,21 @@ class MultiHeadAttention(nn.Module):
         mask: Optional[Tensor] = None,
         kv_cache: Optional[dict] = None,
     ):
-        #print("MultiHeadAttention forward",file=sys.stderr)
         q = self.query(x)
-#        print(q.shape, x is None, mask is None, list(kv_cache.keys()) if kv_cache is not None else None, file=sys.stderr)
-        # print(mask, kv_cache, xa, file=sys.stderr)
 
-        if kv_cache is None or xa is None or self.key.cache_id not in kv_cache:
+        if kv_cache is None or xa is None or self.key not in kv_cache:
+            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+            # otherwise, perform key/value projections for self- or cross-attention as usual.
             k = self.key(x if xa is None else xa)
             v = self.value(x if xa is None else xa)
-            # print(self.key.cache_id, "cache miss") # , kv_cache is None, xa is None, self.key.cache_id not in kv_cache if kv_cache is not None else None, k.shape, x.shape)
-            # if kv_cache is not None:
-            #     print(kv_cache.keys())
         else:
-            # print(self.key.cache_id, "cache hit") #, kv_cache is None, xa is None, self.key.cache_id not in kv_cache)
-            # if kv_cache is not None:
-            #     print(kv_cache.keys())
-            k = kv_cache[self.key.cache_id]
-            v = kv_cache[self.value.cache_id]
-        # print(self.key.cache_id, "qkv attention", q.shape, k.shape, v.shape)
+            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+            k = kv_cache[self.key]
+            v = kv_cache[self.value]
+
         wv, qk = self.qkv_attention(q, k, v, mask)
         return self.out(wv), qk
 
-    # def qkv_attention(
-    #     self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None
-    # ):
-    #     n_batch, n_ctx, n_state = q.shape
-    #     scale = (n_state // self.n_head) ** -0.25
-    #     q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
-    #     k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
-    #     v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
-
-    #     qk = q @ k
-    #     if mask is not None:
-    #         qk = qk + mask[:n_ctx, :n_ctx]
-    #     # qk = qk.float()
-
-    #     w = F.softmax(qk, dim=-1) # .to(q.dtype)
-    #     return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
-
-
     def qkv_attention(
         self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
@@ -158,21 +143,22 @@ class MultiHeadAttention(nn.Module):
 
 
 class ResidualAttentionBlock(nn.Module):
-    def __init__(self, n_state: int, n_head: int, cache_id: str="", cross_attention: bool = False):
+    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False, cache_id: str = ""):
         super().__init__()
 
         self.attn = MultiHeadAttention(n_state, n_head, cache_id=f"{cache_id}_self_attn")
-        self.attn_ln = nn.LayerNorm(n_state)
+        self.attn_ln = LayerNorm(n_state)
 
-        self.cross_attn = MultiHeadAttention(n_state, n_head, cache_id=f"{cache_id}_cross_attn") if cross_attention else None
-
-        self.cross_attn_ln = nn.LayerNorm(n_state) if cross_attention else None
+        self.cross_attn = (
+            MultiHeadAttention(n_state, n_head, cache_id=f"{cache_id}_cross_attn") if cross_attention else None
+        )
+        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
 
         n_mlp = n_state * 4
         self.mlp = nn.Sequential(
-            nn.Linear(n_state, n_mlp), nn.GELU(), nn.Linear(n_mlp, n_state)
+            Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
         )
-        self.mlp_ln = nn.LayerNorm(n_state)
+        self.mlp_ln = LayerNorm(n_state)
 
     def forward(
         self,
@@ -181,8 +167,6 @@ class ResidualAttentionBlock(nn.Module):
         mask: Optional[Tensor] = None,
         kv_cache: Optional[dict] = None,
     ):
-        # print("ResidualAttentionBlock forward",file=sys.stderr)
-        # print(x.shape, file=sys.stderr)
         x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
         if self.cross_attn:
             x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
@@ -195,44 +179,32 @@ class AudioEncoder(nn.Module):
         self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
     ):
         super().__init__()
-        self.conv1 = nn.Conv1d(n_mels, n_state, kernel_size=3, padding=1)
-        self.conv2 = nn.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
         self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
 
         self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
             [ResidualAttentionBlock(n_state, n_head, cache_id=f"enc_layer{i}") for i in range(n_layer)]
         )
-        self.ln_post = nn.LayerNorm(n_state)
+        self.ln_post = LayerNorm(n_state)
 
-    def forward(self, x: Tensor, return_layer_results: bool=False):
+    def forward(self, x: Tensor):
         """
         x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
             the mel spectrogram of the audio
         """
-
         x = F.gelu(self.conv1(x))
         x = F.gelu(self.conv2(x))
-        x = x.permute(0, 2, 1) # BDT -> BTD
+        x = x.permute(0, 2, 1)
 
-        # 两层卷积，2倍降采样
-        # 最终剩下1500帧
+        assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
+        x = (x + self.positional_embedding).to(x.dtype)
 
-        x = (x + self.positional_embedding[:x.shape[1], :]) #.to(x.dtype)
-
-        layer_results = []
-        i = 0
         for block in self.blocks:
-            # print(f"encoder layer {i}")
             x = block(x)
-            layer_results.append(x)
-            i += 1
 
         x = self.ln_post(x)
-
-        if return_layer_results:
-            return x, layer_results
-        else:
-            return x
+        return x
 
 
 class TextDecoder(nn.Module):
@@ -250,7 +222,7 @@ class TextDecoder(nn.Module):
                 for i in range(n_layer)
             ]
         )
-        self.ln = nn.LayerNorm(n_state)
+        self.ln = LayerNorm(n_state)
 
         mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
         self.register_buffer("mask", mask, persistent=False)
@@ -262,22 +234,20 @@ class TextDecoder(nn.Module):
         xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state)
             the encoded audio features to be attended on
         """
-
         offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
         x = (
             self.token_embedding(x)
             + self.positional_embedding[offset : offset + x.shape[-1]]
         )
-        # x = x.to(xa.dtype)
+        x = x.to(xa.dtype)
 
-        i = 0
         for block in self.blocks:
-            # print(f"decoder layer {i}")
             x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
-            i += 1
 
         x = self.ln(x)
-        logits = x @ torch.transpose(self.token_embedding.weight, 0, 1)
+        logits = (
+            x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
+        ).float()
 
         return logits
 
@@ -300,7 +270,8 @@ class Whisper(nn.Module):
             self.dims.n_text_head,
             self.dims.n_text_layer,
         )
-        # use the last half layers for alignment by default; see `set_alignment_heads()` below
+        # use the last half among the decoder layers for time alignment by default;
+        # to use a specific set of heads, see `set_alignment_heads()` below.
         all_heads = torch.zeros(
             self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool
         )
@@ -320,15 +291,11 @@ class Whisper(nn.Module):
         return self.encoder(mel)
 
     def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
-        # tokens = tokens.to(self.decoder.ln.weight.dtype)
-        # audio_features = audio_features.to(self.decoder.ln.weight.dtype)
         return self.decoder(tokens, audio_features)
 
     def forward(
         self, mel: torch.Tensor, tokens: torch.Tensor
     ) -> Dict[str, torch.Tensor]:
-        # mel = mel.to(self.decoder.ln.weight.dtype)
-        # tokens = tokens.to(self.decoder.ln.weight.dtype)
         return self.decoder(tokens, self.encoder(mel))
 
     @property
@@ -343,7 +310,6 @@ class Whisper(nn.Module):
     def num_languages(self):
         return self.dims.n_vocab - 51765 - int(self.is_multilingual)
 
-    # 为decoder加入缓存机制，每次推理时保存上次的k和v，下次推理无需重新计算
     def install_kv_cache_hooks(self, cache: Optional[dict] = None):
         """
         The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value

whisperlivekit/simul_whisper/whisper/normalizers/basic.py

@@ -30,15 +30,19 @@ def remove_symbols_and_diacritics(s: str, keep=""):
     and drop any diacritics (category 'Mn' and some manual mappings)
     """
     return "".join(
-        c
-        if c in keep
-        else ADDITIONAL_DIACRITICS[c]
-        if c in ADDITIONAL_DIACRITICS
-        else ""
-        if unicodedata.category(c) == "Mn"
-        else " "
-        if unicodedata.category(c)[0] in "MSP"
-        else c
+        (
+            c
+            if c in keep
+            else (
+                ADDITIONAL_DIACRITICS[c]
+                if c in ADDITIONAL_DIACRITICS
+                else (
+                    ""
+                    if unicodedata.category(c) == "Mn"
+                    else " " if unicodedata.category(c)[0] in "MSP" else c
+                )
+            )
+        )
         for c in unicodedata.normalize("NFKD", s)
     )
 

whisperlivekit/simul_whisper/whisper/timing.py

@@ -56,9 +56,8 @@ def median_filter(x: torch.Tensor, filter_width: int):
 
 @numba.jit(nopython=True)
 def backtrace(trace: np.ndarray):
-    i = trace.shape[0] - 1 # trace: (N+1, M+1), i=N
-    j = trace.shape[1] - 1 # j=M
-    # 边界点其实无意义？
+    i = trace.shape[0] - 1
+    j = trace.shape[1] - 1
     trace[0, :] = 2
     trace[:, 0] = 1
 
@@ -83,8 +82,8 @@ def backtrace(trace: np.ndarray):
 @numba.jit(nopython=True, parallel=True)
 def dtw_cpu(x: np.ndarray):
     N, M = x.shape
-    cost = np.ones((N + 1, M + 1), dtype=np.float32) * np.inf # cost: x[0, 0]到x[i-1, j-1]的最小代价
-    trace = -np.ones((N + 1, M + 1), dtype=np.float32) # trace: 
+    cost = np.ones((N + 1, M + 1), dtype=np.float32) * np.inf
+    trace = -np.ones((N + 1, M + 1), dtype=np.float32)
 
     cost[0, 0] = 0
     for j in range(1, M + 1):
@@ -118,7 +117,7 @@ def dtw_cuda(x, BLOCK_SIZE=1024):
     x_skew = x_skew.T.contiguous()
     cost = torch.ones(N + M + 2, M + 2) * np.inf
     cost[0, 0] = 0
-    cost = cost.cuda()
+    cost = cost.to(x.device)
     trace = torch.zeros_like(cost, dtype=torch.int32)
 
     dtw_kernel[(1,)](
@@ -192,21 +191,19 @@ def find_alignment(
         for i, block in enumerate(model.decoder.blocks)
     ]
 
-    # 进行前传，获得token概率
-    with torch.no_grad():
+    from .model import disable_sdpa
+
+    with torch.no_grad(), disable_sdpa():
         logits = model(mel.unsqueeze(0), tokens.unsqueeze(0))[0]
         sampled_logits = logits[len(tokenizer.sot_sequence) :, : tokenizer.eot]
         token_probs = sampled_logits.softmax(dim=-1)
         text_token_probs = token_probs[np.arange(len(text_tokens)), text_tokens]
         text_token_probs = text_token_probs.tolist()
 
-    # 移除钩子
     for hook in hooks:
         hook.remove()
 
     # heads * tokens * frames
-    # print(model.alignment_heads)
-    # exit(0)
     weights = torch.stack([QKs[_l][_h] for _l, _h in model.alignment_heads.indices().T])
     weights = weights[:, :, : num_frames // 2]
     weights = (weights * qk_scale).softmax(dim=-1)
@@ -215,18 +212,9 @@ def find_alignment(
     weights = median_filter(weights, medfilt_width)
 
     matrix = weights.mean(axis=0)
-    print("attention", matrix.shape, matrix[:5, :5])
     matrix = matrix[len(tokenizer.sot_sequence) : -1]
-    print("attention", matrix.shape, matrix[:5, :5])
     text_indices, time_indices = dtw(-matrix)
 
-    print("num_frames", num_frames)
-    print("attention", matrix.shape, matrix[:5, :5])
-    print("text_indices", text_indices)
-    print("time", time_indices)
-    print("text_tokens", text_tokens, tokenizer.decode(text_tokens), len(text_tokens))
-    print("eot", tokenizer.eot)
-
     words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot])
     if len(word_tokens) <= 1:
         # return on eot only
@@ -238,9 +226,7 @@ def find_alignment(
     word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0))
 
     jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool)
-    # print("jumps", jumps, jumps.shape)
     jump_times = time_indices[jumps] / TOKENS_PER_SECOND
-    # print("jump_times", jump_times)
     start_times = jump_times[word_boundaries[:-1]]
     end_times = jump_times[word_boundaries[1:]]
     word_probabilities = [
@@ -315,6 +301,7 @@ def add_word_timestamps(
     word_durations = np.array([t.end - t.start for t in alignment])
     word_durations = word_durations[word_durations.nonzero()]
     median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0
+    median_duration = min(0.7, float(median_duration))
     max_duration = median_duration * 2
 
     # hack: truncate long words at sentence boundaries.

whisperlivekit/simul_whisper/whisper/trans_nopad.py

@@ -1,501 +0,0 @@
-import argparse
-import os
-import warnings
-from typing import TYPE_CHECKING, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import tqdm
-
-from whisper.audio import (
-    FRAMES_PER_SECOND,
-    HOP_LENGTH,
-    N_FRAMES,
-    N_SAMPLES,
-    SAMPLE_RATE,
-    log_mel_spectrogram,
-    pad_or_trim,
-)
-from whisper.decoding import DecodingOptions, DecodingResult
-from whisper.timing import add_word_timestamps
-from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
-from whisper.utils import (
-    exact_div,
-    format_timestamp,
-    get_writer,
-    make_safe,
-    optional_float,
-    optional_int,
-    str2bool,
-)
-
-if TYPE_CHECKING:
-    from whisper.model import Whisper
-
-
-def transcribe(
-    model: "Whisper",
-    audio: Union[str, np.ndarray, torch.Tensor],
-    *,
-    verbose: Optional[bool] = None,
-    temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
-    compression_ratio_threshold: Optional[float] = 2.4,
-    logprob_threshold: Optional[float] = -1.0,
-    no_speech_threshold: Optional[float] = 0.6,
-    condition_on_previous_text: bool = True,
-    initial_prompt: Optional[str] = None,
-    word_timestamps: bool = False,
-    prepend_punctuations: str = "\"'“¿([{-",
-    append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
-    **decode_options,
-):
-    """
-    Transcribe an audio file using Whisper
-
-    Parameters
-    ----------
-    model: Whisper
-        The Whisper model instance
-
-    audio: Union[str, np.ndarray, torch.Tensor]
-        The path to the audio file to open, or the audio waveform
-
-    verbose: bool
-        Whether to display the text being decoded to the console. If True, displays all the details,
-        If False, displays minimal details. If None, does not display anything
-
-    temperature: Union[float, Tuple[float, ...]]
-        Temperature for sampling. It can be a tuple of temperatures, which will be successively used
-        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
-
-    compression_ratio_threshold: float
-        If the gzip compression ratio is above this value, treat as failed
-
-    logprob_threshold: float
-        If the average log probability over sampled tokens is below this value, treat as failed
-
-    no_speech_threshold: float
-        If the no_speech probability is higher than this value AND the average log probability
-        over sampled tokens is below `logprob_threshold`, consider the segment as silent
-
-    condition_on_previous_text: bool
-        if True, the previous output of the model is provided as a prompt for the next window;
-        disabling may make the text inconsistent across windows, but the model becomes less prone to
-        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
-
-    word_timestamps: bool
-        Extract word-level timestamps using the cross-attention pattern and dynamic time warping,
-        and include the timestamps for each word in each segment.
-
-    prepend_punctuations: str
-        If word_timestamps is True, merge these punctuation symbols with the next word
-
-    append_punctuations: str
-        If word_timestamps is True, merge these punctuation symbols with the previous word
-
-    initial_prompt: Optional[str]
-        Optional text to provide as a prompt for the first window. This can be used to provide, or
-        "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
-        to make it more likely to predict those word correctly.
-
-    decode_options: dict
-        Keyword arguments to construct `DecodingOptions` instances
-
-    Returns
-    -------
-    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
-    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
-    """
-    # print("HACKED")
-    dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
-    if model.device == torch.device("cpu"):
-        if torch.cuda.is_available():
-            warnings.warn("Performing inference on CPU when CUDA is available")
-        if dtype == torch.float16:
-            warnings.warn("FP16 is not supported on CPU; using FP32 instead")
-            dtype = torch.float32
-
-    if dtype == torch.float32:
-        decode_options["fp16"] = False
-
-    # Pad 30-seconds of silence to the input audio, for slicing
-    mel = log_mel_spectrogram(audio, padding=0) # log_mel_spectrogram(audio, padding=N_SAMPLES) # 添加16000*30 = 480000个点
-    # mel = pad_or_trim(mel, 3000)
-    content_frames = mel.shape[-1] # - N_FRAMES # 对应3000帧；真正有内容的是去掉尾部3000的那些数据
-
-    # 判断语种
-    if decode_options.get("language", None) is None:
-        # 如果是单语种模型，直接设成英文
-        if not model.is_multilingual:
-            decode_options["language"] = "en"
-        # 否则需要前传一次
-        else:
-            if verbose:
-                print(
-                    "Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
-                )
-            mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
-            # print(mel_segment.shape)
-            _, probs = model.detect_language(mel_segment)
-            decode_options["language"] = max(probs, key=probs.get)
-            if verbose is not None:
-                print(
-                    f"Detected language: {LANGUAGES[decode_options['language']].title()}"
-                )
-
-    language: str = decode_options["language"]
-    task: str = decode_options.get("task", "transcribe")
-    # 输出编码器
-    tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task)
-
-    # 词级别时间戳
-    if word_timestamps and task == "translate":
-        warnings.warn("Word-level timestamps on translations may not be reliable.")
-
-    def decode_with_fallback(segment: torch.Tensor) -> DecodingResult:
-        temperatures = (
-            [temperature] if isinstance(temperature, (int, float)) else temperature
-        )
-        decode_result = None
-
-        for t in temperatures:
-            kwargs = {**decode_options}
-            if t > 0:
-                # disable beam_size and patience when t > 0
-                kwargs.pop("beam_size", None)
-                kwargs.pop("patience", None)
-            else:
-                # disable best_of when t == 0
-                kwargs.pop("best_of", None)
-
-            options = DecodingOptions(**kwargs, temperature=t)
-            decode_result = model.decode(segment, options)
-
-            # 几种解码可能失败的情况。这些情况下会重复解码
-            # 感觉是一种KnowHow的东西 或许ChatGPT里有不少这种trick
-            needs_fallback = False
-            if (
-                compression_ratio_threshold is not None
-                and decode_result.compression_ratio > compression_ratio_threshold
-            ):
-                needs_fallback = True  # too repetitive
-            if (
-                logprob_threshold is not None
-                and decode_result.avg_logprob < logprob_threshold
-            ):
-                needs_fallback = True  # average log probability is too low
-            if (
-                no_speech_threshold is not None
-                and decode_result.no_speech_prob > no_speech_threshold
-            ):
-                needs_fallback = False  # silence
-            if not needs_fallback:
-                break
-            # print("decode with temperature {} compress rate {:.3f}/{:.3f}, log_prob {:.3f}/{:.3f}, {:.3f}/{:.3f}".format(
-            #     t, 
-            #     decode_result.compression_ratio, compression_ratio_threshold,
-            #     -decode_result.avg_logprob, -logprob_threshold,
-            #     decode_result.no_speech_prob, no_speech_threshold
-            # ))
-
-        return decode_result
-
-    seek = 0
-    input_stride = exact_div(
-        N_FRAMES, model.dims.n_audio_ctx
-    )  # mel frames per output token: 2
-    # 这里output token指的应该是CNN输出的那个东西
-
-    time_precision = (
-        input_stride * HOP_LENGTH / SAMPLE_RATE
-    )  # time per output token: 0.02 (seconds)
-    all_tokens = []
-    all_segments = []
-    prompt_reset_since = 0
-
-    if initial_prompt is not None:
-        initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
-        all_tokens.extend(initial_prompt_tokens)
-    else:
-        initial_prompt_tokens = []
-
-    def new_segment(
-        *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult
-    ):
-        tokens = tokens.tolist()
-        text_tokens = [token for token in tokens if token < tokenizer.eot]
-        return {
-            "seek": seek,
-            "start": start,
-            "end": end,
-            "text": tokenizer.decode(text_tokens),
-            "tokens": tokens,
-            "temperature": result.temperature,
-            "avg_logprob": result.avg_logprob,
-            "compression_ratio": result.compression_ratio,
-            "no_speech_prob": result.no_speech_prob,
-        }
-
-    # show the progress bar when verbose is False (if True, transcribed text will be printed)
-    with tqdm.tqdm(
-        total=content_frames, unit="frames", disable=verbose is not False
-    ) as pbar:
-        last_speech_timestamp = 0.0
-        while seek < content_frames: # seek：标记mel频谱当前帧的位置 直接跳过Padding上的部分
-            # print("seek segments", seek, content_frames)
-            time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) # 本片段的开始时间
-            # mel_segment = mel[:, seek : seek + N_FRAMES] # 获得当前片段的数据
-            mel_segment = mel[:, seek:]
-            segment_size = min(N_FRAMES, content_frames - seek) # segment_size: 排除padding的真的长度。content_frames：有内容的段的真正长度 如果不够N_FRAMES的话就会截断
-            segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE # 当前片段的时长
-            mel_segment = mel_segment.to(model.device).to(dtype) # pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype) # 补到mel_segment帧
-
-            decode_options["prompt"] = all_tokens[prompt_reset_since:]
-            result: DecodingResult = decode_with_fallback(mel_segment)
-            tokens = torch.tensor(result.tokens)
-
-            # 跳过静音部分
-            if no_speech_threshold is not None:
-                # no voice activity check
-                should_skip = result.no_speech_prob > no_speech_threshold
-                if (
-                    logprob_threshold is not None
-                    and result.avg_logprob > logprob_threshold
-                ):
-                    # don't skip if the logprob is high enough, despite the no_speech_prob
-                    should_skip = False
-
-                if should_skip:
-                    seek += segment_size  # fast-forward to the next segment boundary
-                    continue
-
-            previous_seek = seek
-            current_segments = []
-
-            timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) # timestamp begin是<|0.00|>的token；bos比文字token大，eos的值比bos还大，所以是ge
-            timestamp_tokens[-1] = False
-            single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True] # 如果最后是[False,True]：本段里一个句子结束了
-
-            consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] 
-            # torch.where(condition) is identical to torch.nonzero(condition, as_tuple=True).
-            # timestamp_token就是个一维向量吧 那为啥不直接nonzero
-            # 如果有两个连续的时间戳 这个会是一个一维tensor 是这两个连续时间戳的结尾位置
-            # 多个的话指向第二个 那如果有三个怎么办？
-            # 否则是个0维tensor
-
-            consecutive.add_(1) # 0维tensor+1还是0维 哪儿找的这些edge cases js是吧
-            if len(consecutive) > 0:
-                # if the output contains two consecutive timestamp tokens
-                slices = consecutive.tolist()
-                if single_timestamp_ending:
-                    slices.append(len(tokens)) # 把最后一段的结尾也加进去
-                # print("many sentenses", consecutive)
-                last_slice = 0
-                for current_slice in slices:
-                    sliced_tokens = tokens[last_slice:current_slice]
-                    # 看起来语音开始帧、语音结束帧的位置会被编码到start_timestamp中
-                    start_timestamp_pos = (
-                        sliced_tokens[0].item() - tokenizer.timestamp_begin
-                    )
-                    end_timestamp_pos = (
-                        sliced_tokens[-1].item() - tokenizer.timestamp_begin
-                    )
-                    # 获取一个新的语音段
-                    current_segments.append(
-                        new_segment(
-                            start=time_offset + start_timestamp_pos * time_precision,
-                            end=time_offset + end_timestamp_pos * time_precision,
-                            tokens=sliced_tokens,
-                            result=result,
-                        )
-                    )
-                    last_slice = current_slice
-
-                if single_timestamp_ending:
-                    # single timestamp at the end means no speech after the last timestamp.
-                    seek += segment_size
-                else:
-                    # otherwise, ignore the unfinished segment and seek to the last timestamp
-                    # 如果语音尚未结束，那么seek变为上一个结束的语段的位置
-                    # 换句话说就是针对30s长的chunk的语音设计的
-                    last_timestamp_pos = (
-                        tokens[last_slice - 1].item() - tokenizer.timestamp_begin
-                    )
-                    seek += last_timestamp_pos * input_stride
-            else:
-                duration = segment_duration
-                timestamps = tokens[timestamp_tokens.nonzero().flatten()]
-                # print(timestamps)
-                if (
-                    len(timestamps) > 0
-                    and timestamps[-1].item() != tokenizer.timestamp_begin
-                ):
-                    # no consecutive timestamps but it has a timestamp; use the last one.
-                    # 取最后一个；假设要么有一个结束的time stamp；要么有一对儿？
-                    # 如果里面只有一个开始的timestamp 似乎后面的东西都会被丢掉？
-                    last_timestamp_pos = (
-                        timestamps[-1].item() - tokenizer.timestamp_begin
-                    )
-                    duration = last_timestamp_pos * time_precision
-
-                current_segments.append(
-                    new_segment(
-                        start=time_offset,
-                        end=time_offset + duration,
-                        tokens=tokens,
-                        result=result,
-                    )
-                )
-                seek += segment_size
-
-            # 每个token有自己的时间戳
-            if word_timestamps:
-                add_word_timestamps(
-                    segments=current_segments,
-                    model=model,
-                    tokenizer=tokenizer,
-                    mel=mel_segment,
-                    num_frames=segment_size,
-                    prepend_punctuations=prepend_punctuations,
-                    append_punctuations=append_punctuations,
-                    last_speech_timestamp=last_speech_timestamp,
-                )
-                word_end_timestamps = [
-                    w["end"] for s in current_segments for w in s["words"]
-                ]
-                if len(word_end_timestamps) > 0:
-                    last_speech_timestamp = word_end_timestamps[-1]
-                if not single_timestamp_ending and len(word_end_timestamps) > 0:
-                    seek_shift = round(
-                        (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND
-                    )
-                    if seek_shift > 0:
-                        seek = previous_seek + seek_shift
-
-            if verbose:
-                for segment in current_segments:
-                    start, end, text = segment["start"], segment["end"], segment["text"]
-                    line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}"
-                    print(make_safe(line))
-
-            # if a segment is instantaneous or does not contain text, clear it
-            for i, segment in enumerate(current_segments):
-                if segment["start"] == segment["end"] or segment["text"].strip() == "":
-                    segment["text"] = ""
-                    segment["tokens"] = []
-                    segment["words"] = []
-
-            # 更新结果
-            all_segments.extend(
-                [
-                    {"id": i, **segment}
-                    for i, segment in enumerate(
-                        current_segments, start=len(all_segments)
-                    )
-                ]
-            )
-            all_tokens.extend(
-                [token for segment in current_segments for token in segment["tokens"]]
-            )
-
-            if not condition_on_previous_text or result.temperature > 0.5:
-                # do not feed the prompt tokens if a high temperature was used
-                prompt_reset_since = len(all_tokens)
-
-            # update progress bar
-            pbar.update(min(content_frames, seek) - previous_seek)
-
-            # print("太长了")
-            # break
-
-    return dict(
-        text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]),
-        segments=all_segments,
-        language=language,
-    )
-
-
-def cli():
-    from . import available_models
-
-    # fmt: off
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
-    parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
-    parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
-    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
-    parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
-    parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], help="format of the output file; if not specified, all available formats will be produced")
-    parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
-
-    parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
-    parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
-
-    parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
-    parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
-    parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
-    parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
-    parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default")
-
-    parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
-    parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
-    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
-    parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
-
-    parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
-    parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
-    parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
-    parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
-    parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them")
-    parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word")
-    parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word")
-    parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
-    parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line")
-    parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment")
-    parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
-    # fmt: on
-
-    args = parser.parse_args().__dict__
-    model_name: str = args.pop("model")
-    model_dir: str = args.pop("model_dir")
-    output_dir: str = args.pop("output_dir")
-    output_format: str = args.pop("output_format")
-    device: str = args.pop("device")
-    os.makedirs(output_dir, exist_ok=True)
-
-    if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
-        if args["language"] is not None:
-            warnings.warn(
-                f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead."
-            )
-        args["language"] = "en"
-
-    temperature = args.pop("temperature")
-    if (increment := args.pop("temperature_increment_on_fallback")) is not None:
-        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment))
-    else:
-        temperature = [temperature]
-
-    if (threads := args.pop("threads")) > 0:
-        torch.set_num_threads(threads)
-
-    from . import load_model
-
-    model = load_model(model_name, device=device, download_root=model_dir)
-
-    writer = get_writer(output_format, output_dir)
-    word_options = ["highlight_words", "max_line_count", "max_line_width"]
-    if not args["word_timestamps"]:
-        for option in word_options:
-            if args[option]:
-                parser.error(f"--{option} requires --word_timestamps True")
-    if args["max_line_count"] and not args["max_line_width"]:
-        warnings.warn("--max_line_count has no effect without --max_line_width")
-    writer_args = {arg: args.pop(arg) for arg in word_options}
-    for audio_path in args.pop("audio"):
-        result = transcribe(model, audio_path, temperature=temperature, **args)
-        writer(result, audio_path, writer_args)
-
-
-if __name__ == "__main__":
-    cli()

whisperlivekit/simul_whisper/whisper/transcribe.py

@@ -1,7 +1,8 @@
 import argparse
 import os
+import traceback
 import warnings
-from typing import TYPE_CHECKING, Optional, Tuple, Union
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -22,6 +23,7 @@ from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
 from .utils import (
     exact_div,
     format_timestamp,
+    get_end,
     get_writer,
     make_safe,
     optional_float,
@@ -44,9 +46,12 @@ def transcribe(
     no_speech_threshold: Optional[float] = 0.6,
     condition_on_previous_text: bool = True,
     initial_prompt: Optional[str] = None,
+    carry_initial_prompt: bool = False,
     word_timestamps: bool = False,
     prepend_punctuations: str = "\"'“¿([{-",
     append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
+    clip_timestamps: Union[str, List[float]] = "0",
+    hallucination_silence_threshold: Optional[float] = None,
     **decode_options,
 ):
     """
@@ -98,15 +103,27 @@ def transcribe(
         "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
         to make it more likely to predict those word correctly.
 
+    carry_initial_prompt: bool
+        If carry_initial_prompt is True, `initial_prompt` is prepended to the prompt of each internal
+        `decode()` call. If there is not enough context space at the start of the prompt, it is
+        left-sliced to make space.
+
     decode_options: dict
         Keyword arguments to construct `DecodingOptions` instances
 
+    clip_timestamps: Union[str, List[float]]
+        Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process.
+        The last end timestamp defaults to the end of the file.
+
+    hallucination_silence_threshold: Optional[float]
+        When word_timestamps is True, skip silent periods longer than this threshold (in seconds)
+        when a possible hallucination is detected
+
     Returns
     -------
     A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
     the spoken language ("language"), which is detected when `decode_options["language"]` is None.
     """
-    # print("transcribe")
     dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
     if model.device == torch.device("cpu"):
         if torch.cuda.is_available():
@@ -119,8 +136,9 @@ def transcribe(
         decode_options["fp16"] = False
 
     # Pad 30-seconds of silence to the input audio, for slicing
-    mel = log_mel_spectrogram(audio, padding=N_SAMPLES)
+    mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)
     content_frames = mel.shape[-1] - N_FRAMES
+    content_duration = float(content_frames * HOP_LENGTH / SAMPLE_RATE)
 
     if decode_options.get("language", None) is None:
         if not model.is_multilingual:
@@ -131,7 +149,6 @@ def transcribe(
                     "Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
                 )
             mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
-            # print(mel_segment.shape)
             _, probs = model.detect_language(mel_segment)
             decode_options["language"] = max(probs, key=probs.get)
             if verbose is not None:
@@ -141,7 +158,25 @@ def transcribe(
 
     language: str = decode_options["language"]
     task: str = decode_options.get("task", "transcribe")
-    tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task)
+    tokenizer = get_tokenizer(
+        model.is_multilingual,
+        num_languages=model.num_languages,
+        language=language,
+        task=task,
+    )
+
+    if isinstance(clip_timestamps, str):
+        clip_timestamps = [
+            float(ts) for ts in (clip_timestamps.split(",") if clip_timestamps else [])
+        ]
+    seek_points: List[int] = [round(ts * FRAMES_PER_SECOND) for ts in clip_timestamps]
+    if len(seek_points) == 0:
+        seek_points.append(0)
+    if len(seek_points) % 2 == 1:
+        seek_points.append(content_frames)
+    seek_clips: List[Tuple[int, int]] = list(zip(seek_points[::2], seek_points[1::2]))
+
+    punctuation = "\"'“¿([{-\"'.。,，!！?？:：”)]}、"
 
     if word_timestamps and task == "translate":
         warnings.warn("Word-level timestamps on translations may not be reliable.")
@@ -179,6 +214,8 @@ def transcribe(
             if (
                 no_speech_threshold is not None
                 and decode_result.no_speech_prob > no_speech_threshold
+                and logprob_threshold is not None
+                and decode_result.avg_logprob < logprob_threshold
             ):
                 needs_fallback = False  # silence
             if not needs_fallback:
@@ -186,7 +223,8 @@ def transcribe(
 
         return decode_result
 
-    seek = 0
+    clip_idx = 0
+    seek = seek_clips[clip_idx][0]
     input_stride = exact_div(
         N_FRAMES, model.dims.n_audio_ctx
     )  # mel frames per output token: 2
@@ -197,9 +235,11 @@ def transcribe(
     all_segments = []
     prompt_reset_since = 0
 
+    remaining_prompt_length = model.dims.n_text_ctx // 2 - 1
     if initial_prompt is not None:
         initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
         all_tokens.extend(initial_prompt_tokens)
+        remaining_prompt_length -= len(initial_prompt_tokens)
     else:
         initial_prompt_tokens = []
 
@@ -225,16 +265,33 @@ def transcribe(
         total=content_frames, unit="frames", disable=verbose is not False
     ) as pbar:
         last_speech_timestamp = 0.0
-        while seek < content_frames:
+        # NOTE: This loop is obscurely flattened to make the diff readable.
+        # A later commit should turn this into a simpler nested loop.
+        # for seek_clip_start, seek_clip_end in seek_clips:
+        #     while seek < seek_clip_end
+        while clip_idx < len(seek_clips):
+            seek_clip_start, seek_clip_end = seek_clips[clip_idx]
+            if seek < seek_clip_start:
+                seek = seek_clip_start
+            if seek >= seek_clip_end:
+                clip_idx += 1
+                if clip_idx < len(seek_clips):
+                    seek = seek_clips[clip_idx][0]
+                continue
             time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
-            mel_segment = mel[:, seek : seek + N_FRAMES]
-            segment_size = min(N_FRAMES, content_frames - seek)
+            window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE)
+            segment_size = min(N_FRAMES, content_frames - seek, seek_clip_end - seek)
+            mel_segment = mel[:, seek : seek + segment_size]
             segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
             mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
 
-            # print("melshape", mel_segment.shape)
+            if carry_initial_prompt:
+                nignored = max(len(initial_prompt_tokens), prompt_reset_since)
+                remaining_prompt = all_tokens[nignored:][-remaining_prompt_length:]
+                decode_options["prompt"] = initial_prompt_tokens + remaining_prompt
+            else:
+                decode_options["prompt"] = all_tokens[prompt_reset_since:]
 
-            decode_options["prompt"] = all_tokens[prompt_reset_since:]
             result: DecodingResult = decode_with_fallback(mel_segment)
             tokens = torch.tensor(result.tokens)
 
@@ -255,6 +312,30 @@ def transcribe(
             previous_seek = seek
             current_segments = []
 
+            # anomalous words are very long/short/improbable
+            def word_anomaly_score(word: dict) -> float:
+                probability = word.get("probability", 0.0)
+                duration = word["end"] - word["start"]
+                score = 0.0
+                if probability < 0.15:
+                    score += 1.0
+                if duration < 0.133:
+                    score += (0.133 - duration) * 15
+                if duration > 2.0:
+                    score += duration - 2.0
+                return score
+
+            def is_segment_anomaly(segment: Optional[dict]) -> bool:
+                if segment is None or not segment["words"]:
+                    return False
+                words = [w for w in segment["words"] if w["word"] not in punctuation]
+                words = words[:8]
+                score = sum(word_anomaly_score(w) for w in words)
+                return score >= 3 or score + 0.01 >= len(words)
+
+            def next_words_segment(segments: List[dict]) -> Optional[dict]:
+                return next((s for s in segments if s["words"]), None)
+
             timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
             single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]
 
@@ -317,9 +398,7 @@ def transcribe(
                 )
                 seek += segment_size
 
-            # print("word_timestamps, ", word_timestamps)
             if word_timestamps:
-                # print("=========run timestamps here=========")
                 add_word_timestamps(
                     segments=current_segments,
                     model=model,
@@ -330,17 +409,71 @@ def transcribe(
                     append_punctuations=append_punctuations,
                     last_speech_timestamp=last_speech_timestamp,
                 )
-                word_end_timestamps = [
-                    w["end"] for s in current_segments for w in s["words"]
-                ]
-                if len(word_end_timestamps) > 0:
-                    last_speech_timestamp = word_end_timestamps[-1]
-                if not single_timestamp_ending and len(word_end_timestamps) > 0:
-                    seek_shift = round(
-                        (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND
-                    )
-                    if seek_shift > 0:
-                        seek = previous_seek + seek_shift
+
+                if not single_timestamp_ending:
+                    last_word_end = get_end(current_segments)
+                    if last_word_end is not None and last_word_end > time_offset:
+                        seek = round(last_word_end * FRAMES_PER_SECOND)
+
+                # skip silence before possible hallucinations
+                if hallucination_silence_threshold is not None:
+                    threshold = hallucination_silence_threshold
+                    if not single_timestamp_ending:
+                        last_word_end = get_end(current_segments)
+                        if last_word_end is not None and last_word_end > time_offset:
+                            remaining_duration = window_end_time - last_word_end
+                            if remaining_duration > threshold:
+                                seek = round(last_word_end * FRAMES_PER_SECOND)
+                            else:
+                                seek = previous_seek + segment_size
+
+                    # if first segment might be a hallucination, skip leading silence
+                    first_segment = next_words_segment(current_segments)
+                    if first_segment is not None and is_segment_anomaly(first_segment):
+                        gap = first_segment["start"] - time_offset
+                        if gap > threshold:
+                            seek = previous_seek + round(gap * FRAMES_PER_SECOND)
+                            continue
+
+                    # skip silence before any possible hallucination that is surrounded
+                    # by silence or more hallucinations
+                    hal_last_end = last_speech_timestamp
+                    for si in range(len(current_segments)):
+                        segment = current_segments[si]
+                        if not segment["words"]:
+                            continue
+                        if is_segment_anomaly(segment):
+                            next_segment = next_words_segment(
+                                current_segments[si + 1 :]
+                            )
+                            if next_segment is not None:
+                                hal_next_start = next_segment["words"][0]["start"]
+                            else:
+                                hal_next_start = time_offset + segment_duration
+                            silence_before = (
+                                segment["start"] - hal_last_end > threshold
+                                or segment["start"] < threshold
+                                or segment["start"] - time_offset < 2.0
+                            )
+                            silence_after = (
+                                hal_next_start - segment["end"] > threshold
+                                or is_segment_anomaly(next_segment)
+                                or window_end_time - segment["end"] < 2.0
+                            )
+                            if silence_before and silence_after:
+                                seek = round(
+                                    max(time_offset + 1, segment["start"])
+                                    * FRAMES_PER_SECOND
+                                )
+                                if content_duration - segment["end"] < threshold:
+                                    seek = content_frames
+                                current_segments[si:] = []
+                                break
+                        hal_last_end = segment["end"]
+
+                last_word_end = get_end(current_segments)
+                if last_word_end is not None:
+                    last_speech_timestamp = last_word_end
 
             if verbose:
                 for segment in current_segments:
@@ -384,10 +517,17 @@ def transcribe(
 def cli():
     from . import available_models
 
+    def valid_model_name(name):
+        if name in available_models() or os.path.exists(name):
+            return name
+        raise ValueError(
+            f"model should be one of {available_models()} or path to a model checkpoint"
+        )
+
     # fmt: off
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
-    parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
+    parser.add_argument("--model", default="turbo", type=valid_model_name, help="name of the Whisper model to use")
     parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
     parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
     parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
@@ -405,6 +545,8 @@ def cli():
 
     parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
     parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
+    parser.add_argument("--carry_initial_prompt", type=str2bool, default=False, help="if True, prepend initial_prompt to every internal decode() call. May reduce the effectiveness of condition_on_previous_text")
+
     parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
     parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
 
@@ -418,7 +560,10 @@ def cli():
     parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt")
     parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line")
     parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment")
+    parser.add_argument("--max_words_per_line", type=optional_int, default=None, help="(requires --word_timestamps True, no effect with --max_line_width) the maximum number of words in a segment")
     parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
+    parser.add_argument("--clip_timestamps", type=str, default="0", help="comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process, where the last end timestamp defaults to the end of the file")
+    parser.add_argument("--hallucination_silence_threshold", type=optional_float, help="(requires --word_timestamps True) skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected")
     # fmt: on
 
     args = parser.parse_args().__dict__
@@ -450,17 +595,28 @@ def cli():
     model = load_model(model_name, device=device, download_root=model_dir)
 
     writer = get_writer(output_format, output_dir)
-    word_options = ["highlight_words", "max_line_count", "max_line_width"]
+    word_options = [
+        "highlight_words",
+        "max_line_count",
+        "max_line_width",
+        "max_words_per_line",
+    ]
     if not args["word_timestamps"]:
         for option in word_options:
             if args[option]:
                 parser.error(f"--{option} requires --word_timestamps True")
     if args["max_line_count"] and not args["max_line_width"]:
         warnings.warn("--max_line_count has no effect without --max_line_width")
+    if args["max_words_per_line"] and args["max_line_width"]:
+        warnings.warn("--max_words_per_line has no effect with --max_line_width")
     writer_args = {arg: args.pop(arg) for arg in word_options}
     for audio_path in args.pop("audio"):
-        result = transcribe(model, audio_path, temperature=temperature, **args)
-        writer(result, audio_path, writer_args)
+        try:
+            result = transcribe(model, audio_path, temperature=temperature, **args)
+            writer(result, audio_path, **writer_args)
+        except Exception as e:
+            traceback.print_exc()
+            print(f"Skipping {audio_path} due to {type(e).__name__}: {str(e)}")
 
 
 if __name__ == "__main__":

whisperlivekit/simul_whisper/whisper/triton_ops.py

@@ -60,7 +60,7 @@ def median_kernel(filter_width: int):
         tl.store(y_ptr + offsets, MIDDLE_ROW_HERE, mask=mask)  # noqa: F821
 
     kernel = triton.JITFunction(kernel.fn)
-    kernel.src = kernel.src.replace(
+    new_kernel = kernel.src.replace(
         "    LOAD_ALL_ROWS_HERE",
         "\n".join(
             [
@@ -69,7 +69,8 @@ def median_kernel(filter_width: int):
             ]
         ),
     )
-    kernel.src = kernel.src.replace(
+
+    new_kernel = new_kernel.replace(
         "    BUBBLESORT_HERE",
         "\n\n".join(
             [
@@ -90,7 +91,14 @@ def median_kernel(filter_width: int):
             ]
         ),
     )
-    kernel.src = kernel.src.replace("MIDDLE_ROW_HERE", f"row{filter_width // 2}")
+
+    new_kernel = new_kernel.replace("MIDDLE_ROW_HERE", f"row{filter_width // 2}")
+
+    if hasattr(kernel, "_unsafe_update_src") is True:
+        kernel._unsafe_update_src(new_kernel)
+        kernel.hash = None
+    else:
+        kernel.src = new_kernel
 
     return kernel
 

whisperlivekit/simul_whisper/whisper/utils.py

@@ -3,7 +3,7 @@ import os
 import re
 import sys
 import zlib
-from typing import Callable, Optional, TextIO
+from typing import Callable, List, Optional, TextIO
 
 system_encoding = sys.getdefaultencoding()
 
@@ -68,13 +68,29 @@ def format_timestamp(
     )
 
 
+def get_start(segments: List[dict]) -> Optional[float]:
+    return next(
+        (w["start"] for s in segments for w in s["words"]),
+        segments[0]["start"] if segments else None,
+    )
+
+
+def get_end(segments: List[dict]) -> Optional[float]:
+    return next(
+        (w["end"] for s in reversed(segments) for w in reversed(s["words"])),
+        segments[-1]["end"] if segments else None,
+    )
+
+
 class ResultWriter:
     extension: str
 
     def __init__(self, output_dir: str):
         self.output_dir = output_dir
 
-    def __call__(self, result: dict, audio_path: str, options: dict):
+    def __call__(
+        self, result: dict, audio_path: str, options: Optional[dict] = None, **kwargs
+    ):
         audio_basename = os.path.basename(audio_path)
         audio_basename = os.path.splitext(audio_basename)[0]
         output_path = os.path.join(
@@ -82,16 +98,20 @@ class ResultWriter:
         )
 
         with open(output_path, "w", encoding="utf-8") as f:
-            self.write_result(result, file=f, options=options)
+            self.write_result(result, file=f, options=options, **kwargs)
 
-    def write_result(self, result: dict, file: TextIO, options: dict):
+    def write_result(
+        self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs
+    ):
         raise NotImplementedError
 
 
 class WriteTXT(ResultWriter):
     extension: str = "txt"
 
-    def write_result(self, result: dict, file: TextIO, options: dict):
+    def write_result(
+        self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs
+    ):
         for segment in result["segments"]:
             print(segment["text"].strip(), file=file, flush=True)
 
@@ -100,48 +120,76 @@ class SubtitlesWriter(ResultWriter):
     always_include_hours: bool
     decimal_marker: str
 
-    def iterate_result(self, result: dict, options: dict):
-        raw_max_line_width: Optional[int] = options["max_line_width"]
-        max_line_count: Optional[int] = options["max_line_count"]
-        highlight_words: bool = options["highlight_words"]
-        max_line_width = 1000 if raw_max_line_width is None else raw_max_line_width
-        preserve_segments = max_line_count is None or raw_max_line_width is None
+    def iterate_result(
+        self,
+        result: dict,
+        options: Optional[dict] = None,
+        *,
+        max_line_width: Optional[int] = None,
+        max_line_count: Optional[int] = None,
+        highlight_words: bool = False,
+        max_words_per_line: Optional[int] = None,
+    ):
+        options = options or {}
+        max_line_width = max_line_width or options.get("max_line_width")
+        max_line_count = max_line_count or options.get("max_line_count")
+        highlight_words = highlight_words or options.get("highlight_words", False)
+        max_words_per_line = max_words_per_line or options.get("max_words_per_line")
+        preserve_segments = max_line_count is None or max_line_width is None
+        max_line_width = max_line_width or 1000
+        max_words_per_line = max_words_per_line or 1000
 
         def iterate_subtitles():
             line_len = 0
             line_count = 1
             # the next subtitle to yield (a list of word timings with whitespace)
-            subtitle: list[dict] = []
-            last = result["segments"][0]["words"][0]["start"]
+            subtitle: List[dict] = []
+            last: float = get_start(result["segments"]) or 0.0
             for segment in result["segments"]:
-                for i, original_timing in enumerate(segment["words"]):
-                    timing = original_timing.copy()
-                    long_pause = not preserve_segments and timing["start"] - last > 3.0
-                    has_room = line_len + len(timing["word"]) <= max_line_width
-                    seg_break = i == 0 and len(subtitle) > 0 and preserve_segments
-                    if line_len > 0 and has_room and not long_pause and not seg_break:
-                        # line continuation
-                        line_len += len(timing["word"])
-                    else:
-                        # new line
-                        timing["word"] = timing["word"].strip()
+                chunk_index = 0
+                words_count = max_words_per_line
+                while chunk_index < len(segment["words"]):
+                    remaining_words = len(segment["words"]) - chunk_index
+                    if max_words_per_line > len(segment["words"]) - chunk_index:
+                        words_count = remaining_words
+                    for i, original_timing in enumerate(
+                        segment["words"][chunk_index : chunk_index + words_count]
+                    ):
+                        timing = original_timing.copy()
+                        long_pause = (
+                            not preserve_segments and timing["start"] - last > 3.0
+                        )
+                        has_room = line_len + len(timing["word"]) <= max_line_width
+                        seg_break = i == 0 and len(subtitle) > 0 and preserve_segments
                         if (
-                            len(subtitle) > 0
-                            and max_line_count is not None
-                            and (long_pause or line_count >= max_line_count)
-                            or seg_break
+                            line_len > 0
+                            and has_room
+                            and not long_pause
+                            and not seg_break
                         ):
-                            # subtitle break
-                            yield subtitle
-                            subtitle = []
-                            line_count = 1
-                        elif line_len > 0:
-                            # line break
-                            line_count += 1
-                            timing["word"] = "\n" + timing["word"]
-                        line_len = len(timing["word"].strip())
-                    subtitle.append(timing)
-                    last = timing["start"]
+                            # line continuation
+                            line_len += len(timing["word"])
+                        else:
+                            # new line
+                            timing["word"] = timing["word"].strip()
+                            if (
+                                len(subtitle) > 0
+                                and max_line_count is not None
+                                and (long_pause or line_count >= max_line_count)
+                                or seg_break
+                            ):
+                                # subtitle break
+                                yield subtitle
+                                subtitle = []
+                                line_count = 1
+                            elif line_len > 0:
+                                # line break
+                                line_count += 1
+                                timing["word"] = "\n" + timing["word"]
+                            line_len = len(timing["word"].strip())
+                        subtitle.append(timing)
+                        last = timing["start"]
+                    chunk_index += max_words_per_line
             if len(subtitle) > 0:
                 yield subtitle
 
@@ -161,9 +209,11 @@ class SubtitlesWriter(ResultWriter):
 
                         yield start, end, "".join(
                             [
-                                re.sub(r"^(\s*)(.*)$", r"\1<u>\2</u>", word)
-                                if j == i
-                                else word
+                                (
+                                    re.sub(r"^(\s*)(.*)$", r"\1<u>\2</u>", word)
+                                    if j == i
+                                    else word
+                                )
                                 for j, word in enumerate(all_words)
                             ]
                         )
@@ -190,9 +240,11 @@ class WriteVTT(SubtitlesWriter):
     always_include_hours: bool = False
     decimal_marker: str = "."
 
-    def write_result(self, result: dict, file: TextIO, options: dict):
+    def write_result(
+        self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs
+    ):
         print("WEBVTT\n", file=file)
-        for start, end, text in self.iterate_result(result, options):
+        for start, end, text in self.iterate_result(result, options, **kwargs):
             print(f"{start} --> {end}\n{text}\n", file=file, flush=True)
 
 
@@ -201,9 +253,11 @@ class WriteSRT(SubtitlesWriter):
     always_include_hours: bool = True
     decimal_marker: str = ","
 
-    def write_result(self, result: dict, file: TextIO, options: dict):
+    def write_result(
+        self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs
+    ):
         for i, (start, end, text) in enumerate(
-            self.iterate_result(result, options), start=1
+            self.iterate_result(result, options, **kwargs), start=1
         ):
             print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True)
 
@@ -220,7 +274,9 @@ class WriteTSV(ResultWriter):
 
     extension: str = "tsv"
 
-    def write_result(self, result: dict, file: TextIO, options: dict):
+    def write_result(
+        self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs
+    ):
         print("start", "end", "text", sep="\t", file=file)
         for segment in result["segments"]:
             print(round(1000 * segment["start"]), file=file, end="\t")
@@ -231,7 +287,9 @@ class WriteTSV(ResultWriter):
 class WriteJSON(ResultWriter):
     extension: str = "json"
 
-    def write_result(self, result: dict, file: TextIO, options: dict):
+    def write_result(
+        self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs
+    ):
         json.dump(result, file)
 
 
@@ -249,9 +307,11 @@ def get_writer(
     if output_format == "all":
         all_writers = [writer(output_dir) for writer in writers.values()]
 
-        def write_all(result: dict, file: TextIO, options: dict):
+        def write_all(
+            result: dict, file: TextIO, options: Optional[dict] = None, **kwargs
+        ):
             for writer in all_writers:
-                writer(result, file, options)
+                writer(result, file, options, **kwargs)
 
         return write_all
 

whisperlivekit/simul_whisper/whisper/version.py

@@ -1 +1 @@
-__version__ = "20230918"
+__version__ = "20250625"

whisperlivekit/token_buffer.py

@@ -1,73 +0,0 @@
-import torch
-import sys
-class TokenBuffer:
-
-    def __init__(self, text="", tokenizer=None, device=None, prefix_token_ids=[]):
-        self.text = text
-        self.prefix_token_ids = prefix_token_ids
-        self.tokenizer = tokenizer
-        self.device = device
-
-    def as_token_ids(self, tokenizer=None):
-
-        if tokenizer is None:
-            tokenizer = self.tokenizer
-        if tokenizer is None:
-            raise ValueError("Tokenizer is not set.") 
-        return self.prefix_token_ids + tokenizer.encode(self.text)
-
-    def as_tensor(self, device=None):
-        if device is None:
-            device = self.device
-        if device is None:
-            raise ValueError("Device is not set.")
-        tok_ids = self.as_token_ids()
-        return torch.tensor(tok_ids, 
-                     dtype=torch.long, device=device).unsqueeze(0)
-
-    def as_tensor_beam(self, beam, device=None):
-        t = self.as_tensor(device=device)
-        return t.repeat_interleave(beam, dim=0)
-
-
-    def as_text(self):
-        return self.text
-
-    @staticmethod
-    def empty(*a, **kw):
-        return TokenBuffer(*a,**kw)
-
-    @staticmethod
-    def from_text(text, *a, **kw):
-        return TokenBuffer(*a, text=text, **kw)
-    
-    def is_empty(self):
-        return self.text is None or self.text == ""
-
-    def trim_words(self, num=1, after=0):
-        '''
-        num: how many words to trim from the beginning
-        after: how many characters to skip (length of the static prompt)
-        '''
-        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)
-        print(words, file=sys.stderr)
-        print(wids, file=sys.stderr)
-        if not words:
-            return 0
-        self.text = self.text[:after] + "".join(words[num:])
-        return sum(len(wi) for wi in wids[:num])
-
-    def append_token_ids(self, token_ids):
-        tokenizer = self.tokenizer
-        assert tokenizer is not None, "Tokenizer is not set."
-        self.text += self.tokenizer.decode(token_ids)
-
-    def as_split_word_tokens(self):
-        tokenizer = self.tokenizer
-        assert tokenizer is not None, "Tokenizer is not set."
-        ids = tokenizer.encode(self.text)
-        return tokenizer.split_to_word_tokens(ids)

whisperlivekit/warmup.py

@@ -0,0 +1,62 @@
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+def load_file(warmup_file=None, timeout=5):
+    import os
+    import tempfile
+    import librosa
+        
+    if warmup_file is None:
+        # Download JFK sample if not already present
+        jfk_url = "https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav"
+        temp_dir = tempfile.gettempdir()
+        warmup_file = os.path.join(temp_dir, "whisper_warmup_jfk.wav")
+        
+        if not os.path.exists(warmup_file):
+            logger.debug(f"Downloading warmup file from {jfk_url}")
+            print(f"Downloading warmup file from {jfk_url}")
+            import time
+            import urllib.request
+            import urllib.error
+            import socket
+            
+            original_timeout = socket.getdefaulttimeout()
+            socket.setdefaulttimeout(timeout)
+            
+            start_time = time.time()
+            try:
+                urllib.request.urlretrieve(jfk_url, warmup_file)
+                logger.debug(f"Download successful in {time.time() - start_time:.2f}s")
+            except (urllib.error.URLError, socket.timeout) as e:
+                logger.warning(f"Download failed: {e}. Proceeding without warmup.")
+                return False
+            finally:
+                socket.setdefaulttimeout(original_timeout)
+    elif not warmup_file:
+        return False 
+    
+    if not warmup_file or not os.path.exists(warmup_file) or os.path.getsize(warmup_file) == 0:
+        logger.warning(f"Warmup file {warmup_file} invalid or missing.")
+        return False
+    
+    try:
+        audio, sr = librosa.load(warmup_file, sr=16000)
+    except Exception as e:
+        logger.warning(f"Failed to load audio file: {e}")
+        return False
+    return audio
+
+def warmup_asr(asr, warmup_file=None, timeout=5):
+    """
+    Warmup the ASR model by transcribing a short audio file.
+    """
+    audio = load_file(warmup_file=None, timeout=5)
+    asr.transcribe(audio)
+    logger.info("ASR model is warmed up")
+    
+def warmup_online(online, warmup_file=None, timeout=5):
+    audio = load_file(warmup_file=None, timeout=5)
+    online.warmup(audio)
+    logger.warning("ASR is warmed up")

whisperlivekit/web/live_transcription.html

@@ -4,12 +4,87 @@
 <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Audio Transcription</title>
+    <title>WhisperLiveKit</title>
     <style>
+        :root {
+            --bg: #ffffff;
+            --text: #111111;
+            --muted: #666666;
+            --border: #e5e5e5;
+            --chip-bg: rgba(0, 0, 0, 0.04);
+            --chip-text: #000000;
+            --spinner-border: #8d8d8d5c;
+            --spinner-top: #b0b0b0;
+            --silence-bg: #f3f3f3;
+            --loading-bg: rgba(255, 77, 77, 0.06);
+            --button-bg: #ffffff;
+            --button-border: #e9e9e9;
+            --wave-stroke: #000000;
+            --label-dia-text: #868686;
+            --label-trans-text: #111111;
+        }
+
+        @media (prefers-color-scheme: dark) {
+            :root:not([data-theme="light"]) {
+                --bg: #0b0b0b;
+                --text: #e6e6e6;
+                --muted: #9aa0a6;
+                --border: #333333;
+                --chip-bg: rgba(255, 255, 255, 0.08);
+                --chip-text: #e6e6e6;
+                --spinner-border: #555555;
+                --spinner-top: #dddddd;
+                --silence-bg: #1a1a1a;
+                --loading-bg: rgba(255, 77, 77, 0.12);
+                --button-bg: #111111;
+                --button-border: #333333;
+                --wave-stroke: #e6e6e6;
+                --label-dia-text: #b3b3b3;
+                --label-trans-text: #ffffff;
+            }
+        }
+
+        :root[data-theme="dark"] {
+            --bg: #0b0b0b;
+            --text: #e6e6e6;
+            --muted: #9aa0a6;
+            --border: #333333;
+            --chip-bg: rgba(255, 255, 255, 0.08);
+            --chip-text: #e6e6e6;
+            --spinner-border: #555555;
+            --spinner-top: #dddddd;
+            --silence-bg: #1a1a1a;
+            --loading-bg: rgba(255, 77, 77, 0.12);
+            --button-bg: #111111;
+            --button-border: #333333;
+            --wave-stroke: #e6e6e6;
+            --label-dia-text: #b3b3b3;
+            --label-trans-text: #ffffff;
+        }
+
+        :root[data-theme="light"] {
+            --bg: #ffffff;
+            --text: #111111;
+            --muted: #666666;
+            --border: #e5e5e5;
+            --chip-bg: rgba(0, 0, 0, 0.04);
+            --chip-text: #000000;
+            --spinner-border: #8d8d8d5c;
+            --spinner-top: #b0b0b0;
+            --silence-bg: #f3f3f3;
+            --loading-bg: rgba(255, 77, 77, 0.06);
+            --button-bg: #ffffff;
+            --button-border: #e9e9e9;
+            --wave-stroke: #000000;
+            --label-dia-text: #868686;
+            --label-trans-text: #111111;
+        }
         body {
             font-family: ui-sans-serif, system-ui, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
             margin: 20px;
             text-align: center;
+            background-color: var(--bg);
+            color: var(--text);
         }
 
         #recordButton {
@@ -17,10 +92,10 @@
             height: 50px;
             border: none;
             border-radius: 50%;
-            background-color: white;
+            background-color: var(--button-bg);
             cursor: pointer;
             transition: all 0.3s ease;
-            border: 1px solid rgb(233, 233, 233);
+            border: 1px solid var(--button-border);
             display: flex;
             align-items: center;
             justify-content: center;
@@ -94,14 +169,14 @@
         .timer {
             font-size: 14px;
             font-weight: 500;
-            color: #333;
+            color: var(--text);
             margin-left: 10px;
         }
 
         #status {
             margin-top: 20px;
             font-size: 16px;
-            color: #333;
+            color: var(--text);
         }
 
         .settings-container {
@@ -120,12 +195,14 @@
         }
 
         #chunkSelector,
-        #websocketInput {
+        #websocketInput,
+        #themeSelector {
             font-size: 16px;
             padding: 5px;
             border-radius: 5px;
-            border: 1px solid #ddd;
-            background-color: #ffffff;
+            border: 1px solid var(--border);
+            background-color: var(--button-bg);
+            color: var(--text);
             max-height: 30px;
         }
 
@@ -134,7 +211,8 @@
         }
 
         #chunkSelector:focus,
-        #websocketInput:focus {
+        #websocketInput:focus,
+        #themeSelector:focus {
             outline: none;
             border-color: #007bff;
         }
@@ -156,18 +234,18 @@
         }
 
         #linesTranscript strong {
-            color: #333;
+            color: var(--text);
         }
 
         #speaker {
-            border: 1px solid rgb(229, 229, 229);
+            border: 1px solid var(--border);
             border-radius: 100px;
             padding: 2px 10px;
             font-size: 14px;
             margin-bottom: 0px;
         }
         .label_diarization {
-            background-color: #ffffff66;
+            background-color: var(--chip-bg);
             border-radius: 8px 8px 8px 8px;
             padding: 2px 10px;
             margin-left: 10px;
@@ -175,11 +253,11 @@
             white-space: nowrap;
             font-size: 14px;
             margin-bottom: 0px;
-            color: rgb(134, 134, 134)
+            color: var(--label-dia-text)
         }
 
         .label_transcription {
-            background-color: #ffffff66;
+            background-color: var(--chip-bg);
             border-radius: 8px 8px 8px 8px;
             padding: 2px 10px;
             display: inline-block;
@@ -187,11 +265,11 @@
             margin-left: 10px;
             font-size: 14px;
             margin-bottom: 0px;
-            color: #000000
+            color: var(--label-trans-text)
         }
 
         #timeInfo {
-            color: #666;
+            color: var(--muted);
             margin-left: 10px;
         }
 
@@ -206,7 +284,7 @@
         }
 
         .buffer_diarization {
-            color: rgb(134, 134, 134);
+            color: var(--label-dia-text);
             margin-left: 4px;
         }
 
@@ -220,10 +298,10 @@
             display: inline-block;
             width: 8px;
             height: 8px;
-            border: 2px solid #8d8d8d5c;
-            border-top: 2px solid #6c6c6ce5;
+            border: 2px solid var(--spinner-border);
+            border-top: 2px solid var(--spinner-top);
             border-radius: 50%;
-            animation: spin 0.6s linear infinite;
+            animation: spin 0.7s linear infinite;
             vertical-align: middle;
             margin-bottom: 2px;
             margin-right: 5px;
@@ -236,16 +314,16 @@
         }
 
         .silence {
-            color: #666;
-            background-color: #f3f3f3;
+            color: var(--muted);
+            background-color: var(--silence-bg);
             font-size: 13px;
             border-radius: 30px;
             padding: 2px 10px;
         }
 
         .loading {
-            color: #666;
-            background-color: #ff4d4d0f;
+            color: var(--muted);
+            background-color: var(--loading-bg);
             border-radius: 8px 8px 8px 0px;
             padding: 2px 10px;
             font-size: 14px;
@@ -284,6 +362,14 @@
                 <label for="websocketInput">WebSocket URL:</label>
                 <input id="websocketInput" type="text" />
             </div>
+            <div>
+                <label for="themeSelector">Theme:</label>
+                <select id="themeSelector">
+                    <option value="system" selected>System</option>
+                    <option value="light">Light</option>
+                    <option value="dark">Dark</option>
+                </select>
+            </div>
         </div>
     </div>
 
@@ -299,6 +385,7 @@
         let chunkDuration = 1000;
         let websocketUrl = "ws://localhost:8000/asr";
         let userClosing = false;
+        let wakeLock = null;
         let startTime = null;
         let timerInterval = null;
         let audioContext = null;
@@ -309,6 +396,7 @@
         let animationFrame = null;
         let waitingForStop = false;
         let lastReceivedData = null;
+        let lastSignature = null;
         waveCanvas.width = 60 * (window.devicePixelRatio || 1);
         waveCanvas.height = 30 * (window.devicePixelRatio || 1);
         waveCtx.scale(window.devicePixelRatio || 1, window.devicePixelRatio || 1);
@@ -319,6 +407,57 @@
         const websocketInput = document.getElementById("websocketInput");
         const linesTranscriptDiv = document.getElementById("linesTranscript");
         const timerElement = document.querySelector(".timer");
+        const themeSelector = document.getElementById("themeSelector");
+
+        function getWaveStroke() {
+            const styles = getComputedStyle(document.documentElement);
+            const v = styles.getPropertyValue("--wave-stroke").trim();
+            return v || "#000";
+        }
+
+        let waveStroke = getWaveStroke();
+
+        function updateWaveStroke() {
+            waveStroke = getWaveStroke();
+        }
+
+        function applyTheme(pref) {
+            if (pref === "light") {
+                document.documentElement.setAttribute("data-theme", "light");
+            } else if (pref === "dark") {
+                document.documentElement.setAttribute("data-theme", "dark");
+            } else {
+                document.documentElement.removeAttribute("data-theme");
+            }
+            updateWaveStroke();
+        }
+
+        const savedThemePref = localStorage.getItem("themePreference") || "system";
+        applyTheme(savedThemePref);
+        if (themeSelector) {
+            themeSelector.value = savedThemePref;
+            themeSelector.addEventListener("change", () => {
+                const val = themeSelector.value;
+                localStorage.setItem("themePreference", val);
+                applyTheme(val);
+            });
+        }
+
+        const darkMq = window.matchMedia && window.matchMedia("(prefers-color-scheme: dark)");
+        const handleOsThemeChange = () => {
+            const pref = localStorage.getItem("themePreference") || "system";
+            if (pref === "system") updateWaveStroke();
+        };
+        if (darkMq && darkMq.addEventListener) {
+            darkMq.addEventListener("change", handleOsThemeChange);
+        } else if (darkMq && darkMq.addListener) {
+            darkMq.addListener(handleOsThemeChange);
+        }
+
+        function fmt1(x) {
+            const n = Number(x);
+            return Number.isFinite(n) ? n.toFixed(1) : x;
+        }
 
         const host = window.location.hostname || "localhost";
         const port = window.location.port;
@@ -446,10 +585,35 @@
 
         function renderLinesWithBuffer(lines, buffer_diarization, buffer_transcription, remaining_time_diarization, remaining_time_transcription, isFinalizing = false, current_status = "active_transcription") {
             if (current_status === "no_audio_detected") {
-                linesTranscriptDiv.innerHTML = "<p style='text-align: center; color: #666; margin-top: 20px;'><em>No audio detected...</em></p>";
+                linesTranscriptDiv.innerHTML = "<p style='text-align: center; color: var(--muted); margin-top: 20px;'><em>No audio detected...</em></p>";
                 return; 
             }
 
+            // try to keep stable DOM despite having updates every 0.1s. only update numeric lag values if structure hasn't changed
+            const showLoading = (!isFinalizing) && (lines || []).some(it => it.speaker == 0);
+            const showTransLag = !isFinalizing && remaining_time_transcription > 0;
+            const showDiaLag = !isFinalizing && !!buffer_diarization && remaining_time_diarization > 0;
+            const signature = JSON.stringify({
+                lines: (lines || []).map(it => ({ speaker: it.speaker, text: it.text, beg: it.beg, end: it.end })),
+                buffer_transcription: buffer_transcription || "",
+                buffer_diarization: buffer_diarization || "",
+                status: current_status,
+                showLoading,
+                showTransLag,
+                showDiaLag,
+                isFinalizing: !!isFinalizing
+            });
+            if (lastSignature === signature) {
+                const t = document.querySelector(".lag-transcription-value");
+                if (t) t.textContent = fmt1(remaining_time_transcription);
+                const d = document.querySelector(".lag-diarization-value");
+                if (d) d.textContent = fmt1(remaining_time_diarization);
+                const ld = document.querySelector(".loading-diarization-value");
+                if (ld) ld.textContent = fmt1(remaining_time_diarization);
+                return;
+            }
+            lastSignature = signature;
+
             const linesHtml = lines.map((item, idx) => {
                 let timeInfo = "";
                 if (item.beg !== undefined && item.end !== undefined) {
@@ -460,7 +624,7 @@
                 if (item.speaker === -2) {
                     speakerLabel = `<span class="silence">Silence<span id='timeInfo'>${timeInfo}</span></span>`;
                 } else if (item.speaker == 0 && !isFinalizing) {
-                    speakerLabel = `<span class='loading'><span class="spinner"></span><span id='timeInfo'>${remaining_time_diarization} second(s) of audio are undergoing diarization</span></span>`;
+                    speakerLabel = `<span class='loading'><span class="spinner"></span><span id='timeInfo'><span class="loading-diarization-value">${fmt1(remaining_time_diarization)}</span> second(s) of audio are undergoing diarization</span></span>`;
                 } else if (item.speaker == -1) {
                     speakerLabel = `<span id="speaker">Speaker 1<span id='timeInfo'>${timeInfo}</span></span>`;
                 } else if (item.speaker !== -1 && item.speaker !== 0) {
@@ -471,12 +635,12 @@
                 let currentLineText = item.text || "";
 
                 if (idx === lines.length - 1) { 
-                    if (!isFinalizing) {
+                    if (!isFinalizing && item.speaker !== -2) {
                         if (remaining_time_transcription > 0) {
-                             speakerLabel += `<span class="label_transcription"><span class="spinner"></span>Transcription lag <span id='timeInfo'>${remaining_time_transcription}s</span></span>`;
+                             speakerLabel += `<span class="label_transcription"><span class="spinner"></span>Transcription lag <span id='timeInfo'><span class="lag-transcription-value">${fmt1(remaining_time_transcription)}</span>s</span></span>`;
                         }
                         if (buffer_diarization && remaining_time_diarization > 0) {
-                             speakerLabel += `<span class="label_diarization"><span class="spinner"></span>Diarization lag<span id='timeInfo'>${remaining_time_diarization}s</span></span>`;
+                             speakerLabel += `<span class="label_diarization"><span class="spinner"></span>Diarization lag<span id='timeInfo'><span class="lag-diarization-value">${fmt1(remaining_time_diarization)}</span>s</span></span>`;
                         }
                     }
 
@@ -502,6 +666,7 @@
             }).join("");
 
             linesTranscriptDiv.innerHTML = linesHtml;
+            window.scrollTo({ top: document.body.scrollHeight, behavior: 'smooth' });
         }
 
         function updateTimer() {
@@ -522,7 +687,7 @@
             
             waveCtx.clearRect(0, 0, waveCanvas.width / (window.devicePixelRatio || 1), waveCanvas.height / (window.devicePixelRatio || 1));
             waveCtx.lineWidth = 1;
-            waveCtx.strokeStyle = 'rgb(0, 0, 0)';
+            waveCtx.strokeStyle = waveStroke;
             waveCtx.beginPath();
             
             const sliceWidth = (waveCanvas.width / (window.devicePixelRatio || 1)) / bufferLength;
@@ -549,6 +714,16 @@
 
         async function startRecording() {
             try {
+
+                // https://developer.mozilla.org/en-US/docs/Web/API/Screen_Wake_Lock_API
+                // create an async function to request a wake lock
+                try {
+                  wakeLock = await navigator.wakeLock.request("screen");
+                } catch (err) {
+                  // The Wake Lock request has failed - usually system related, such as battery.
+                  console.log("Error acquiring wake lock.")
+                }
+
                 const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
                 
                 audioContext = new (window.AudioContext || window.webkitAudioContext)();
@@ -578,6 +753,10 @@
         }
 
         async function stopRecording() {
+            wakeLock.release().then(() => {
+              wakeLock = null;
+            });
+  
             userClosing = true;
             waitingForStop = true;
             

whisperlivekit/whisper_streaming_custom/backends.py

@@ -3,43 +3,10 @@ import logging
 import io
 import soundfile as sf
 import math
-try: 
-    import torch
-except ImportError: 
-    torch = None
 from typing import List
 import numpy as np
 from whisperlivekit.timed_objects import ASRToken
-
 logger = logging.getLogger(__name__)
-SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS = ImportError(
-"""SimulStreaming dependencies are not available.
-Please install WhisperLiveKit using pip install "whisperlivekit[simulstreaming]"
-""")
-
-try:
-    from whisperlivekit.simul_whisper.config import AlignAttConfig
-    from whisperlivekit.simul_whisper.simul_whisper import PaddedAlignAttWhisper, DEC_PAD
-    from whisperlivekit.simul_whisper.whisper import tokenizer
-    SIMULSTREAMING_AVAILABLE = True
-except ImportError:
-    logger.warning("⚠️ SimulStreaming dependencies not available. Attempting to download them.")
-    try:
-        from whisperlivekit import download_simulstreaming_backend
-        download_simulstreaming_backend()
-        from whisperlivekit.simul_whisper.config import AlignAttConfig
-        from whisperlivekit.simul_whisper.simul_whisper import PaddedAlignAttWhisper, DEC_PAD
-        from whisperlivekit.simul_whisper.whisper import tokenizer
-        SIMULSTREAMING_AVAILABLE = True
-        logger.info("SimulStreaming dependencies downloaded successfully.")
-    except Exception as e:
-        logger.error(f"Failed to download or import SimulStreaming dependencies: {e}")
-        SIMULSTREAMING_AVAILABLE = False
-        AlignAttConfig = None
-        PaddedAlignAttWhisper = None
-        DEC_PAD = None
-        tokenizer = None
-
 class ASRBase:
     sep = " "  # join transcribe words with this character (" " for whisper_timestamped,
               # "" for faster-whisper because it emits the spaces when needed)
@@ -320,182 +287,4 @@ class OpenaiApiASR(ASRBase):
         self.use_vad_opt = True
 
     def set_translate_task(self):
-        self.task = "translate"
-
-
-class SimulStreamingASR(ASRBase):
-    """SimulStreaming backend with AlignAtt policy."""
-    sep = ""
-
-    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr, **kwargs):
-        if not SIMULSTREAMING_AVAILABLE:
-            raise SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS
-        with open("whisperlivekit/simul_whisper/dual_license_simulstreaming.md", "r") as f:
-            print("*"*80 + f.read() + "*"*80)
-        self.logfile = logfile
-        self.transcribe_kargs = {}
-        self.original_language = None if lan == "auto" else lan
-        
-        self.model_path = kwargs.get('model_path', './large-v3.pt')
-        self.frame_threshold = kwargs.get('frame_threshold', 25)
-        self.audio_max_len = kwargs.get('audio_max_len', 30.0)
-        self.audio_min_len = kwargs.get('audio_min_len', 0.0)
-        self.segment_length = kwargs.get('segment_length', 0.5)
-        self.beams = kwargs.get('beams', 1)
-        self.decoder_type = kwargs.get('decoder_type', 'greedy' if self.beams == 1 else 'beam')
-        self.task = kwargs.get('task', 'transcribe')
-        self.cif_ckpt_path = kwargs.get('cif_ckpt_path', None)
-        self.never_fire = kwargs.get('never_fire', False)
-        self.init_prompt = kwargs.get('init_prompt', None)
-        self.static_init_prompt = kwargs.get('static_init_prompt', None)
-        self.max_context_tokens = kwargs.get('max_context_tokens', None)
-        
-        if model_dir is not None:
-            self.model_path = model_dir
-        elif modelsize is not None: #For the moment the .en.pt models do not work!
-            model_mapping = {
-                'tiny': './tiny.pt',
-                'base': './base.pt',
-                'small': './small.pt',
-                'medium': './medium.pt',
-                'medium.en': './medium.en.pt',
-                'large-v1': './large-v1.pt',
-                'base.en': './base.en.pt',
-                'small.en': './small.en.pt',
-                'tiny.en': './tiny.en.pt',
-                'large-v2': './large-v2.pt',
-                'large-v3': './large-v3.pt',
-                'large': './large-v3.pt'
-            }
-            self.model_path = model_mapping.get(modelsize, f'./{modelsize}.pt')
-        
-        self.model = self.load_model(modelsize, cache_dir, model_dir)
-        
-        # Set up tokenizer for translation if needed
-        if self.task == "translate":
-            self.set_translate_task()
-
-    def load_model(self, modelsize, cache_dir, model_dir):
-        try:
-            cfg = AlignAttConfig(
-                model_path=self.model_path,
-                segment_length=self.segment_length,
-                frame_threshold=self.frame_threshold,
-                language=self.original_language,
-                audio_max_len=self.audio_max_len,
-                audio_min_len=self.audio_min_len,
-                cif_ckpt_path=self.cif_ckpt_path,
-                decoder_type="beam",
-                beam_size=self.beams,
-                task=self.task,
-                never_fire=self.never_fire,
-                init_prompt=self.init_prompt,
-                max_context_tokens=self.max_context_tokens,
-                static_init_prompt=self.static_init_prompt,
-            )
-            
-            logger.info(f"Loading SimulStreaming model with language: {self.original_language}")
-            model = PaddedAlignAttWhisper(cfg)
-            return model
-            
-        except Exception as e:
-            logger.error(f"Failed to load SimulStreaming model: {e}")
-            raise
-
-    def transcribe(self, audio, init_prompt=""):
-        """Transcribe audio using SimulStreaming."""
-        try:
-            if isinstance(audio, np.ndarray):
-                audio_tensor = torch.from_numpy(audio).float()
-            else:
-                audio_tensor = audio
-                
-            prompt = init_prompt if init_prompt else (self.init_prompt or "")
-            
-            result = self.model.infer(audio_tensor, init_prompt=prompt)
-            
-            if torch.is_tensor(result):
-                result = result[result < DEC_PAD]
-                
-            logger.debug(f"SimulStreaming transcription result: {result}")
-            return result
-            
-        except Exception as e:
-            logger.error(f"SimulStreaming transcription failed: {e}")
-            raise
-
-    def ts_words(self, result) -> List[ASRToken]:
-        """Convert SimulStreaming result to ASRToken list."""
-        tokens = []
-        
-        try:
-            if torch.is_tensor(result):
-                text = self.model.tokenizer.decode(result.cpu().numpy())
-            else:
-                text = str(result)
-                
-            if not text or len(text.strip()) == 0:
-                return tokens
-                
-            # We dont have word-level timestamps here. 1rst approach, should be improved later.
-            words = text.strip().split()
-            if not words:
-                return tokens
-            
-            duration_per_word = 0.1  # this will be modified based on actual audio duration 
-            #with the SimulStreamingOnlineProcessor
-            
-            for i, word in enumerate(words):
-                start_time = i * duration_per_word
-                end_time = (i + 1) * duration_per_word
-                
-                token = ASRToken(
-                    start=start_time,
-                    end=end_time,
-                    text=word,
-                    probability=1.0
-                )
-                tokens.append(token)
-                
-        except Exception as e:
-            logger.error(f"Error converting SimulStreaming result to tokens: {e}")
-            
-        return tokens
-
-    def segments_end_ts(self, result) -> List[float]:
-        """Get segment end timestamps."""
-        if torch.is_tensor(result):
-            num_tokens = len(result)
-            return [num_tokens * 0.1]  # rough estimate
-        return [1.0]
-
-    def use_vad(self):
-        """Enable VAD - SimulStreaming has different VAD handling."""
-        logger.info("VAD requested for SimulStreaming - handled internally by the model")
-        pass
-
-    def set_translate_task(self):
-        """Set up translation task."""
-        try:
-            self.model.tokenizer = tokenizer.get_tokenizer(
-                multilingual=True,
-                language=self.model.cfg.language,
-                num_languages=self.model.model.num_languages,
-                task="translate"
-            )
-            logger.info("SimulStreaming configured for translation task")
-        except Exception as e:
-            logger.error(f"Failed to configure SimulStreaming for translation: {e}")
-            raise
-
-    def warmup(self, audio, init_prompt=""):
-        """Warmup the SimulStreaming model."""
-        try:
-            if isinstance(audio, np.ndarray):
-                audio = torch.from_numpy(audio).float()
-            self.model.insert_audio(audio)
-            self.model.infer(True)
-            self.model.refresh_segment(complete=True)
-            logger.info("SimulStreaming model warmed up successfully")
-        except Exception as e:
-            logger.warning(f"SimulStreaming warmup failed: {e}")
+        self.task = "translate"

whisperlivekit/whisper_streaming_custom/online_asr.py

@@ -6,18 +6,6 @@ from whisperlivekit.timed_objects import ASRToken, Sentence, Transcript
 
 logger = logging.getLogger(__name__)
 
-# simulStreaming imports - we check if the files are here
-try:
-    import torch
-    from whisperlivekit.simul_whisper.config import AlignAttConfig
-    SIMULSTREAMING_AVAILABLE = True
-except ImportError:
-    logger.warning("SimulStreaming dependencies not available for online processor.")
-    SIMULSTREAMING_AVAILABLE = False
-    OnlineProcessorInterface = None
-    torch = None
-
-
 class HypothesisBuffer:
     """
     Buffer to store and process ASR hypothesis tokens.
@@ -528,205 +516,3 @@ class VACOnlineASRProcessor:
         """
         return self.online.concatenate_tokens(self.online.transcript_buffer.buffer)
 
-
-class SimulStreamingOnlineProcessor:
-    SAMPLING_RATE = 16000
-
-    def __init__(
-        self,
-        asr,
-        tokenize_method: Optional[callable] = None,
-        buffer_trimming: Tuple[str, float] = ("segment", 15),
-        confidence_validation = False,
-        logfile=sys.stderr,
-    ):
-        if not SIMULSTREAMING_AVAILABLE:
-            raise ImportError("SimulStreaming dependencies are not available.")
-        
-        self.asr = asr
-        self.tokenize = tokenize_method
-        self.logfile = logfile
-        self.confidence_validation = confidence_validation
-        self.init()
-        
-        # buffer does not work yet
-        self.buffer_trimming_way, self.buffer_trimming_sec = buffer_trimming
-
-    def init(self, offset: Optional[float] = None):
-        """Initialize or reset the processing state."""
-        self.audio_chunks = []
-        self.offset = offset if offset is not None else 0.0
-        self.is_last = False
-        self.beg = self.offset
-        self.end = self.offset
-        self.cumulative_audio_duration = 0.0
-        self.last_audio_stream_end_time = self.offset
-        
-        self.committed: List[ASRToken] = []
-        self.last_result_tokens: List[ASRToken] = []        
-        self.buffer_content = ""
-        self.processed_audio_duration = 0.0
-
-    def get_audio_buffer_end_time(self) -> float:
-        """Returns the absolute end time of the current audio buffer."""
-        return self.end
-
-    def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time: Optional[float] = None):
-        """Append an audio chunk to be processed by SimulStreaming."""
-        if torch is None:
-            raise ImportError("PyTorch is required for SimulStreaming but not available")
-            
-        # Convert numpy array to torch tensor
-        audio_tensor = torch.from_numpy(audio).float()
-        self.audio_chunks.append(audio_tensor)
-        
-        # Update timing
-        chunk_duration = len(audio) / self.SAMPLING_RATE
-        self.cumulative_audio_duration += chunk_duration
-        
-        if audio_stream_end_time is not None:
-            self.last_audio_stream_end_time = audio_stream_end_time
-            self.end = audio_stream_end_time
-        else:
-            self.end = self.offset + self.cumulative_audio_duration
-
-    def prompt(self) -> Tuple[str, str]:
-        """
-        Returns a tuple: (prompt, context).
-        SimulStreaming handles prompting internally, so we return empty strings.
-        """
-        return "", ""
-
-    def get_buffer(self):
-        """
-        Get the unvalidated buffer content.
-        """
-        buffer_end = self.end if hasattr(self, 'end') else None
-        return Transcript(
-            start=None, 
-            end=buffer_end, 
-            text=self.buffer_content, 
-            probability=None
-        )
-
-    def timestamped_text(self, tokens, generation):
-        # From the simulstreaming repo. self.model to self.asr.model
-        pr = generation["progress"]
-        if "result" not in generation:
-            split_words, split_tokens = self.asr.model.tokenizer.split_to_word_tokens(tokens)
-        else:
-            split_words, split_tokens = generation["result"]["split_words"], generation["result"]["split_tokens"]
-
-        frames = [p["most_attended_frames"][0] for p in pr]
-        tokens = tokens.copy()
-        ret = []
-        for sw,st in zip(split_words,split_tokens):
-            b = None
-            for stt in st:
-                t,f = tokens.pop(0), frames.pop(0)
-                if t != stt:
-                    raise ValueError(f"Token mismatch: {t} != {stt} at frame {f}.")
-                if b is None:
-                    b = f
-            e = f
-            out = (b*0.02, e*0.02, sw)
-            ret.append(out)
-            logger.debug(f"TS-WORD:\t{' '.join(map(str, out))}")
-        return ret
-
-    def process_iter(self) -> Tuple[List[ASRToken], float]:
-        """
-        Process accumulated audio chunks using SimulStreaming.
-        
-        Returns a tuple: (list of committed ASRToken objects, float representing the audio processed up to time).
-        """
-        if not self.audio_chunks:
-            return [], self.end
-
-        try:
-            # concatenate all audio chunks
-            if len(self.audio_chunks) == 1:
-                audio = self.audio_chunks[0]
-            else:
-                audio = torch.cat(self.audio_chunks, dim=0)
-            
-            audio_duration = audio.shape[0] / self.SAMPLING_RATE if audio.shape[0] > 0 else 0
-            self.processed_audio_duration += audio_duration
-            
-            self.audio_chunks = []
-            
-            logger.debug(f"SimulStreaming processing audio shape: {audio.shape}, duration: {audio_duration:.2f}s")
-            logger.debug(f"Current end time: {self.end:.2f}s, last stream time: {self.last_audio_stream_end_time:.2f}s")
-            
-            self.asr.model.insert_audio(audio)
-            tokens, generation_progress = self.asr.model.infer(is_last=self.is_last)
-            ts_words = self.timestamped_text(tokens, generation_progress)
-            text = self.asr.model.tokenizer.decode(tokens)
-            
-            new_tokens = []
-            for ts_word in ts_words:
-                
-                start, end, word = ts_word
-                token = ASRToken(
-                    start=start,
-                    end=end,
-                    text=word,
-                    probability=0.95  # fake prob. Maybe we can extract it from the model?
-                )
-                new_tokens.append(token)
-                self.committed.extend(new_tokens)
-            
-            return new_tokens, self.end
-
-            
-        except Exception as e:
-            logger.error(f"SimulStreaming processing error: {e}")
-            logger.error(f"Error details: {type(e).__name__}: {str(e)}")
-            return [], self.end
-
-    def finish(self) -> Tuple[List[ASRToken], float]:
-        logger.debug("SimulStreaming finish() called")
-        self.is_last = True        
-        final_tokens, final_time = self.process_iter()
-        self.is_last = False
-        return final_tokens, final_time
-
-    def concatenate_tokens(
-        self,
-        tokens: List[ASRToken],
-        sep: Optional[str] = None,
-        offset: float = 0
-    ) -> Transcript:
-        """Concatenate tokens into a Transcript object."""
-        sep = sep if sep is not None else self.asr.sep
-        text = sep.join(token.text for token in tokens)
-        probability = sum(token.probability for token in tokens if token.probability) / len(tokens) if tokens else None
-        if tokens:
-            start = offset + tokens[0].start
-            end = offset + tokens[-1].end
-        else:
-            start = None
-            end = None
-        return Transcript(start, end, text, probability=probability)
-
-    def chunk_at(self, time: float):
-        """
-        useless but kept for compatibility
-        """
-        logger.debug(f"SimulStreaming chunk_at({time:.2f}) - handled internally")
-        pass
-
-    def words_to_sentences(self, tokens: List[ASRToken]) -> List[Sentence]:
-        """
-        Create simple sentences.
-        """
-        if not tokens:
-            return []
-        
-        full_text = " ".join(token.text for token in tokens)
-        sentence = Sentence(
-            start=tokens[0].start,
-            end=tokens[-1].end,
-            text=full_text
-        )
-        return [sentence]

whisperlivekit/whisper_streaming_custom/whisper_online.py

@@ -5,8 +5,7 @@ import librosa
 from functools import lru_cache
 import time
 import logging
-from .backends import FasterWhisperASR, MLXWhisper, WhisperTimestampedASR, OpenaiApiASR, SimulStreamingASR, SIMULSTREAMING_AVAILABLE, SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS
-from .online_asr import OnlineASRProcessor, VACOnlineASRProcessor, SimulStreamingOnlineProcessor, SIMULSTREAMING_AVAILABLE as SIMULSTREAMING_ONLINE_AVAILABLE
+from .backends import FasterWhisperASR, MLXWhisper, WhisperTimestampedASR, OpenaiApiASR
 
 logger = logging.getLogger(__name__)
 
@@ -68,35 +67,7 @@ def backend_factory(args):
     backend = args.backend
     if backend == "openai-api":
         logger.debug("Using OpenAI API.")
-        asr = OpenaiApiASR(lan=args.lan)
-    elif backend == "simulstreaming":
-        logger.debug("Using SimulStreaming backend.")
-        if not SIMULSTREAMING_AVAILABLE:
-            raise SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS
-        
-        simulstreaming_kwargs = {}
-        for attr in ['frame_threshold', 'beams', 'decoder_type', 'audio_max_len', 'audio_min_len', 
-                     'cif_ckpt_path', 'never_fire', 'init_prompt', 'static_init_prompt', 
-                     'max_context_tokens', 'model_path']:
-            if hasattr(args, attr):
-                simulstreaming_kwargs[attr] = getattr(args, attr)
-        
-        # Add segment_length from min_chunk_size
-        simulstreaming_kwargs['segment_length'] = getattr(args, 'min_chunk_size', 0.5)
-        simulstreaming_kwargs['task'] = args.task
-        
-        size = args.model
-        t = time.time()
-        logger.info(f"Loading SimulStreaming {size} model for language {args.lan}...")
-        asr = SimulStreamingASR(
-            modelsize=size,
-            lan=args.lan,
-            cache_dir=getattr(args, 'model_cache_dir', None),
-            model_dir=getattr(args, 'model_dir', None),
-            **simulstreaming_kwargs
-        )
-        e = time.time()
-        logger.info(f"done. It took {round(e-t,2)} seconds.")
+        asr = OpenaiApiASR(lan=args.lan)        
     else:
         if backend == "faster-whisper":
             asr_cls = FasterWhisperASR
@@ -136,107 +107,4 @@ def backend_factory(args):
         tokenizer = create_tokenizer(tgt_language)
     else:
         tokenizer = None
-    return asr, tokenizer
-
-def online_factory(args, asr, tokenizer, logfile=sys.stderr):
-    if args.backend == "simulstreaming":
-        if not SIMULSTREAMING_ONLINE_AVAILABLE:
-            raise SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS
-        
-        logger.debug("Creating SimulStreaming online processor")
-        online = SimulStreamingOnlineProcessor(
-            asr,
-            tokenizer,
-            logfile=logfile,
-            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
-            confidence_validation=args.confidence_validation
-        )
-    elif args.vac:
-        online = VACOnlineASRProcessor(
-            args.min_chunk_size,
-            asr,
-            tokenizer,
-            logfile=logfile,
-            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
-            confidence_validation = args.confidence_validation
-        )
-    else:
-        online = OnlineASRProcessor(
-            asr,
-            tokenizer,
-            logfile=logfile,
-            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
-            confidence_validation = args.confidence_validation
-        )
-    return online
-  
-def asr_factory(args, logfile=sys.stderr):
-    """
-    Creates and configures an ASR and ASR Online instance based on the specified backend and arguments.
-    """
-    asr, tokenizer = backend_factory(args)
-    online = online_factory(args, asr, tokenizer, logfile=logfile)
-    return asr, online
-
-def warmup_asr(asr, warmup_file=None, timeout=5):
-    """
-    Warmup the ASR model by transcribing a short audio file.
-    """
-    import os
-    import tempfile
-    
-    is_simulstreaming = hasattr(asr, 'warmup') and callable(getattr(asr, 'warmup'))
-    
-    if warmup_file is None:
-        # Download JFK sample if not already present
-        jfk_url = "https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav"
-        temp_dir = tempfile.gettempdir()
-        warmup_file = os.path.join(temp_dir, "whisper_warmup_jfk.wav")
-        
-        if not os.path.exists(warmup_file):
-            logger.debug(f"Downloading warmup file from {jfk_url}")
-            print(f"Downloading warmup file from {jfk_url}")
-            import time
-            import urllib.request
-            import urllib.error
-            import socket
-            
-            original_timeout = socket.getdefaulttimeout()
-            socket.setdefaulttimeout(timeout)
-            
-            start_time = time.time()
-            try:
-                urllib.request.urlretrieve(jfk_url, warmup_file)
-                logger.debug(f"Download successful in {time.time() - start_time:.2f}s")
-            except (urllib.error.URLError, socket.timeout) as e:
-                logger.warning(f"Download failed: {e}. Proceeding without warmup.")
-                return False
-            finally:
-                socket.setdefaulttimeout(original_timeout)
-    elif not warmup_file:
-        return False 
-    
-    if not warmup_file or not os.path.exists(warmup_file) or os.path.getsize(warmup_file) == 0:
-        logger.warning(f"Warmup file {warmup_file} invalid or missing.")
-        return False
-    
-    print(f"Warming up {'SimulStreaming' if is_simulstreaming else 'Whisper'} with {warmup_file}")
-    try:
-        import librosa
-        audio, sr = librosa.load(warmup_file, sr=16000)
-    except Exception as e:
-        logger.warning(f"Failed to load audio file: {e}")
-        return False
-    
-    try:
-        if is_simulstreaming:
-            asr.warmup(audio)
-        else:
-            asr.transcribe(audio)
-        
-        logger.info(f"{'SimulStreaming' if is_simulstreaming else 'Whisper'} is warmed up")
-        return True
-        
-    except Exception as e:
-        logger.warning(f"Warmup failed: {e}")
-        return False
+    return asr, tokenizer