Merge branch 'main' of https://github.com/QuentinFuxa/whisper_streaming_web

https://github.com/QuentinFuxa/WhisperLiveKit/issues/90
https://github.com/QuentinFuxa/WhisperLiveKit/issues/87
https://github.com/QuentinFuxa/WhisperLiveKit/issues/81
https://github.com/QuentinFuxa/WhisperLiveKit/issues/2

Dockerfile

@@ -0,0 +1,82 @@
+FROM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+ARG EXTRAS
+ARG HF_PRECACHE_DIR
+ARG HF_TKN_FILE
+
+# Install system dependencies
+#RUN apt-get update && \
+#    apt-get install -y ffmpeg git && \
+#    apt-get clean && \
+#    rm -rf /var/lib/apt/lists/*
+
+# 2) Install system dependencies + Python + pip
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        python3 \
+        python3-pip \
+        ffmpeg \
+        git && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+
+COPY . .
+
+# Install WhisperLiveKit directly, allowing for optional dependencies
+#   Note: For gates modedls, need to add your HF toke. See README.md
+#         for more details.
+RUN if [ -n "$EXTRAS" ]; then \
+      echo "Installing with extras: [$EXTRAS]"; \
+      pip install --no-cache-dir .[$EXTRAS]; \
+    else \
+      echo "Installing base package only"; \
+      pip install --no-cache-dir .; \
+    fi
+
+# Enable in-container caching for Hugging Face models by: 
+# Note: If running multiple containers, better to map a shared
+# bucket. 
+#
+# A) Make the cache directory persistent via an anonymous volume.
+#    Note: This only persists for a single, named container. This is 
+#          only for convenience at de/test stage. 
+#          For prod, it is better to use a named volume via host mount/k8s.
+VOLUME ["/root/.cache/huggingface/hub"]
+
+# or
+# B) Conditionally copy a local pre-cache from the build context to the 
+#    container's cache via the HF_PRECACHE_DIR build-arg.
+#    WARNING: This will copy ALL files in the pre-cache location.
+
+# Conditionally copy a cache directory if provided
+RUN if [ -n "$HF_PRECACHE_DIR" ]; then \
+      echo "Copying Hugging Face cache from $HF_PRECACHE_DIR"; \
+      mkdir -p /root/.cache/huggingface/hub && \
+      cp -r $HF_PRECACHE_DIR/* /root/.cache/huggingface/hub; \
+    else \
+      echo "No local Hugging Face cache specified, skipping copy"; \
+    fi
+
+# Conditionally copy a Hugging Face token if provided
+
+RUN if [ -n "$HF_TKN_FILE" ]; then \
+      echo "Copying Hugging Face token from $HF_TKN_FILE"; \
+      mkdir -p /root/.cache/huggingface && \
+      cp $HF_TKN_FILE /root/.cache/huggingface/token; \
+    else \
+      echo "No Hugging Face token file specified, skipping token setup"; \
+    fi
+    
+# Expose port for the transcription server
+EXPOSE 8000
+
+ENTRYPOINT ["whisperlivekit-server", "--host", "0.0.0.0"]
+
+# Default args
+CMD ["--model", "tiny.en"]

README.md

@@ -1,158 +1,316 @@
 <h1 align="center">WhisperLiveKit</h1>
-<p align="center"><b>Real-time, Fully Local Whisper's Speech-to-Text and Speaker Diarization</b></p>
-
-
-This project is based on [Whisper Streaming](https://github.com/ufal/whisper_streaming) and lets you transcribe audio directly from your browser. Simply launch the local server and grant microphone access. Everything runs locally on your machine ✨
 
 <p align="center">
-  <img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/demo.png" alt="Demo Screenshot" width="730">
+  <img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/demo.png" alt="WhisperLiveKit Demo" width="730">
 </p>
 
-### Differences from [Whisper Streaming](https://github.com/ufal/whisper_streaming)
+<p align="center"><b>Real-time, Fully Local Speech-to-Text with Speaker Diarization</b></p>
 
-#### ⚙️ **Core Improvements**  
+<p align="center">
+  <a href="https://pypi.org/project/whisperlivekit/"><img alt="PyPI Version" src="https://img.shields.io/pypi/v/whisperlivekit?color=g"></a>
+  <a href="https://pepy.tech/project/whisperlivekit"><img alt="PyPI Downloads" src="https://static.pepy.tech/personalized-badge/whisperlivekit?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=downloads"></a>
+  <a href="https://pypi.org/project/whisperlivekit/"><img alt="Python Versions" src="https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-dark_green"></a>
+  <a href="https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/QuentinFuxa/WhisperLiveKit?color=blue"></a>
+</p>
+
+## 🚀 Overview
+
+This project is based on [Whisper Streaming](https://github.com/ufal/whisper_streaming) and lets you transcribe audio directly from your browser. WhisperLiveKit provides a complete backend solution for real-time speech transcription with an example frontend that you can customize for your own needs. Everything runs locally on your machine ✨
+
+### 🔄 Architecture
+
+WhisperLiveKit consists of two main components:
+
+- **Backend (Server)**: FastAPI WebSocket server that processes audio and provides real-time transcription
+- **Frontend Example**: Basic HTML & JavaScript implementation that demonstrates how to capture and stream audio
+
+> **Note**: We recommend installing this library on the server/backend. For the frontend, you can use and adapt the provided HTML template from [whisperlivekit/web/live_transcription.html](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/web/live_transcription.html) for your specific use case.
+
+### ✨ Key Features
+
+- **🎙️ Real-time Transcription** - Convert speech to text instantly as you speak
+- **👥 Speaker Diarization** - Identify different speakers in real-time using [Diart](https://github.com/juanmc2005/diart)
+- **🔒 Fully Local** - All processing happens on your machine - no data sent to external servers
+- **📱 Multi-User Support** - Handle multiple users simultaneously with a single backend/server
+ 
+### ⚙️ Differences from [Whisper Streaming](https://github.com/ufal/whisper_streaming)
+
+- **Multi-User Support** – Handles multiple users simultaneously by decoupling backend and online ASR
+- **MLX Whisper Backend** – Optimized for Apple Silicon for faster local processing
 - **Buffering Preview** – Displays unvalidated transcription segments
-- **Multi-User Support** – Handles multiple users simultaneously by decoupling backend and online asr
-- **MLX Whisper Backend** – Optimized for Apple Silicon for faster local processing.  
-- **Confidence validation** – Immediately validate high-confidence tokens for faster inference
+- **Confidence Validation** – Immediately validate high-confidence tokens for faster inference
+- **Apple Silicon Optimized** - MLX backend for faster local processing on Mac
 
-#### 🎙️ **Speaker Identification**  
-- **Real-Time Diarization** – Identify different speakers in real time using [Diart](https://github.com/juanmc2005/diart)
+## 📖 Quick Start
 
-#### 🌐 **Web & API**  
-- **Built-in Web UI** – Simple raw html browser interface with no frontend setup required
-- **FastAPI WebSocket Server** – Real-time speech-to-text processing with async FFmpeg streaming.  
-- **JavaScript Client** – Ready-to-use MediaRecorder implementation for seamless client-side integration.
+```bash
+# Install the package
+pip install whisperlivekit
 
-## Installation
+# Start the transcription server
+whisperlivekit-server --model tiny.en
 
-### Via pip
+# Open your browser at http://localhost:8000
+```
+
+That's it! Start speaking and watch your words appear on screen.
+
+## 🛠️ Installation Options
+
+### Install from PyPI (Recommended)
 
 ```bash
 pip install whisperlivekit
 ```
 
-### From source
+### Install from Source
 
-1. **Clone the Repository**:
-
-   ```bash
-   git clone https://github.com/QuentinFuxa/WhisperLiveKit
-   cd WhisperLiveKit
-   pip install -e .
-   ```
+```bash
+git clone https://github.com/QuentinFuxa/WhisperLiveKit
+cd WhisperLiveKit
+pip install -e .
+```
 
 ### System Dependencies
 
-You need to install FFmpeg on your system:
+FFmpeg is required:
 
-- Install system dependencies:
-    ```bash
-    # Install FFmpeg on your system (required for audio processing)
-    # For Ubuntu/Debian:
-    sudo apt install ffmpeg
-    
-    # For macOS:
-    brew install ffmpeg
-    
-    # For Windows:
-    # Download from https://ffmpeg.org/download.html and add to PATH
-    ```
+```bash
+# Ubuntu/Debian
+sudo apt install ffmpeg
 
-- Install required Python dependencies:
+# macOS
+brew install ffmpeg
 
-    ```bash
-    # Whisper streaming required dependencies
-    pip install librosa soundfile
+# Windows
+# Download from https://ffmpeg.org/download.html and add to PATH
+```
 
-    # Whisper streaming web required dependencies
-    pip install fastapi ffmpeg-python
-    ```
-- Install at least one whisper backend among:
+### Optional Dependencies
 
-    ```
-   whisper
-   whisper-timestamped
-   faster-whisper (faster backend on NVIDIA GPU)
-   mlx-whisper (faster backend on Apple Silicon)
+```bash
+# Voice Activity Controller (prevents hallucinations)
+pip install torch
+
+# Sentence-based buffer trimming
+pip install mosestokenizer wtpsplit
+pip install tokenize_uk  # If you work with Ukrainian text
+
+# Speaker diarization
+pip install diart
+
+# Alternative Whisper backends (default is faster-whisper)
+pip install whisperlivekit[whisper]              # Original Whisper
+pip install whisperlivekit[whisper-timestamped]  # Improved timestamps
+pip install whisperlivekit[mlx-whisper]          # Apple Silicon optimization
+pip install whisperlivekit[openai]               # OpenAI API
+```
+
+### 🎹 Pyannote Models Setup
+
+For diarization, you need access to pyannote.audio models:
+
+1. [Accept user conditions](https://huggingface.co/pyannote/segmentation) for the `pyannote/segmentation` model
+2. [Accept user conditions](https://huggingface.co/pyannote/segmentation-3.0) for the `pyannote/segmentation-3.0` model
+3. [Accept user conditions](https://huggingface.co/pyannote/embedding) for the `pyannote/embedding` model
+4. Login with HuggingFace:
+   ```bash
+   pip install huggingface_hub
+   huggingface-cli login
    ```
-- Optionnal dependencies
 
-    ```
-    # If you want to use VAC (Voice Activity Controller). Useful for preventing hallucinations
-    torch
-   
-    # If you choose sentences as buffer trimming strategy
-    mosestokenizer
-    wtpsplit
-    tokenize_uk # If you work with Ukrainian text
+## 💻 Usage Examples
 
-    # If you want to run the server using uvicorn (recommended)
-    uvicorn
+### Command-line Interface
 
-    # If you want to use diarization
-    diart
-    ```
+Start the transcription server with various options:
 
-    Diart uses by default [pyannote.audio](https://github.com/pyannote/pyannote-audio) models from the _huggingface hub_. To use them, please follow the steps described [here](https://github.com/juanmc2005/diart?tab=readme-ov-file#get-access-to--pyannote-models).
+```bash
+# Basic server with English model
+whisperlivekit-server --model tiny.en
 
+# Advanced configuration with diarization
+whisperlivekit-server --host 0.0.0.0 --port 8000 --model medium --diarization --language auto
+```
 
-3. **Run the FastAPI Server**:
+### Python API Integration (Backend)
 
-    ```bash
-    python whisper_fastapi_online_server.py --host 0.0.0.0 --port 8000
-    ```
+```python
+from whisperlivekit import WhisperLiveKit
+from whisperlivekit.audio_processor import AudioProcessor
+from fastapi import FastAPI, WebSocket
+import asyncio
+from fastapi.responses import HTMLResponse
 
-    **Parameters**
-   
-    The following parameters are supported:
-  
-    - `--host` and `--port` let you specify the server's IP/port. 
-    - `-min-chunk-size` sets the minimum chunk size for audio processing. Make sure this value aligns with the chunk size selected in the frontend. If not aligned, the system will work but may unnecessarily over-process audio data.
-    - `--transcription`: Enable/disable transcription (default: True)
-    - `--diarization`: Enable/disable speaker diarization (default: False)
-    - `--confidence-validation`: Use confidence scores for faster validation. Transcription will be faster but punctuation might be less accurate (default: True)
-    - `--warmup-file`: The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast. :
-      - If not set, uses https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav.
-      - If False, no warmup is performed.
-    - `--min-chunk-size` Minimum audio chunk size in seconds. It waits up to this time to do processing. If the processing takes shorter time, it waits, otherwise it processes the whole segment that was received by this time.
-    - `--model` {_tiny.en, tiny, base.en, base, small.en, small, medium.en, medium, large-v1, large-v2, large-v3, large, large-v3-turbo_}
-                        Name size of the Whisper model to use (default: tiny). The model is automatically downloaded from the model hub if not present in model cache dir.
-    - `--model_cache_dir` Overriding the default model cache dir where models downloaded from the hub are saved
-    - `--model_dir` Dir where Whisper model.bin and other files are saved. This option overrides --model and --model_cache_dir parameter.
-    - `--lan`, --language Source language code, e.g. en,de,cs, or 'auto' for language detection.
-    - `--task` {_transcribe, translate_} Transcribe or translate. If translate is set, we recommend avoiding the _large-v3-turbo_ backend, as it [performs significantly worse](https://github.com/QuentinFuxa/whisper_streaming_web/issues/40#issuecomment-2652816533) than other models for translation.
-    - `--backend` {_faster-whisper, whisper_timestamped, openai-api, mlx-whisper_} Load only this backend for Whisper processing.
-    - `--vac` Use VAC = voice activity controller. Requires torch.
-    - `--vac-chunk-size` VAC sample size in seconds.
-    - `--vad` Use VAD = voice activity detection, with the default parameters.
-    - `--buffer_trimming` {_sentence, segment_} Buffer trimming strategy -- trim completed sentences marked with punctuation mark and detected by sentence segmenter, or the completed segments returned by Whisper. Sentence segmenter must be installed for "sentence" option.
-    - `--buffer_trimming_sec` Buffer trimming length threshold in seconds. If buffer length is longer, trimming sentence/segment is triggered.
+# Initialize components
+app = FastAPI()
+kit = WhisperLiveKit(model="medium", diarization=True)
 
-5. **Open the Provided HTML**:
+# Serve the web interface
+@app.get("/")
+async def get():
+    return HTMLResponse(kit.web_interface())  # Use the built-in web interface
 
-    - By default, the server root endpoint `/` serves a simple `live_transcription.html` page.  
-    - Open your browser at `http://localhost:8000` (or replace `localhost` and `8000` with whatever you specified).  
-    - The page uses vanilla JavaScript and the WebSocket API to capture your microphone and stream audio to the server in real time.
+# Process WebSocket connections
+async def handle_websocket_results(websocket, results_generator):
+    async for response in results_generator:
+        await websocket.send_json(response)
 
-### How the Live Interface Works
+@app.websocket("/asr")
+async def websocket_endpoint(websocket: WebSocket):
+    audio_processor = AudioProcessor()
+    await websocket.accept()
+    results_generator = await audio_processor.create_tasks()
+    websocket_task = asyncio.create_task(
+        handle_websocket_results(websocket, results_generator)
+    )
 
-- Once you **allow microphone access**, the page records small chunks of audio using the **MediaRecorder** API in **webm/opus** format.  
-- These chunks are sent over a **WebSocket** to the FastAPI endpoint at `/asr`.  
-- The Python server decodes `.webm` chunks on the fly using **FFmpeg** and streams them into the **whisper streaming** implementation for transcription.  
-- **Partial transcription** appears as soon as enough audio is processed. The “unvalidated” text is shown in **lighter or grey color** (i.e., an ‘aperçu’) to indicate it’s still buffered partial output. Once Whisper finalizes that segment, it’s displayed in normal text.  
-- You can watch the transcription update in near real time, ideal for demos, prototyping, or quick debugging.
+    try:
+        while True:
+            message = await websocket.receive_bytes()
+            await audio_processor.process_audio(message)
+    except Exception as e:
+        print(f"WebSocket error: {e}")
+        websocket_task.cancel()
+```
 
-### Deploying to a Remote Server
+### Frontend Implementation
 
-If you want to **deploy** this setup:
+The package includes a simple HTML/JavaScript implementation that you can adapt for your project. You can get in in [whisperlivekit/web/live_transcription.html](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/web/live_transcription.html), or using :
 
-1. **Host the FastAPI app** behind a production-grade HTTP(S) server (like **Uvicorn + Nginx** or Docker). If you use HTTPS, use "wss" instead of "ws" in WebSocket URL.
-2. The **HTML/JS page** can be served by the same FastAPI app or a separate static host.  
-3. Users open the page in **Chrome/Firefox** (any modern browser that supports MediaRecorder + WebSocket).  
+```python
+kit.web_interface()
+```
 
-No additional front-end libraries or frameworks are required. The WebSocket logic in `live_transcription.html` is minimal enough to adapt for your own custom UI or embed in other pages.
+## ⚙️ Configuration Reference
 
-## Acknowledgments
+WhisperLiveKit offers extensive configuration options:
 
-This project builds upon the foundational work of the Whisper Streaming project. We extend our gratitude to the original authors for their contributions.
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--host` | Server host address | `localhost` |
+| `--port` | Server port | `8000` |
+| `--model` | Whisper model size | `tiny` |
+| `--language` | Source language code or `auto` | `en` |
+| `--task` | `transcribe` or `translate` | `transcribe` |
+| `--backend` | Processing backend | `faster-whisper` |
+| `--diarization` | Enable speaker identification | `False` |
+| `--confidence-validation` | Use confidence scores for faster validation | `False` |
+| `--min-chunk-size` | Minimum audio chunk size (seconds) | `1.0` |
+| `--vac` | Use Voice Activity Controller | `False` |
+| `--no-vad` | Disable Voice Activity Detection | `False` |
+| `--buffer_trimming` | Buffer trimming strategy (`sentence` or `segment`) | `segment` |
+| `--warmup-file` | Audio file path for model warmup | `jfk.wav` |
+
+## 🔧 How It Works
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/demo.png" alt="WhisperLiveKit in Action" width="500">
+</p>
+
+1. **Audio Capture**: Browser's MediaRecorder API captures audio in webm/opus format
+2. **Streaming**: Audio chunks are sent to the server via WebSocket
+3. **Processing**: Server decodes audio with FFmpeg and streams into Whisper for transcription
+4. **Real-time Output**: 
+   - Partial transcriptions appear immediately in light gray (the 'aperçu')
+   - Finalized text appears in normal color
+   - (When enabled) Different speakers are identified and highlighted
+
+## 🚀 Deployment Guide
+
+To deploy WhisperLiveKit in production:
+
+1. **Server Setup** (Backend):
+   ```bash
+   # Install production ASGI server
+   pip install uvicorn gunicorn
+
+   # Launch with multiple workers
+   gunicorn -k uvicorn.workers.UvicornWorker -w 4 your_app:app
+   ```
+
+2. **Frontend Integration**:
+   - Host your customized version of the example HTML/JS in your web application
+   - Ensure WebSocket connection points to your server's address
+
+3. **Nginx Configuration** (recommended for production):
+   ```nginx
+   server {
+       listen 80;
+       server_name your-domain.com;
+
+       location / {
+           proxy_pass http://localhost:8000;
+           proxy_set_header Upgrade $http_upgrade;
+           proxy_set_header Connection "upgrade";
+           proxy_set_header Host $host;
+       }
+   }
+   ```
+
+4. **HTTPS Support**: For secure deployments, use "wss://" instead of "ws://" in WebSocket URL
+
+### 🐋 Docker
+
+A basic Dockerfile is provided which allows re-use of Python package installation options. See below usage examples:
+
+**NOTE:** For **larger** models, ensure that your **docker runtime** has enough **memory** available.
+
+#### 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
+```
+
+> **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
+```
+
+- `--build-arg` Options:
+  - `EXTRAS="whisper-timestamped"` - Add extras to the image's installation (no spaces). Remember to set necessary container options!
+  - `HF_PRECACHE_DIR="./.cache/"` - Pre-load a model cache for faster first-time start
+  - `HF_TOKEN="./token"` - Add your Hugging Face Hub access token to download gated models
+
+## 🔮 Use Cases
+
+- **Meeting Transcription**: Capture discussions in real-time
+- **Accessibility Tools**: Help hearing-impaired users follow conversations
+- **Content Creation**: Transcribe podcasts or videos automatically
+- **Customer Service**: Transcribe support calls with speaker identification
+
+## 🤝 Contributing
+
+Contributions are welcome! Here's how to get started:
+
+1. Fork the repository
+2. Create a feature branch: `git checkout -b feature/amazing-feature`
+3. Commit your changes: `git commit -m 'Add amazing feature'`
+4. Push to your branch: `git push origin feature/amazing-feature`
+5. Open a Pull Request
+
+## 🙏 Acknowledgments
+
+This project builds upon the foundational work of:
+- [Whisper Streaming](https://github.com/ufal/whisper_streaming)
+- [Diart](https://github.com/juanmc2005/diart)
+- [OpenAI Whisper](https://github.com/openai/whisper)
+
+We extend our gratitude to the original authors for their contributions.
+
+## 📄 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🔗 Links
+
+- [GitHub Repository](https://github.com/QuentinFuxa/WhisperLiveKit)
+- [PyPI Package](https://pypi.org/project/whisperlivekit/)
+- [Issue Tracker](https://github.com/QuentinFuxa/WhisperLiveKit/issues)

setup.py

@@ -1,8 +1,7 @@
 from setuptools import setup, find_packages
-
 setup(
     name="whisperlivekit",
-    version="0.1.0",
+    version="0.1.4",
     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",
@@ -22,13 +21,17 @@ setup(
         "diarization": ["diart"],
         "vac": ["torch"],
         "sentence": ["mosestokenizer", "wtpsplit"],
+        "whisper": ["whisper"],
+        "whisper-timestamped": ["whisper-timestamped"],
+        "mlx-whisper": ["mlx-whisper"],
+        "openai": ["openai"],
     },
     package_data={
         'whisperlivekit': ['web/*.html'],
     },
     entry_points={
         'console_scripts': [
-            'whisperlivekit-server=whisperlivekit.server:run_server',
+            'whisperlivekit-server=whisperlivekit.basic_server:main',
         ],
     },
     classifiers=[

whisperlivekit/audio_processor.py

@@ -6,7 +6,6 @@ import math
 import logging
 import traceback
 from datetime import timedelta
-from typing import List, Dict, Any
 from whisperlivekit.timed_objects import ASRToken
 from whisperlivekit.whisper_streaming_custom.whisper_online import online_factory
 from whisperlivekit.core import WhisperLiveKit
@@ -39,7 +38,10 @@ class AudioProcessor:
         self.bytes_per_sample = 2
         self.bytes_per_sec = self.samples_per_sec * self.bytes_per_sample
         self.max_bytes_per_sec = 32000 * 5  # 5 seconds of audio at 32 kHz
-        
+        self.last_ffmpeg_activity = time()
+        self.ffmpeg_health_check_interval = 5
+        self.ffmpeg_max_idle_time = 10
+ 
         # State management
         self.tokens = []
         self.buffer_transcription = ""
@@ -78,14 +80,50 @@ class AudioProcessor:
 
     async def restart_ffmpeg(self):
         """Restart the FFmpeg process after failure."""
+        logger.warning("Restarting FFmpeg process...")
+        
         if self.ffmpeg_process:
             try:
-                self.ffmpeg_process.kill()
-                await asyncio.get_event_loop().run_in_executor(None, self.ffmpeg_process.wait)
+                # we check if process is still running
+                if self.ffmpeg_process.poll() is None:
+                    logger.info("Terminating existing FFmpeg process")
+                    self.ffmpeg_process.stdin.close()
+                    self.ffmpeg_process.terminate()
+                    
+                    # wait for termination with timeout
+                    try:
+                        await asyncio.wait_for(
+                            asyncio.get_event_loop().run_in_executor(None, self.ffmpeg_process.wait),
+                            timeout=5.0
+                        )
+                    except asyncio.TimeoutError:
+                        logger.warning("FFmpeg process did not terminate, killing forcefully")
+                        self.ffmpeg_process.kill()
+                        await asyncio.get_event_loop().run_in_executor(None, self.ffmpeg_process.wait)
             except Exception as e:
-                logger.warning(f"Error killing FFmpeg process: {e}")
+                logger.error(f"Error during FFmpeg process termination: {e}")
+                logger.error(traceback.format_exc())
+        
+        # we start new process
+        try:
+            logger.info("Starting new FFmpeg process")
             self.ffmpeg_process = self.start_ffmpeg_decoder()
             self.pcm_buffer = bytearray()
+            self.last_ffmpeg_activity = time()
+            logger.info("FFmpeg process restarted successfully")
+        except Exception as e:
+            logger.error(f"Failed to restart FFmpeg process: {e}")
+            logger.error(traceback.format_exc())
+            # try again after 5s
+            await asyncio.sleep(5)
+            try:
+                self.ffmpeg_process = self.start_ffmpeg_decoder()
+                self.pcm_buffer = bytearray()
+                self.last_ffmpeg_activity = time()
+                logger.info("FFmpeg process restarted successfully on second attempt")
+            except Exception as e2:
+                logger.critical(f"Failed to restart FFmpeg process on second attempt: {e2}")
+                logger.critical(traceback.format_exc())
 
     async def update_transcription(self, new_tokens, buffer, end_buffer, full_transcription, sep):
         """Thread-safe update of transcription with new data."""
@@ -154,21 +192,33 @@ class AudioProcessor:
         
         while True:
             try:
-                # Calculate buffer size based on elapsed time
-                elapsed_time = math.floor((time() - beg) * 10) / 10  # Round to 0.1 sec
+                current_time = time()
+                elapsed_time = math.floor((current_time - beg) * 10) / 10
                 buffer_size = max(int(32000 * elapsed_time), 4096)
-                beg = time()
+                beg = current_time
 
-                # Read chunk with timeout
+                # Detect idle state much more quickly
+                if current_time - self.last_ffmpeg_activity > self.ffmpeg_max_idle_time:
+                    logger.warning(f"FFmpeg process idle for {current_time - self.last_ffmpeg_activity:.2f}s. Restarting...")
+                    await self.restart_ffmpeg()
+                    beg = time()
+                    self.last_ffmpeg_activity = time()
+                    continue
+
+                # Reduce timeout for reading from FFmpeg
                 try:
                     chunk = await asyncio.wait_for(
                         loop.run_in_executor(None, self.ffmpeg_process.stdout.read, buffer_size),
-                        timeout=15.0
+                        timeout=5.0  # Shorter timeout (5 seconds instead of 15)
                     )
+                    if chunk:
+                        self.last_ffmpeg_activity = time()
+                        
                 except asyncio.TimeoutError:
                     logger.warning("FFmpeg read timeout. Restarting...")
                     await self.restart_ffmpeg()
                     beg = time()
+                    self.last_ffmpeg_activity = time()
                     continue
 
                 if not chunk:
@@ -366,7 +416,7 @@ class AudioProcessor:
                 logger.warning(f"Exception in results_formatter: {e}")
                 logger.warning(f"Traceback: {traceback.format_exc()}")
                 await asyncio.sleep(0.5)  # Back off on error
-                
+        
     async def create_tasks(self):
         """Create and start processing tasks."""
             
@@ -378,6 +428,35 @@ class AudioProcessor:
             tasks.append(asyncio.create_task(self.diarization_processor(self.diarization)))
         
         tasks.append(asyncio.create_task(self.ffmpeg_stdout_reader()))
+        
+        # Monitor overall system health
+        async def watchdog():
+            while True:
+                try:
+                    await asyncio.sleep(10)  # Check every 10 seconds instead of 60
+                    
+                    current_time = time()
+                    # Check for stalled tasks
+                    for i, task in enumerate(tasks):
+                        if task.done():
+                            exc = task.exception() if task.done() else None
+                            task_name = task.get_name() if hasattr(task, 'get_name') else f"Task {i}"
+                            logger.error(f"{task_name} unexpectedly completed with exception: {exc}")
+                    
+                    # Check for FFmpeg process health with shorter thresholds
+                    ffmpeg_idle_time = current_time - self.last_ffmpeg_activity
+                    if ffmpeg_idle_time > 15:  # 15 seconds instead of 180
+                        logger.warning(f"FFmpeg idle for {ffmpeg_idle_time:.2f}s - may need attention")
+                        
+                        # Force restart after 30 seconds of inactivity (instead of 600)
+                        if ffmpeg_idle_time > 30:
+                            logger.error("FFmpeg idle for too long, forcing restart")
+                            await self.restart_ffmpeg()
+                            
+                except Exception as e:
+                    logger.error(f"Error in watchdog task: {e}")
+
+        tasks.append(asyncio.create_task(watchdog()))
         self.tasks = tasks
         
         return self.results_formatter()
@@ -399,11 +478,34 @@ class AudioProcessor:
 
     async def process_audio(self, message):
         """Process incoming audio data."""
-        try:
-            self.ffmpeg_process.stdin.write(message)
-            self.ffmpeg_process.stdin.flush()
-        except (BrokenPipeError, AttributeError) as e:
-            logger.warning(f"Error writing to FFmpeg: {e}. Restarting...")
-            await self.restart_ffmpeg()
-            self.ffmpeg_process.stdin.write(message)
-            self.ffmpeg_process.stdin.flush()
+        retry_count = 0
+        max_retries = 3
+        
+        # Log periodic heartbeats showing ongoing audio proc
+        current_time = time()
+        if not hasattr(self, '_last_heartbeat') or current_time - self._last_heartbeat >= 10:
+            logger.debug(f"Processing audio chunk, last FFmpeg activity: {current_time - self.last_ffmpeg_activity:.2f}s ago")
+            self._last_heartbeat = current_time
+        
+        while retry_count < max_retries:
+            try:
+                if not self.ffmpeg_process or not hasattr(self.ffmpeg_process, 'stdin') or self.ffmpeg_process.poll() is not None:
+                    logger.warning("FFmpeg process not available, restarting...")
+                    await self.restart_ffmpeg()
+                    
+                self.ffmpeg_process.stdin.write(message)
+                self.ffmpeg_process.stdin.flush()
+                self.last_ffmpeg_activity = time()  # Update activity timestamp
+                return
+                
+            except (BrokenPipeError, AttributeError, OSError) as e:
+                retry_count += 1
+                logger.warning(f"Error writing to FFmpeg: {e}. Retry {retry_count}/{max_retries}...")
+                
+                if retry_count < max_retries:
+                    await self.restart_ffmpeg()
+                    await asyncio.sleep(0.5)  # Shorter pause between retries
+                else:
+                    logger.error("Maximum retries reached for FFmpeg process")
+                    await self.restart_ffmpeg()
+                    return

whisperlivekit/basic_server.py

@@ -0,0 +1,86 @@
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi.responses import HTMLResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+from whisperlivekit import WhisperLiveKit
+from whisperlivekit.audio_processor import AudioProcessor
+
+import asyncio
+import logging
+import os
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logging.getLogger().setLevel(logging.WARNING)
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+kit = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global kit
+    kit = WhisperLiveKit()
+    yield
+
+app = FastAPI(lifespan=lifespan)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+async def get():
+    return HTMLResponse(kit.web_interface())
+
+
+async def handle_websocket_results(websocket, results_generator):
+    """Consumes results from the audio processor and sends them via WebSocket."""
+    try:
+        async for response in results_generator:
+            await websocket.send_json(response)
+    except Exception as e:
+        logger.warning(f"Error in WebSocket results handler: {e}")
+
+
+@app.websocket("/asr")
+async def websocket_endpoint(websocket: WebSocket):
+    audio_processor = AudioProcessor()
+
+    await websocket.accept()
+    logger.info("WebSocket connection opened.")
+            
+    results_generator = await audio_processor.create_tasks()
+    websocket_task = asyncio.create_task(handle_websocket_results(websocket, results_generator))
+
+    try:
+        while True:
+            message = await websocket.receive_bytes()
+            await audio_processor.process_audio(message)
+    except WebSocketDisconnect:
+        logger.warning("WebSocket disconnected.")
+    finally:
+        websocket_task.cancel()
+        await audio_processor.cleanup()
+        logger.info("WebSocket endpoint cleaned up.")
+
+def main():
+    """Entry point for the CLI command."""
+    import uvicorn
+    
+    temp_kit = WhisperLiveKit(transcription=False, diarization=False)
+    
+    uvicorn.run(
+        "whisperlivekit.basic_server:app", 
+        host=temp_kit.args.host, 
+        port=temp_kit.args.port, 
+        reload=True,
+        log_level="info"
+    )
+
+if __name__ == "__main__":
+    main()

whisperlivekit/core.py

@@ -1,4 +1,7 @@
-from whisperlivekit.whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
+try:
+    from whisperlivekit.whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
+except ImportError:
+    from .whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
 from argparse import Namespace, ArgumentParser
 
 def parse_args():
@@ -26,23 +29,21 @@ def parse_args():
 
     parser.add_argument(
         "--confidence-validation",
-        type=bool,
-        default=False,
+        action="store_true",
         help="Accelerates validation of tokens using confidence scores. Transcription will be faster but punctuation might be less accurate.",
     )
 
     parser.add_argument(
         "--diarization",
-        type=bool,
-        default=True,
-        help="Whether to enable speaker diarization.",
+        action="store_true",
+        default=False,
+        help="Enable speaker diarization.",
     )
 
     parser.add_argument(
-        "--transcription",
-        type=bool,
-        default=True,
-        help="To disable to only see live diarization results.",
+        "--no-transcription",
+        action="store_true",
+        help="Disable transcription to only see live diarization results.",
     )
     
     parser.add_argument(
@@ -51,15 +52,14 @@ def parse_args():
         default=0.5,
         help="Minimum audio chunk size in seconds. It waits up to this time to do processing. If the processing takes shorter time, it waits, otherwise it processes the whole segment that was received by this time.",
     )
+    
     parser.add_argument(
         "--model",
         type=str,
         default="tiny",
-        choices="tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large-v3,large,large-v3-turbo".split(
-            ","
-        ),
-        help="Name size of the Whisper model to use (default: large-v2). The model is automatically downloaded from the model hub if not present in model cache dir.",
+        help="Name size of the Whisper model to use (default: tiny). Suggested values: tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large-v3,large,large-v3-turbo. The model is automatically downloaded from the model hub if not present in model cache dir.",
     )
+    
     parser.add_argument(
         "--model_cache_dir",
         type=str,
@@ -102,12 +102,13 @@ def parse_args():
     parser.add_argument(
         "--vac-chunk-size", type=float, default=0.04, help="VAC sample size in seconds."
     )
+
     parser.add_argument(
-        "--vad",
+        "--no-vad",
         action="store_true",
-        default=True,
-        help="Use VAD = voice activity detection, with the default parameters.",
+        help="Disable VAD (voice activity detection).",
     )
+    
     parser.add_argument(
         "--buffer_trimming",
         type=str,
@@ -131,6 +132,12 @@ def parse_args():
     )
 
     args = parser.parse_args()
+    
+    args.transcription = not args.no_transcription
+    args.vad = not args.no_vad    
+    delattr(args, 'no_transcription')
+    delattr(args, 'no_vad')
+    
     return args
 
 class WhisperLiveKit:

whisperlivekit/web/live_transcription.html

@@ -38,7 +38,6 @@
             transform: scale(0.95);
         }
 
-        /* Shape inside the button */
         .shape-container {
             width: 25px;
             height: 25px;
@@ -56,6 +55,10 @@
             transition: all 0.3s ease;
         }
 
+        #recordButton:disabled .shape {
+            background-color: #6e6d6d;
+        }
+
         #recordButton.recording .shape {
             border-radius: 5px;
             width: 25px;
@@ -279,7 +282,7 @@
             </div>
             <div>
                 <label for="websocketInput">WebSocket URL:</label>
-                <input id="websocketInput" type="text" value="ws://localhost:8000/asr" />
+                <input id="websocketInput" type="text" />
             </div>
         </div>
     </div>
@@ -304,6 +307,7 @@
         let waveCanvas = document.getElementById("waveCanvas");
         let waveCtx = waveCanvas.getContext("2d");
         let animationFrame = null;
+        let waitingForStop = false;
         waveCanvas.width = 60 * (window.devicePixelRatio || 1);
         waveCanvas.height = 30 * (window.devicePixelRatio || 1);
         waveCtx.scale(window.devicePixelRatio || 1, window.devicePixelRatio || 1);
@@ -315,6 +319,12 @@
         const linesTranscriptDiv = document.getElementById("linesTranscript");
         const timerElement = document.querySelector(".timer");
 
+        const host = window.location.hostname || "localhost";
+        const port = window.location.port || "8000";
+        const defaultWebSocketUrl = `ws://${host}:${port}/asr`;
+        websocketInput.value = defaultWebSocketUrl;
+        websocketUrl = defaultWebSocketUrl;
+
         chunkSelector.addEventListener("change", () => {
             chunkDuration = parseInt(chunkSelector.value);
         });
@@ -346,10 +356,16 @@
 
                 websocket.onclose = () => {
                     if (userClosing) {
-                        statusText.textContent = "WebSocket closed by user.";
+                        if (!statusText.textContent.includes("Recording stopped. Processing final audio")) { // This is a bit of a hack. We should have a better way to handle this. eg. using a status code.
+                            statusText.textContent = "Finished processing audio! Ready to record again.";
+                        }
+                        waitingForStop = false;
                     } else {
                         statusText.textContent =
                             "Disconnected from the WebSocket server. (Check logs if model is loading.)";
+                        if (isRecording) {
+                            stopRecording();
+                        }
                     }
                     userClosing = false;
                 };
@@ -363,6 +379,27 @@
                 websocket.onmessage = (event) => {
                     const data = JSON.parse(event.data);
                     
+                    // Check for status messages
+                    if (data.type === "ready_to_stop") {
+                        console.log("Ready to stop, closing WebSocket");
+
+                        // signal that we are not waiting for stop anymore
+                        waitingForStop = false;
+                        recordButton.disabled = false; // this should be elsewhere
+                        console.log("Record button enabled");
+
+                        //Now we can close the WebSocket
+                        if (websocket) {
+                            websocket.close();
+                            websocket = null;
+                        }
+
+
+                        
+                        return;
+                    }
+                    
+                    // Handle normal transcription updates
                     const { 
                         lines = [], 
                         buffer_transcription = "", 
@@ -494,8 +531,17 @@
             }
         }
 
-        function stopRecording() {
+        async function stopRecording() {
             userClosing = true;
+            waitingForStop = true;
+            
+            if (websocket && websocket.readyState === WebSocket.OPEN) {
+                // Send empty audio buffer as stop signal
+                const emptyBlob = new Blob([], { type: 'audio/webm' });
+                websocket.send(emptyBlob);
+                statusText.textContent = "Recording stopped. Processing final audio...";
+            }
+            
             if (recorder) {
                 recorder.stop();
                 recorder = null;
@@ -531,34 +577,67 @@
             timerElement.textContent = "00:00";
             startTime = null;
             
-            isRecording = false;
-
-            if (websocket) {
-                websocket.close();
-                websocket = null;
+            if (websocket && websocket.readyState === WebSocket.OPEN) {
+                try {
+                    await websocket.send(JSON.stringify({
+                        type: "stop",
+                        message: "User stopped recording"
+                    }));
+                    statusText.textContent = "Recording stopped. Processing final audio...";
+                } catch (e) {
+                    console.error("Could not send stop message:", e);
+                    statusText.textContent = "Recording stopped. Error during final audio processing.";
+                    websocket.close();
+                    websocket = null;
+                }
             }
-
+            
+            isRecording = false;
             updateUI();	
         }
 
         async function toggleRecording() {
             if (!isRecording) {
-                linesTranscriptDiv.innerHTML = "";
+                if (waitingForStop) {
+                    console.log("Waiting for stop, early return");
+                    return;  // Early return, UI is already updated
+                }
+                console.log("Connecting to WebSocket");
                 try {
-                    await setupWebSocket();
-                    await startRecording();
+                    // If we have an active WebSocket that's still processing, just restart audio capture
+                    if (websocket && websocket.readyState === WebSocket.OPEN) {
+                        await startRecording();
+                    } else {
+                        // If no active WebSocket or it's closed, create new one
+                        await setupWebSocket();
+                        await startRecording();
+                    }
                 } catch (err) {
                     statusText.textContent = "Could not connect to WebSocket or access mic. Aborted.";
                     console.error(err);
                 }
             } else {
+                console.log("Stopping recording");
                 stopRecording();
             }
         }
 
         function updateUI() {
             recordButton.classList.toggle("recording", isRecording);
-            statusText.textContent = isRecording ? "Recording..." : "Click to start transcription";
+            
+            if (waitingForStop) {
+                statusText.textContent = "Please wait for processing to complete...";
+                recordButton.disabled = true;  // Optionally disable the button while waiting
+                console.log("Record button disabled");
+            } else if (isRecording) {
+                statusText.textContent = "Recording...";
+                recordButton.disabled = false;
+                console.log("Record button enabled");
+            } else {
+                statusText.textContent = "Click to start transcription";
+                recordButton.disabled = false;
+                console.log("Record button enabled");
+            }
         }
 
         recordButton.addEventListener("click", toggleRecording);

whisperlivekit/whisper_streaming_custom/backends.py

@@ -3,7 +3,10 @@ import logging
 import io
 import soundfile as sf
 import math
-import torch
+try: 
+    import torch
+except ImportError: 
+    torch = None
 from typing import List
 import numpy as np
 from whisperlivekit.timed_objects import ASRToken
@@ -102,8 +105,9 @@ class FasterWhisperASR(ASRBase):
             model_size_or_path = modelsize
         else:
             raise ValueError("Either modelsize or model_dir must be set")
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        compute_type = "float16" if device == "cuda" else "float32"
+        device = "auto" # Allow CTranslate2 to decide available device
+        compute_type = "auto" # Allow CTranslate2 to decide faster compute type
+                              
 
         model = WhisperModel(
             model_size_or_path,
@@ -249,8 +253,8 @@ class OpenaiApiASR(ASRBase):
         no_speech_segments = []
         if self.use_vad_opt:
             for segment in segments.segments:
-                if segment["no_speech_prob"] > 0.8:
-                    no_speech_segments.append((segment.get("start"), segment.get("end")))
+                if segment.no_speech_prob > 0.8:
+                    no_speech_segments.append((segment.start, segment.end))
         tokens = []
         for word in segments.words:
             start = word.start

whisperlivekit/whisper_streaming_custom/online_asr.py

@@ -216,31 +216,54 @@ class OnlineASRProcessor:
         """
         If the committed tokens form at least two sentences, chunk the audio
         buffer at the end time of the penultimate sentence.
+        Also ensures chunking happens if audio buffer exceeds a time limit.
         """
+        buffer_duration = len(self.audio_buffer) / self.SAMPLING_RATE        
         if not self.committed:
+            if buffer_duration > self.buffer_trimming_sec:
+                chunk_time = self.buffer_time_offset + (buffer_duration / 2)
+                logger.debug(f"--- No speech detected, forced chunking at {chunk_time:.2f}")
+                self.chunk_at(chunk_time)
             return
+        
         logger.debug("COMPLETED SENTENCE: " + " ".join(token.text for token in self.committed))
         sentences = self.words_to_sentences(self.committed)
         for sentence in sentences:
             logger.debug(f"\tSentence: {sentence.text}")
-        if len(sentences) < 2:
-            return
-        # Keep the last two sentences.
-        while len(sentences) > 2:
-            sentences.pop(0)
-        chunk_time = sentences[-2].end
-        logger.debug(f"--- Sentence chunked at {chunk_time:.2f}")
-        self.chunk_at(chunk_time)
+        
+        chunk_done = False
+        if len(sentences) >= 2:
+            while len(sentences) > 2:
+                sentences.pop(0)
+            chunk_time = sentences[-2].end
+            logger.debug(f"--- Sentence chunked at {chunk_time:.2f}")
+            self.chunk_at(chunk_time)
+            chunk_done = True
+        
+        if not chunk_done and buffer_duration > self.buffer_trimming_sec:
+            last_committed_time = self.committed[-1].end
+            logger.debug(f"--- Not enough sentences, chunking at last committed time {last_committed_time:.2f}")
+            self.chunk_at(last_committed_time)
 
     def chunk_completed_segment(self, res):
         """
         Chunk the audio buffer based on segment-end timestamps reported by the ASR.
+        Also ensures chunking happens if audio buffer exceeds a time limit.
         """
+        buffer_duration = len(self.audio_buffer) / self.SAMPLING_RATE        
         if not self.committed:
+            if buffer_duration > self.buffer_trimming_sec:
+                chunk_time = self.buffer_time_offset + (buffer_duration / 2)
+                logger.debug(f"--- No speech detected, forced chunking at {chunk_time:.2f}")
+                self.chunk_at(chunk_time)
             return
+        
+        logger.debug("Processing committed tokens for segmenting")
         ends = self.asr.segments_end_ts(res)
-        last_committed_time = self.committed[-1].end
+        last_committed_time = self.committed[-1].end        
+        chunk_done = False
         if len(ends) > 1:
+            logger.debug("Multiple segments available for chunking")
             e = ends[-2] + self.buffer_time_offset
             while len(ends) > 2 and e > last_committed_time:
                 ends.pop(-1)
@@ -248,11 +271,18 @@ class OnlineASRProcessor:
             if e <= last_committed_time:
                 logger.debug(f"--- Segment chunked at {e:.2f}")
                 self.chunk_at(e)
+                chunk_done = True
             else:
                 logger.debug("--- Last segment not within committed area")
         else:
             logger.debug("--- Not enough segments to chunk")
-
+        
+        if not chunk_done and buffer_duration > self.buffer_trimming_sec:
+            logger.debug(f"--- Buffer too large, chunking at last committed time {last_committed_time:.2f}")
+            self.chunk_at(last_committed_time)
+        
+        logger.debug("Segment chunking complete")
+        
     def chunk_at(self, time: float):
         """
         Trim both the hypothesis and audio buffer at the given time.
@@ -358,7 +388,7 @@ class VACOnlineASRProcessor:
         # Load a VAD model (e.g. Silero VAD)
         import torch
         model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad")
-        from silero_vad_iterator import FixedVADIterator
+        from .silero_vad_iterator import FixedVADIterator
 
         self.vac = FixedVADIterator(model)
         self.logfile = self.online.logfile