add eos to faster_whisper server

Signed-off-by: makaveli10 <vineet.suryan@collabora.com>

.github/workflows/ci.yml

@@ -1,4 +1,4 @@
-name: CI
+name: Test & Build CI/CD
 
 on:
   push:
@@ -7,45 +7,191 @@ on:
     tags:
       - v*
   pull_request:
-    branches:
+    branches: [ main ]
-      - main
+    types: [opened, synchronize, reopened]
 
 jobs:
-  build-and-push-package:
+  run-tests:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
+    strategy:
+      matrix:
+        python-version: [3.8, 3.9, '3.10', 3.11]
+    steps:
+      - uses: actions/checkout@v2
+      
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Cache Python dependencies
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/.cache/pip
+            !~/.cache/pip/log
+          key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('requirements/server.txt', 'requirements/client.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-${{ matrix.python-version }}-
+      
+      - name: Install system dependencies
+        run: sudo apt-get update && sudo apt-get install -y ffmpeg portaudio19-dev
+      
+      - name: Install Python dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements/server.txt --extra-index-url https://download.pytorch.org/whl/cpu
+          pip install -r requirements/client.txt
+      
+      - name: Run tests
+        run: |
+          echo "Running tests with Python ${{ matrix.python-version }}"
+          python -m unittest discover -s tests
+  
+  check-code-format:
+    runs-on: ubuntu-22.04
+    strategy:
+      matrix:
+        python-version: [3.8, 3.9, '3.10', 3.11]
 
     steps:
-    - name: Check Out Repository
+      - uses: actions/checkout@v2
-      uses: actions/checkout@v2
 
-    - name: Set up Python
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install flake8
+
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
+  build-and-push-docker-cpu:
+    needs: [run-tests, check-code-format]
+    runs-on: ubuntu-22.04
+    if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags/'))
+    steps:
+      - uses: actions/checkout@v2
+      
+      - name: Log in to GitHub Container Registry
+        uses: docker/login-action@v1
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GHCR_TOKEN }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v1
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v2
+        with:
+          context: .
+          file: docker/Dockerfile.cpu
+          push: true
+          tags: ghcr.io/collabora/whisperlive-cpu:latest
+  
+  build-and-push-docker-tensorrt:
+    needs: [run-tests, check-code-format]
+    timeout-minutes: 20
+    runs-on: ubuntu-22.04
+    if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags/'))
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Log in to GitHub Container Registry
+        uses: docker/login-action@v1
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GHCR_TOKEN }}
+
+      - name: Docker Prune
+        run: docker system prune -af
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v1
+
+      - name: Build and push Docker GPU image
+        uses: docker/build-push-action@v2
+        with:
+          context: .
+          file: docker/Dockerfile.tensorrt
+          push: true
+          tags: ghcr.io/collabora/whisperlive-tensorrt:latest
+
+  build-and-push-docker-gpu:
+    needs: [run-tests, check-code-format, build-and-push-docker-cpu]
+    timeout-minutes: 20
+    runs-on: ubuntu-22.04
+    if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags/'))
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Log in to GitHub Container Registry
+        uses: docker/login-action@v1
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GHCR_TOKEN }}
+
+      - name: Docker Prune
+        run: docker system prune -af
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v1
+
+      - name: Build and push Docker GPU image
+        uses: docker/build-push-action@v2
+        with:
+          context: .
+          file: docker/Dockerfile.gpu
+          push: true
+          tags: ghcr.io/collabora/whisperlive-gpu:latest
+
+  publish-to-pypi:
+    needs: [run-tests, check-code-format]
+    runs-on: ubuntu-22.04
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Set up Python 3.8
         uses: actions/setup-python@v2
         with:
           python-version: 3.8
 
-    - name: Set up FFmpeg
+      - name: Cache Python dependencies
-      uses: FedericoCarboni/setup-ffmpeg@v2
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/.cache/pip
+            !~/.cache/pip/log
+          key: ubuntu-latest-pip-3.8-${{ hashFiles('requirements/server.txt', 'requirements/client.txt') }}
+          restore-keys: |
+            ubuntu-latest-pip-3.8-
 
-    - name: Install Additional requirements
+      - name: Install system dependencies
+        run: sudo apt-get update && sudo apt-get install -y ffmpeg portaudio19-dev
+
+      - name: Install Python dependencies
         run: |
-        sudo apt-get -y install portaudio19-dev wget
+          pip install -r requirements/server.txt
-      shell: bash
+          pip install -r requirements/client.txt
+          pip install wheel
 
-    - name: Install Client Requirements
+      - name: Build package
-      run: pip install -r requirements/client.txt
+        run: python setup.py sdist bdist_wheel
 
-    - name: Install Server Requirements
+      - name: Publish package to PyPI
-      run: pip install -r requirements/server.txt
-
-    - name: Install Wheel for build 
-      run: pip install wheel twine
-    
-    - name: Build wheel
-      run: |
-        python setup.py sdist bdist_wheel
-    
-    - name: Push package on Test PyPI
-      if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           user: __token__

Audio-Transcription-Chrome/background.js

@@ -157,7 +157,8 @@ async function startCapture(options) {
           multilingual: options.useMultilingual,
           language: options.language,
           task: options.task,
-          modelSize: options.modelSize
+          modelSize: options.modelSize,
+          useVad: options.useVad,
         },
       });
     } else {

Audio-Transcription-Chrome/options.js

@@ -99,7 +99,8 @@ async function startRecord(option) {
           uid: uuid,
           language: option.language,
           task: option.task,
-          model: option.modelSize
+          model: option.modelSize,
+          use_vad: option.useVad
         })
       );
     };

Audio-Transcription-Chrome/popup.html

@@ -15,6 +15,10 @@
     <input type="checkbox" id="useServerCheckbox">
     <label for="useServerCheckbox">Use Collabora Whisper-Live Server</label>
   </div>
+  <div class="checkbox-container">
+    <input type="checkbox" id="useVadCheckbox">
+    <label for="useVadCheckbox">Use Voice Activity Detection</label>
+  </div>
   <div class="dropdown-container">
     <label for="languageDropdown">Select Language:</label>
     <select id="languageDropdown">

Audio-Transcription-Chrome/popup.js

@@ -4,6 +4,7 @@ document.addEventListener("DOMContentLoaded", function () {
   const stopButton = document.getElementById("stopCapture");
 
   const useServerCheckbox = document.getElementById("useServerCheckbox");
+  const useVadCheckbox = document.getElementById("useVadCheckbox");
   const languageDropdown = document.getElementById('languageDropdown');
   const taskDropdown = document.getElementById('taskDropdown');
   const modelSizeDropdown = document.getElementById('modelSizeDropdown');
@@ -31,6 +32,12 @@ document.addEventListener("DOMContentLoaded", function () {
     }
   });
 
+  chrome.storage.local.get("useVadState", ({ useVadState }) => {
+    if (useVadState !== undefined) {
+      useVadCheckbox.checked = useVadState;
+    }
+  });
+
   chrome.storage.local.get("selectedLanguage", ({ selectedLanguage: storedLanguage }) => {
     if (storedLanguage !== undefined) {
       languageDropdown.value = storedLanguage;
@@ -79,7 +86,8 @@ document.addEventListener("DOMContentLoaded", function () {
         port: port,
         language: selectedLanguage,
         task: selectedTask,
-        modelSize: selectedModelSize
+        modelSize: selectedModelSize,
+        useVad: useVadCheckbox.checked,
       }, () => {
         // Update capturing state in storage and toggle the buttons
         chrome.storage.local.set({ capturingState: { isCapturing: true } }, () => {
@@ -119,6 +127,7 @@ document.addEventListener("DOMContentLoaded", function () {
     startButton.disabled = isCapturing;
     stopButton.disabled = !isCapturing;
     useServerCheckbox.disabled = isCapturing;
+    useVadCheckbox.disabled = isCapturing;
     modelSizeDropdown.disabled = isCapturing;
     languageDropdown.disabled = isCapturing;
     taskDropdown.disabled = isCapturing; 
@@ -132,6 +141,11 @@ document.addEventListener("DOMContentLoaded", function () {
     chrome.storage.local.set({ useServerState });
   });
 
+  useVadCheckbox.addEventListener("change", () => {
+    const useVadState = useVadCheckbox.checked;
+    chrome.storage.local.set({ useVadState });
+  });
+
   languageDropdown.addEventListener('change', function() {
     if (languageDropdown.value === "") {
       selectedLanguage = null;

Audio-Transcription-Firefox/content.js

@@ -74,7 +74,8 @@ function startRecording(data) {
             uid: uuid,
             language: data.language,
             task: data.task,
-            model: data.modelSize
+            model: data.modelSize,
+            use_vad: data.useVad
         })
       );
     };

Audio-Transcription-Firefox/popup.html

@@ -15,6 +15,10 @@
     <input type="checkbox" id="useServerCheckbox">
     <label for="useServerCheckbox">Use Collabora Whisper-Live Server</label>
   </div>
+  <div class="checkbox-container">
+    <input type="checkbox" id="useVadCheckbox">
+    <label for="useVadCheckbox">Use Voice Activity Detection</label>
+  </div>
   <textarea id="waitTextBox" style="display: none;"></textarea>
   <div class="dropdown-container">
     <label for="languageDropdown">Select Language:</label>

Audio-Transcription-Firefox/popup.js

@@ -3,6 +3,7 @@ document.addEventListener("DOMContentLoaded", function() {
   const stopButton = document.getElementById("stopCapture");
 
   const useServerCheckbox = document.getElementById("useServerCheckbox");
+  const useVadCheckbox = document.getElementById("useVadCheckbox");
   const languageDropdown = document.getElementById('languageDropdown');
   const taskDropdown = document.getElementById('taskDropdown');
   const modelSizeDropdown = document.getElementById('modelSizeDropdown');
@@ -34,6 +35,12 @@ document.addEventListener("DOMContentLoaded", function() {
     }
   });
 
+  browser.storage.local.get("useVadState", ({ useVadState }) => {
+    if (useVadState !== undefined) {
+      useVadCheckbox.checked = useVadState;
+    }
+  });
+
   browser.storage.local.get("selectedLanguage", ({ selectedLanguage: storedLanguage }) => {
     if (storedLanguage !== undefined) {
       languageDropdown.value = storedLanguage;
@@ -76,7 +83,8 @@ document.addEventListener("DOMContentLoaded", function() {
               port: port,
               language: selectedLanguage,
               task: selectedTask,
-              modelSize: selectedModelSize
+              modelSize: selectedModelSize,
+              useVad: useVadCheckbox.checked,
             } 
           });
         toggleCaptureButtons(true);
@@ -115,6 +123,7 @@ document.addEventListener("DOMContentLoaded", function() {
     startButton.disabled = isCapturing;
     stopButton.disabled = !isCapturing;
     useServerCheckbox.disabled = isCapturing;
+    useVadCheckbox.disabled = isCapturing;
     modelSizeDropdown.disabled = isCapturing;
     languageDropdown.disabled = isCapturing;
     taskDropdown.disabled = isCapturing; 
@@ -128,6 +137,11 @@ document.addEventListener("DOMContentLoaded", function() {
     browser.storage.local.set({ useServerState });
   });
 
+  useVadCheckbox.addEventListener("change", () => {
+    const useVadState = useVadCheckbox.checked;
+    browser.storage.local.set({ useVadState });
+  });
+
   languageDropdown.addEventListener('change', function() {
     if (languageDropdown.value === "") {
       selectedLanguage = null;

README.md

@@ -1,9 +1,15 @@
-# whisper-live
+# WhisperLive
-A nearly-live implementation of OpenAI's Whisper.
 
-This project is a real-time transcription application that uses the OpenAI Whisper model to convert speech input into text output. It can be used to transcribe both live audio input from microphone and pre-recorded audio files.
+<h2 align="center">
+  <a href="https://www.youtube.com/watch?v=0PHWCApIcCI"><img
+src="https://img.youtube.com/vi/0PHWCApIcCI/0.jpg" style="background-color:rgba(0,0,0,0);" height=300 alt="WhisperLive"></a>
+  <br><br>A nearly-live implementation of OpenAI's Whisper.
+<br><br>
+</h2>
 
-Unlike traditional speech recognition systems that rely on continuous audio streaming, we use [voice activity detection (VAD)](https://github.com/snakers4/silero-vad) to detect the presence of speech and only send the audio data to whisper when speech is detected. This helps to reduce the amount of data sent to the whisper model and improves the accuracy of the transcription output.
+This project is a real-time transcription application that uses the OpenAI Whisper model
+to convert speech input into text output. It can be used to transcribe both live audio
+input from microphone and pre-recorded audio files.
 
 ## Installation
 - Install PyAudio and ffmpeg
@@ -30,7 +36,7 @@ python3 run_server.py --port 9090 \
   
 # running with custom model
 python3 run_server.py --port 9090 \
-                      --backend faster_whisper
+                      --backend faster_whisper \
                       -fw "/path/to/custom/faster/whisper/model"
 ```
 
@@ -47,10 +53,30 @@ python3 run_server.py -p 9090 \
                       -trt /home/TensorRT-LLM/examples/whisper/whisper_small \
                       -m
 ```
+#### Controlling OpenMP Threads
+To control the number of threads used by OpenMP, you can set the `OMP_NUM_THREADS` environment variable. This is useful for managing CPU resources and ensuring consistent performance. If not specified, `OMP_NUM_THREADS` is set to `1` by default. You can change this by using the `--omp_num_threads` argument:
+```bash
+python3 run_server.py --port 9090 \
+                      --backend faster_whisper \
+                      --omp_num_threads 4
+```
+
+#### Single model mode
+By default, when running the server without specifying a model, the server will instantiate a new whisper model for every client connection. This has the advantage, that the server can use different model sizes, based on the client's requested model size. On the other hand, it also means you have to wait for the model to be loaded upon client connection and you will have increased (V)RAM usage.
+
+When serving a custom TensorRT model using the `-trt` or a custom faster_whisper model using the `-fw` option, the server will instead only instantiate the custom model once and then reuse it for all client connections.
+
+If you don't want this, set `--no_single_model`.
 
 
 ### Running the Client
-- To transcribe an audio file:
+- Initializing the client with below parameters:
+  - `lang`: Language of the input audio, applicable only if using a multilingual model.
+  - `translate`: If set to `True` then translate from any language to `en`.
+  - `model`: Whisper model size.
+  - `use_vad`: Whether to use `Voice Activity Detection` on the server.
+  - `save_output_recording`: Set to True to save the microphone input as a `.wav` file during live transcription. This option is helpful for recording sessions for later playback or analysis. Defaults to `False`. 
+  - `output_recording_filename`: Specifies the `.wav` file path where the microphone input will be saved if `save_output_recording` is set to `True`.
 ```python
 from whisper_live.client import TranscriptionClient
 client = TranscriptionClient(
@@ -58,58 +84,61 @@ client = TranscriptionClient(
   9090,
   lang="en",
   translate=False,
-  model="small"
+  model="small",
+  use_vad=False,
+  save_output_recording=True,                         # Only used for microphone input, False by Default
+  output_recording_filename="./output_recording.wav"  # Only used for microphone input
 )
+```
+It connects to the server running on localhost at port 9090. Using a multilingual model, language for the transcription will be automatically detected. You can also use the language option to specify the target language for the transcription, in this case, English ("en"). The translate option should be set to `True` if we want to translate from the source language to English and `False` if we want to transcribe in the source language.
 
+- Transcribe an audio file:
+```python
 client("tests/jfk.wav")
 ```
-This command transcribes the specified audio file (audio.wav) using the Whisper model. It connects to the server running on localhost at port 9090. Using a multilingual model, language for the transcription will be automatically detected. You can also use the language option to specify the target language for the transcription, in this case, English ("en"). The translate option should be set to `True` if we want to translate from the source language to English and `False` if we want to transcribe in the source language.
 
 - To transcribe from microphone:
 ```python
-from whisper_live.client import TranscriptionClient
-client = TranscriptionClient(
-  "localhost",
-  9090,
-  lang="hi",
-  translate=True,
-  model="small"
-)
 client()
 ```
-This command captures audio from the microphone and sends it to the server for transcription. It uses the multilingual model with `hi` as the selected language. We use whisper `small` by default but can be changed to any other option based on the requirements and the hardware running the server.
+
+- To transcribe from a RTSP stream:
+```python
+client(rtsp_url="rtsp://admin:admin@192.168.0.1/rtsp")
+```
 
 - To transcribe from a HLS stream:
 ```python
-from whisper_live.client import TranscriptionClient
-client = TranscriptionClient(host, port, lang="en", translate=False) 
 client(hls_url="http://as-hls-ww-live.akamaized.net/pool_904/live/ww/bbc_1xtra/bbc_1xtra.isml/bbc_1xtra-audio%3d96000.norewind.m3u8")
 ```
-This command streams audio into the server from a HLS stream. It uses the same options as the previous command, using the multilingual model and specifying the target language and task.
 
-## Transcribe audio from browser
+## Browser Extensions
-- Run the server with your desired backend as shown [here](https://github.com/collabora/WhisperLive?tab=readme-ov-file#running-the-server)
+- Run the server with your desired backend as shown [here](https://github.com/collabora/WhisperLive?tab=readme-ov-file#running-the-server).
-
+- Transcribe audio directly from your browser using our Chrome or Firefox extensions. Refer to [Audio-Transcription-Chrome](https://github.com/collabora/whisper-live/tree/main/Audio-Transcription-Chrome#readme) and [Audio-Transcription-Firefox](https://github.com/collabora/whisper-live/tree/main/Audio-Transcription-Firefox#readme) for setup instructions.
-### Chrome Extension
-- Refer to [Audio-Transcription-Chrome](https://github.com/collabora/whisper-live/tree/main/Audio-Transcription-Chrome#readme) to use Chrome extension.
-
-### Firefox Extension
-- Refer to [Audio-Transcription-Firefox](https://github.com/collabora/whisper-live/tree/main/Audio-Transcription-Firefox#readme) to use Mozilla Firefox extension.
 
 ## Whisper Live Server in Docker
 - GPU
   - Faster-Whisper
   ```bash
-  docker build . -t whisper-live -f docker/Dockerfile.gpu
+  docker run -it --gpus all -p 9090:9090 ghcr.io/collabora/whisperlive-gpu:latest
-  docker run -it --gpus all -p 9090:9090 whisper-live:latest
   ```
 
-  - TensorRT. Follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) in order to setup docker and use TensorRT backend. We provide a pre-built docker image which has TensorRT-LLM built and ready to use.
+  - TensorRT. 
+  ```bash
+  docker run -p 9090:9090 --runtime=nvidia --gpus all --entrypoint /bin/bash -it ghcr.io/collabora/whisperlive-tensorrt
+
+  # Build tiny.en engine
+  bash build_whisper_tensorrt.sh /app/TensorRT-LLM-examples small.en
+
+  # Run server with tiny.en
+  python3 run_server.py --port 9090 \
+                        --backend tensorrt \
+                        --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_en"
+  ```
 
 - CPU
 ```bash
-docker build . -t whisper-live -f docker/Dockerfile.cpu
+docker run -it -p 9090:9090 ghcr.io/collabora/whisperlive-cpu:latest
-docker run -it -p 9090:9090 whisper-live:latest
 ```
 **Note**: By default we use "small" model size. To build docker image for a different model size, change the size in server.py and then build the docker image.
 
@@ -140,6 +169,5 @@ We are available to help you with both Open Source and proprietary AI projects.
   publisher = {GitHub},
   journal = {GitHub repository},
   howpublished = {\url{https://github.com/snakers4/silero-vad}},
-  commit = {insert_some_commit_here},
   email = {hello@silero.ai}
 }

TensorRT_whisper.md

@@ -1,6 +1,6 @@
-# Whisper-TensorRT
+# WhisperLive-TensorRT
 We have only tested the TensorRT backend in docker so, we recommend docker for a smooth TensorRT backend setup.
-**Note**: We use [our fork to setup TensorRT](https://github.com/makaveli10/TensorRT-LLM)
+**Note**: We use `tensorrt_llm==0.9.0`
 
 ## Installation
 - Install [docker](https://docs.docker.com/engine/install/)
@@ -12,56 +12,31 @@ git clone https://github.com/collabora/WhisperLive.git
 cd WhisperLive
 ```
 
-- Pull the TensorRT-LLM docker image which we prebuilt for WhisperLive TensorRT backend.
+- Run WhisperLive TensorRT in docker
 ```bash
-docker pull ghcr.io/collabora/whisperbot-base:latest
+docker run -p 9090:9090 --runtime=nvidia --gpus all --entrypoint /bin/bash -it ghcr.io/collabora/whisperlive-tensorrt:latest
 ```
 
-- Next, we run the docker image and mount WhisperLive repo to the containers `/home` directory.
-```bash
-docker run -it --gpus all --shm-size=8g \
-       --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
-       -v /path/to/WhisperLive:/home/WhisperLive \
-       ghcr.io/collabora/whisperbot-base:latest
-```
-
-- Make sure to test the installation. 
-```bash
-# export ENV=${ENV:-/etc/shinit_v2} 
-# source $ENV
-python -c "import torch; import tensorrt; import tensorrt_llm"
-```
-**NOTE**: Uncomment and update library paths if imports fail.
-
 ## Whisper TensorRT Engine
-- We build `small.en` and `small` multilingual TensorRT engine. The script logs the path of the directory with Whisper TensorRT engine. We need the model_path to run the server.
+- We build `small.en` and `small` multilingual TensorRT engine as examples below. The script logs the path of the directory with Whisper TensorRT engine. We need that model_path to run the server.
 ```bash
 # convert small.en
-bash scripts/build_whisper_tensorrt.sh /root/TensorRT-LLM-examples small.en
+bash build_whisper_tensorrt.sh /app/TensorRT-LLM-examples small.en
 
 # convert small multilingual model
-bash scripts/build_whisper_tensorrt.sh /root/TensorRT-LLM-examples small
+bash build_whisper_tensorrt.sh /app/TensorRT-LLM-examples small
 ```
 
 ## Run WhisperLive Server with TensorRT Backend
 ```bash
-cd /home/WhisperLive
-
-# Install requirements
-bash scripts/setup.sh
-pip install -r requirements/server.txt
-
-# Required to create mel spectogram
-wget --directory-prefix=assets assets/mel_filters.npz https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/mel_filters.npz
-
 # Run English only model
 python3 run_server.py --port 9090 \
                       --backend tensorrt \
-                      --trt_model_path "path/to/whisper_trt/from/build/step"
+                      --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_en"
 
 # Run Multilingual model
 python3 run_server.py --port 9090 \
                       --backend tensorrt \
-                      --trt_model_path "path/to/whisper_trt/from/build/step" \
+                      --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small" \
                       --trt_multilingual
 ```

docker/Dockerfile.cpu

@@ -1,45 +1,25 @@
-FROM ubuntu:focal
+FROM python:3.10-bookworm
 
 ARG DEBIAN_FRONTEND=noninteractive
 
-# Remove any third-party apt sources to avoid issues with expiring keys.
+# install lib required for pyaudio
-RUN rm -f /etc/apt/sources.list.d/*.list
+RUN apt update && apt install -y portaudio19-dev && apt-get clean && rm -rf /var/lib/apt/lists/*
 
-# Install some basic utilities.
+# update pip to support for whl.metadata -> less downloading
-RUN apt-get update && apt-get install -y \
+RUN pip install --no-cache-dir -U "pip>=24"
-    curl \
-    ca-certificates \
-    sudo \
-    git \
-    bzip2 \
-    libx11-6 \
- && rm -rf /var/lib/apt/lists/*
 
-RUN apt update
+# create a working directory
-
-# install python
-RUN apt install software-properties-common -y && \
-    add-apt-repository ppa:deadsnakes/ppa && \
-    apt update
-
-RUN apt install python3-dev -y && \
-    apt install python-is-python3
-    
-
-# install pip
-RUN apt install python3-pip -y
-
-# Create a working directory.
 RUN mkdir /app
 WORKDIR /app
 
-COPY scripts/setup.sh /app
+# install pytorch, but without the nvidia-libs that are only necessary for gpu
-COPY requirements/ /app
+RUN pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu
 
-RUN bash setup.sh
+# install the requirements for running the whisper-live server
-RUN pip install -r server.txt
+COPY requirements/server.txt /app/
+RUN pip install --no-cache-dir -r server.txt && rm server.txt
 
 COPY whisper_live /app/whisper_live
-
 COPY run_server.py /app
+
 CMD ["python", "run_server.py"]

docker/Dockerfile.gpu

@@ -1,47 +1,26 @@
-FROM nvidia/cuda:11.2.2-cudnn8-runtime-ubuntu20.04
+FROM python:3.10-bookworm
 
 ARG DEBIAN_FRONTEND=noninteractive
 
-# Remove any third-party apt sources to avoid issues with expiring keys.
+# install lib required for pyaudio
-RUN rm -f /etc/apt/sources.list.d/*.list
+RUN apt update && apt install -y portaudio19-dev && apt-get clean && rm -rf /var/lib/apt/lists/*
 
-# Install some basic utilities.
+# update pip to support for whl.metadata -> less downloading
-RUN apt-get update && apt-get install -y \
+RUN pip install --no-cache-dir -U "pip>=24"
-    curl \
-    ca-certificates \
-    sudo \
-    git \
-    bzip2 \
-    libx11-6 \
- && rm -rf /var/lib/apt/lists/*
 
-RUN apt update
+# create a working directory
-
-# install python
-RUN apt install software-properties-common -y && \
-    add-apt-repository ppa:deadsnakes/ppa && \
-    apt update
-
-RUN apt install python3-dev -y && \
-    apt install python-is-python3
-    
-
-# install pip
-RUN apt install python3-pip -y
-
-# Create a working directory.
 RUN mkdir /app
 WORKDIR /app
 
-COPY scripts/setup.sh /app
+# install the requirements for running the whisper-live server
-COPY requirements/ /app
+COPY requirements/server.txt /app/
+RUN pip install --no-cache-dir -r server.txt && rm server.txt
 
-RUN apt update --fix-missing
+# make the paths of the nvidia libs installed as wheels visible. equivalent to:
-RUN bash setup.sh
+# export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
-RUN pip install -r server.txt
+ENV LD_LIBRARY_PATH="/usr/local/lib/python3.10/site-packages/nvidia/cublas/lib:/usr/local/lib/python3.10/site-packages/nvidia/cudnn/lib"
 
 COPY whisper_live /app/whisper_live
-
 COPY run_server.py /app
 
 CMD ["python", "run_server.py"]

docker/Dockerfile.tensorrt

@@ -0,0 +1,28 @@
+FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && apt-get install -y \
+    python3.10 python3-pip openmpi-bin libopenmpi-dev git wget \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip3 install --no-cache-dir -U tensorrt_llm==0.9.0 --extra-index-url https://pypi.nvidia.com
+
+WORKDIR /app
+
+RUN git clone -b v0.9.0 --depth 1 https://github.com/NVIDIA/TensorRT-LLM.git && \
+    mv TensorRT-LLM/examples ./TensorRT-LLM-examples && \
+    rm -rf TensorRT-LLM
+
+COPY assets/ ./assets
+RUN wget -nc -P assets/ https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/mel_filters.npz
+
+COPY scripts/setup.sh ./
+RUN apt update && bash setup.sh && rm setup.sh
+
+COPY requirements/server.txt .
+RUN pip install --no-cache-dir -r server.txt && rm server.txt
+
+COPY whisper_live ./whisper_live
+COPY scripts/build_whisper_tensorrt.sh .
+COPY run_server.py .

requirements/server.txt

@@ -1,4 +1,4 @@
-faster-whisper==0.10.0
+faster-whisper==1.0.1
 torch
 websockets
 onnxruntime==1.16.0
@@ -8,3 +8,6 @@ kaldialign
 soundfile
 ffmpeg-python
 scipy
+jiwer
+evaluate
+numpy<2

run_server.py

@@ -1,5 +1,5 @@
 import argparse
-from whisper_live.server import TranscriptionServer
+import os
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -21,12 +21,23 @@ if __name__ == "__main__":
     parser.add_argument('--trt_multilingual', '-m',
                         action="store_true",
                         help='Boolean only for TensorRT model. True if multilingual.')
+    parser.add_argument('--omp_num_threads', '-omp',
+                        type=int,
+                        default=1,
+                        help="Number of threads to use for OpenMP")
+    parser.add_argument('--no_single_model', '-nsm',
+                        action='store_true',
+                        help='Set this if every connection should instantiate its own model. Only relevant for custom model, passed using -trt or -fw.')
     args = parser.parse_args()
 
     if args.backend == "tensorrt":
         if args.trt_model_path is None:
             raise ValueError("Please Provide a valid tensorrt model path")
 
+    if "OMP_NUM_THREADS" not in os.environ:
+        os.environ["OMP_NUM_THREADS"] = str(args.omp_num_threads)
+
+    from whisper_live.server import TranscriptionServer
     server = TranscriptionServer()
     server.run(
         "0.0.0.0",
@@ -34,5 +45,6 @@ if __name__ == "__main__":
         backend=args.backend,
         faster_whisper_custom_model_path=args.faster_whisper_custom_model_path,
         whisper_tensorrt_path=args.trt_model_path,
-        trt_multilingual=args.trt_multilingual
+        trt_multilingual=args.trt_multilingual,
+        single_model=not args.no_single_model,
     )

scripts/build_whisper_tensorrt.sh

@@ -57,7 +57,7 @@ download_and_build_model() {
     local output_dir="whisper_${model_name//./_}"
     echo "$output_dir"
     echo "Running build script for $model_name with output directory $output_dir"
-    python3 build.py --output_dir "$output_dir" --use_gpt_attention_plugin --use_gemm_plugin --use_bert_attention_plugin --model_name "$model_name"
+    python3 build.py --output_dir "$output_dir" --use_gpt_attention_plugin --use_gemm_plugin  --use_bert_attention_plugin --enable_context_fmha --model_name "$model_name"
     echo "Whisper $model_name TensorRT engine built."
     echo "========================================="
     echo "Model is located at: $(pwd)/$output_dir"

setup.py

@@ -10,7 +10,8 @@ HERE = pathlib.Path(__file__).parent
 README = (HERE / "README.md").read_text()
 
 # This call to setup() does all the work
-setup(name="whisper-live",
+setup(
+    name="whisper-live",
     version=__version__,
     description="A nearly-live implementation of OpenAI's Whisper.",
     long_description=README,
@@ -32,7 +33,8 @@ setup(name="whisper-live",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
     ],
     packages=find_packages(
-        exclude=("examples",
+        exclude=(
+            "examples",
             "Audio-Transcription-Chrome",
             "Audio-Transcription-Firefox",
             "requirements",
@@ -41,7 +43,7 @@ setup(name="whisper-live",
     ),
     install_requires=[
         "PyAudio",
-        "faster-whisper==0.10.0",
+        "faster-whisper==1.0.1",
         "torch",
         "torchaudio",
         "websockets",

tests/test_client.py

@@ -0,0 +1,156 @@
+import json
+import os
+import scipy
+import websocket
+import copy
+import unittest
+from unittest.mock import patch, MagicMock
+from whisper_live.client import Client, TranscriptionClient, TranscriptionTeeClient
+from whisper_live.utils import resample
+from pathlib import Path
+
+
+class BaseTestCase(unittest.TestCase):
+    @patch('whisper_live.client.websocket.WebSocketApp')
+    @patch('whisper_live.client.pyaudio.PyAudio')
+    def setUp(self, mock_pyaudio, mock_websocket):
+        self.mock_pyaudio_instance = MagicMock()
+        mock_pyaudio.return_value = self.mock_pyaudio_instance
+        self.mock_stream = MagicMock()
+        self.mock_pyaudio_instance.open.return_value = self.mock_stream
+
+        self.mock_ws_app = mock_websocket.return_value
+        self.mock_ws_app.send = MagicMock()
+
+        self.client = TranscriptionClient(host='localhost', port=9090, lang="en").client
+
+        self.mock_pyaudio = mock_pyaudio
+        self.mock_websocket = mock_websocket
+        self.mock_audio_packet = b'\x00\x01\x02\x03'
+
+    def tearDown(self):
+        self.client.close_websocket()
+        self.mock_pyaudio.stop()
+        self.mock_websocket.stop()
+        del self.client
+
+class TestClientWebSocketCommunication(BaseTestCase):
+    def test_websocket_communication(self):
+        expected_url = 'ws://localhost:9090'
+        self.mock_websocket.assert_called()
+        self.assertEqual(self.mock_websocket.call_args[0][0], expected_url)
+
+
+class TestClientCallbacks(BaseTestCase):
+    def test_on_open(self):
+        expected_message = json.dumps({
+            "uid": self.client.uid,
+            "language": self.client.language,
+            "task": self.client.task,
+            "model": self.client.model,
+            "use_vad": True
+        })
+        self.client.on_open(self.mock_ws_app)
+        self.mock_ws_app.send.assert_called_with(expected_message)
+
+    def test_on_message(self):
+        message = json.dumps(
+            {
+                "uid": self.client.uid,
+                "message": "SERVER_READY",
+                "backend": "faster_whisper"
+            }
+        )
+        self.client.on_message(self.mock_ws_app, message)
+
+        message = json.dumps({
+            "uid": self.client.uid,
+            "segments": [
+                {"start": 0, "end": 1, "text": "Test transcript"},
+                {"start": 1, "end": 2, "text": "Test transcript 2"},
+                {"start": 2, "end": 3, "text": "Test transcript 3"}
+            ]
+        })
+        self.client.on_message(self.mock_ws_app, message)
+
+        # Assert that the transcript was updated correctly
+        self.assertEqual(len(self.client.transcript), 2)
+        self.assertEqual(self.client.transcript[1]['text'], "Test transcript 2")
+
+    def test_on_close(self):
+        close_status_code = 1000
+        close_msg = "Normal closure"
+        self.client.on_close(self.mock_ws_app, close_status_code, close_msg)
+
+        self.assertFalse(self.client.recording)
+        self.assertFalse(self.client.server_error)
+        self.assertFalse(self.client.waiting)
+
+    def test_on_error(self):
+        error_message = "Test Error"
+        self.client.on_error(self.mock_ws_app, error_message)
+
+        self.assertTrue(self.client.server_error)
+        self.assertEqual(self.client.error_message, error_message)
+
+
+class TestAudioResampling(unittest.TestCase):
+    def test_resample_audio(self):
+        original_audio = "assets/jfk.flac"
+        expected_sr = 16000
+        resampled_audio = resample(original_audio, expected_sr)
+
+        sr, _ = scipy.io.wavfile.read(resampled_audio)
+        self.assertEqual(sr, expected_sr)
+
+        os.remove(resampled_audio)
+
+
+class TestSendingAudioPacket(BaseTestCase):
+    def test_send_packet(self):
+        self.client.send_packet_to_server(self.mock_audio_packet)
+        self.client.client_socket.send.assert_called_with(self.mock_audio_packet, websocket.ABNF.OPCODE_BINARY)
+
+class TestTee(BaseTestCase):
+    @patch('whisper_live.client.websocket.WebSocketApp')
+    @patch('whisper_live.client.pyaudio.PyAudio')
+    def setUp(self, mock_audio, mock_websocket):
+        super().setUp()
+        self.client2 = Client(host='localhost', port=9090, lang="es", translate=False, srt_file_path="transcript.srt")
+        self.client3 = Client(host='localhost', port=9090, lang="es", translate=True, srt_file_path="translation.srt")
+        # need a separate mock for each websocket
+        self.client3.client_socket = copy.deepcopy(self.client3.client_socket)
+        self.tee = TranscriptionTeeClient([self.client2, self.client3])
+
+    def tearDown(self):
+        self.tee.close_all_clients()
+        del self.tee
+        super().tearDown()
+
+    def test_invalid_constructor(self):
+        with self.assertRaises(Exception) as context:
+            TranscriptionTeeClient([])
+
+    def test_multicast_unconditional(self):
+        self.tee.multicast_packet(self.mock_audio_packet, True)
+        for client in self.tee.clients:
+            client.client_socket.send.assert_called_with(self.mock_audio_packet, websocket.ABNF.OPCODE_BINARY)
+
+    def test_multicast_conditional(self):
+        self.client2.recording = False
+        self.client3.recording = True
+        self.tee.multicast_packet(self.mock_audio_packet, False)
+        self.client2.client_socket.send.assert_not_called()
+        self.client3.client_socket.send.assert_called_with(self.mock_audio_packet, websocket.ABNF.OPCODE_BINARY)
+
+    def test_close_all(self):
+        self.tee.close_all_clients()
+        for client in self.tee.clients:
+            client.client_socket.close.assert_called()
+
+    def test_write_all_srt(self):
+        for client in self.tee.clients:
+            client.server_backend = "faster_whisper"
+        self.tee.write_all_clients_srt()
+        self.assertTrue(Path("transcript.srt").is_file())
+        self.assertTrue(Path("translation.srt").is_file())

tests/test_server.py

@@ -0,0 +1,150 @@
+import subprocess
+import time
+import json
+import unittest
+from unittest import mock
+
+import numpy as np
+import evaluate
+
+from websockets.exceptions import ConnectionClosed
+from whisper_live.server import TranscriptionServer
+from whisper_live.client import Client, TranscriptionClient, TranscriptionTeeClient
+from whisper.normalizers import EnglishTextNormalizer
+
+
+class TestTranscriptionServerInitialization(unittest.TestCase):
+    def test_initialization(self):
+        server = TranscriptionServer()
+        self.assertEqual(server.client_manager.max_clients, 4)
+        self.assertEqual(server.client_manager.max_connection_time, 600)
+        self.assertDictEqual(server.client_manager.clients, {})
+        self.assertDictEqual(server.client_manager.start_times, {})
+
+
+class TestGetWaitTime(unittest.TestCase):
+    def setUp(self):
+        self.server = TranscriptionServer()
+        self.server.client_manager.start_times = {
+            'client1': time.time() - 120,
+            'client2': time.time() - 300
+        }
+        self.server.client_manager.max_connection_time = 600
+
+    def test_get_wait_time(self):
+        expected_wait_time = (600 - (time.time() - self.server.client_manager.start_times['client2'])) / 60
+        print(self.server.client_manager.get_wait_time(), expected_wait_time)
+        self.assertAlmostEqual(self.server.client_manager.get_wait_time(), expected_wait_time, places=2)
+
+
+class TestServerConnection(unittest.TestCase):
+    def setUp(self):
+        self.server = TranscriptionServer()
+
+    @mock.patch('websockets.WebSocketCommonProtocol')
+    def test_connection(self, mock_websocket):
+        mock_websocket.recv.return_value = json.dumps({
+            'uid': 'test_client',
+            'language': 'en',
+            'task': 'transcribe',
+            'model': 'tiny.en'
+        })
+        self.server.recv_audio(mock_websocket, "faster_whisper")
+
+    @mock.patch('websockets.WebSocketCommonProtocol')
+    def test_recv_audio_exception_handling(self, mock_websocket):
+        mock_websocket.recv.side_effect = [json.dumps({
+            'uid': 'test_client',
+            'language': 'en',
+            'task': 'transcribe',
+            'model': 'tiny.en'
+        }),  np.array([1, 2, 3]).tobytes()]
+
+        with self.assertLogs(level="ERROR"):
+            self.server.recv_audio(mock_websocket, "faster_whisper")
+
+        self.assertNotIn(mock_websocket, self.server.client_manager.clients)
+
+
+class TestServerInferenceAccuracy(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.mock_pyaudio_patch = mock.patch('pyaudio.PyAudio')
+        cls.mock_pyaudio = cls.mock_pyaudio_patch.start()
+        cls.mock_pyaudio.return_value.open.return_value = mock.MagicMock()
+        
+        cls.server_process = subprocess.Popen(["python", "run_server.py"])
+        time.sleep(2)
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.server_process.terminate()
+        cls.server_process.wait()
+
+    def setUp(self):
+        self.metric = evaluate.load("wer")
+        self.normalizer = EnglishTextNormalizer()
+
+    def check_prediction(self, srt_path):
+        gt = "And so my fellow Americans, ask not, what your country can do for you. Ask what you can do for your country!"
+        with open(srt_path, "r") as f:
+            lines = f.readlines()
+            prediction = " ".join([line.strip() for line in lines[2::4]])
+        prediction_normalized = self.normalizer(prediction)
+        gt_normalized = self.normalizer(gt)
+
+        # calculate WER
+        wer = self.metric.compute(
+            predictions=[prediction_normalized],
+            references=[gt_normalized]
+        )
+        self.assertLess(wer, 0.05)
+
+    def test_inference(self):
+        client = TranscriptionClient(
+            "localhost", "9090", model="base.en", lang="en",
+        )
+        client("assets/jfk.flac")
+        self.check_prediction("output.srt")
+
+    def test_simultaneous_inference(self):
+        client1 = Client(
+            "localhost", "9090", model="base.en", lang="en", srt_file_path="transcript1.srt")
+        client2 = Client(
+            "localhost", "9090", model="base.en", lang="en", srt_file_path="transcript2.srt")
+        tee = TranscriptionTeeClient([client1, client2])
+        tee("assets/jfk.flac")
+        self.check_prediction("transcript1.srt")
+        self.check_prediction("transcript2.srt")
+
+
+class TestExceptionHandling(unittest.TestCase):
+    def setUp(self):
+        self.server = TranscriptionServer()
+
+    @mock.patch('websockets.WebSocketCommonProtocol')
+    def test_connection_closed_exception(self, mock_websocket):
+        mock_websocket.recv.side_effect = ConnectionClosed(1001, "testing connection closed")
+
+        with self.assertLogs(level="INFO") as log:
+            self.server.recv_audio(mock_websocket, "faster_whisper")
+            self.assertTrue(any("Connection closed by client" in message for message in log.output))
+
+    @mock.patch('websockets.WebSocketCommonProtocol')
+    def test_json_decode_exception(self, mock_websocket):
+        mock_websocket.recv.return_value = "invalid json"
+
+        with self.assertLogs(level="ERROR") as log:
+            self.server.recv_audio(mock_websocket, "faster_whisper")
+            self.assertTrue(any("Failed to decode JSON from client" in message for message in log.output))
+
+    @mock.patch('websockets.WebSocketCommonProtocol')
+    def test_unexpected_exception_handling(self, mock_websocket):
+        mock_websocket.recv.side_effect = RuntimeError("Unexpected error")
+
+        with self.assertLogs(level="ERROR") as log:
+            self.server.recv_audio(mock_websocket, "faster_whisper")
+            for message in log.output:
+                print(message)
+            print()
+            self.assertTrue(any("Unexpected error" in message for message in log.output))

tests/test_vad.py

@@ -0,0 +1,26 @@
+import unittest
+import numpy as np
+from whisper_live.tensorrt_utils import load_audio
+from whisper_live.vad import VoiceActivityDetector
+
+
+class TestVoiceActivityDetection(unittest.TestCase):
+    def setUp(self):
+        self.vad = VoiceActivityDetector()
+        self.sample_rate = 16000
+
+    def generate_silence(self, duration_seconds):
+        return np.zeros(int(self.sample_rate * duration_seconds), dtype=np.float32)
+
+    def load_speech_segment(self, filepath):
+        return load_audio(filepath)
+
+    def test_vad_silence_detection(self):
+        silence = self.generate_silence(3)
+        is_speech_present = self.vad(silence.copy())
+        self.assertFalse(is_speech_present, "VAD incorrectly identified silence as speech.")
+
+    def test_vad_speech_detection(self):
+        audio_tensor = load_audio("assets/jfk.flac")
+        is_speech_present = self.vad(audio_tensor)
+        self.assertTrue(is_speech_present, "VAD failed to identify speech segment.")

whisper_live/__version__.py

@@ -1 +1 @@
-__version__="0.1.0"
+__version__ = "0.5.0"

whisper_live/client.py

@@ -1,76 +1,24 @@
 import os
+import shutil
 import wave
 
 import numpy as np
-import scipy
-import ffmpeg
 import pyaudio
 import threading
-import textwrap
 import json
 import websocket
 import uuid
 import time
-
+import ffmpeg
-
+import whisper_live.utils as utils
-def format_time(s):
-    """Convert seconds (float) to SRT time format."""
-    hours = int(s // 3600)
-    minutes = int((s % 3600) // 60)
-    seconds = int(s % 60)
-    milliseconds = int((s - int(s)) * 1000)
-    return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
-
-def create_srt_file(segments, output_file):
-    with open(output_file, 'w', encoding='utf-8') as srt_file:
-        segment_number = 1
-        for segment in segments:
-            start_time = format_time(float(segment['start']))
-            end_time = format_time(float(segment['end']))
-            text = segment['text']
-            
-            srt_file.write(f"{segment_number}\n")
-            srt_file.write(f"{start_time} --> {end_time}\n")
-            srt_file.write(f"{text}\n\n")
-            
-            segment_number += 1
-
-
-def resample(file: str, sr: int = 16000):
-    """
-    # https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/audio.py#L22
-    Open an audio file and read as mono waveform, resampling as necessary,
-    save the resampled audio
-
-    Args:
-        file (str): The audio file to open
-        sr (int): The sample rate to resample the audio if necessary
-    
-    Returns:
-        resampled_file (str): The resampled audio file
-    """
-    try:
-        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-        out, _ = (
-            ffmpeg.input(file, threads=0)
-            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
-            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
-        )
-    except ffmpeg.Error as e:
-        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-    np_buffer = np.frombuffer(out, dtype=np.int16)
-
-    resampled_file = f"{file.split('.')[0]}_resampled.wav"
-    scipy.io.wavfile.write(resampled_file, sr, np_buffer.astype(np.int16))
-    return resampled_file
 
 
 class Client:
     """
-    Handles audio recording, streaming, and communication with a server using WebSocket.
+    Handles communication with a server using WebSocket.
     """
     INSTANCES = {}
+    END_OF_AUDIO = "END_OF_AUDIO"
 
     def __init__(
         self,
@@ -79,7 +27,8 @@ class Client:
         lang=None,
         translate=False,
         model="small",
-        srt_file_path="output.srt"
+        srt_file_path="output.srt",
+        use_vad=True
     ):
         """
         Initializes a Client instance for audio recording and streaming to a server.
@@ -94,35 +43,25 @@ class Client:
             lang (str, optional): The selected language for transcription. Default is None.
             translate (bool, optional): Specifies if the task is translation. Default is False.
         """
-        self.chunk = 4096
-        self.format = pyaudio.paInt16
-        self.channels = 1
-        self.rate = 16000
-        self.record_seconds = 60000
         self.recording = False
         self.task = "transcribe"
         self.uid = str(uuid.uuid4())
         self.waiting = False
-        self.last_response_recieved = None
+        self.last_response_received = None
         self.disconnect_if_no_response_for = 15
         self.language = lang
         self.model = model
         self.server_error = False
         self.srt_file_path = srt_file_path
+        self.use_vad = use_vad
+        self.last_segment = None
+        self.last_received_segment = None
 
         if translate:
             self.task = "translate"
 
         self.timestamp_offset = 0.0
         self.audio_bytes = None
-        self.p = pyaudio.PyAudio()
-        self.stream = self.p.open(
-            format=self.format,
-            channels=self.channels,
-            rate=self.rate,
-            input=True,
-            frames_per_buffer=self.chunk,
-        )
 
         if host is not None and port is not None:
             socket_url = f"ws://{host}:{port}"
@@ -146,10 +85,43 @@ class Client:
         self.ws_thread.setDaemon(True)
         self.ws_thread.start()
 
-        self.frames = b""
         self.transcript = []
         print("[INFO]: * recording")
 
+    def handle_status_messages(self, message_data):
+        """Handles server status messages."""
+        status = message_data["status"]
+        if status == "WAIT":
+            self.waiting = True
+            print(f"[INFO]: Server is full. Estimated wait time {round(message_data['message'])} minutes.")
+        elif status == "ERROR":
+            print(f"Message from Server: {message_data['message']}")
+            self.server_error = True
+        elif status == "WARNING":
+            print(f"Message from Server: {message_data['message']}")
+
+    def process_segments(self, segments):
+        """Processes transcript segments."""
+        text = []
+        for i, seg in enumerate(segments):
+            if not text or text[-1] != seg["text"]:
+                text.append(seg["text"])
+                if i == len(segments) - 1:
+                    self.last_segment = seg
+                elif (self.server_backend == "faster_whisper" and
+                      (not self.transcript or
+                        float(seg['start']) >= float(self.transcript[-1]['end']))):
+                    self.transcript.append(seg)
+        # update last received segment and last valid response time
+        if self.last_received_segment is None or self.last_received_segment != segments[-1]["text"]:
+            self.last_response_received = time.time()
+            self.last_received_segment = segments[-1]["text"]
+
+        # Truncate to last 3 entries for brevity.
+        text = text[-3:]
+        utils.clear_screen()
+        utils.print_transcript(text)
+
     def on_message(self, ws, message):
         """
         Callback function called when a message is received from the server.
@@ -163,7 +135,6 @@ class Client:
             message (str): The received message from the server.
 
         """
-        self.last_response_recieved = time.time()
         message = json.loads(message)
 
         if self.uid != message.get("uid"):
@@ -171,21 +142,15 @@ class Client:
             return
 
         if "status" in message.keys():
-            if message["status"] == "WAIT":
+            self.handle_status_messages(message)
-                self.waiting = True
-                print(
-                    f"[INFO]:Server is full. Estimated wait time {round(message['message'])} minutes."
-                )
-            elif message["status"] == "ERROR":
-                print(f"Message from Server: {message['message']}")
-                self.server_error = True
             return
 
         if "message" in message.keys() and message["message"] == "DISCONNECT":
-            print("[INFO]: Server overtime disconnected.")
+            print("[INFO]: Server disconnected due to overtime.")
             self.recording = False
 
         if "message" in message.keys() and message["message"] == "SERVER_READY":
+            self.last_response_received = time.time()
             self.recording = True
             self.server_backend = message["backend"]
             print(f"[INFO]: Server Running with backend {self.server_backend}")
@@ -199,44 +164,18 @@ class Client:
             )
             return
 
-        if "segments" not in message.keys():
+        if "segments" in message.keys():
-            return
+            self.process_segments(message["segments"])
-
-        message = message["segments"]
-        text = []
-        n_segments = len(message)
-
-        if n_segments:
-            for i, seg in enumerate(message):
-                if text and text[-1] == seg["text"]:
-                    # already got it
-                    continue
-                text.append(seg["text"])
-
-                if i == n_segments-1: 
-                    self.last_segment = seg
-                elif self.server_backend == "faster_whisper":
-                    if not len(self.transcript) or float(seg['start']) >= float(self.transcript[-1]['end']):
-                        self.transcript.append(seg)
-
-        # keep only last 3
-        if len(text) > 3:
-            text = text[-3:]
-        wrapper = textwrap.TextWrapper(width=60)
-        word_list = wrapper.wrap(text="".join(text))
-        # Print each line.
-        if os.name == "nt":
-            os.system("cls")
-        else:
-            os.system("clear")
-        for element in word_list:
-            print(element)
 
     def on_error(self, ws, error):
-        print(error)
+        print(f"[ERROR] WebSocket Error: {error}")
+        self.server_error = True
+        self.error_message = error
 
     def on_close(self, ws, close_status_code, close_msg):
         print(f"[INFO]: Websocket connection closed: {close_status_code}: {close_msg}")
+        self.recording = False
+        self.waiting = False
 
     def on_open(self, ws):
         """
@@ -257,27 +196,11 @@ class Client:
                     "language": self.language,
                     "task": self.task,
                     "model": self.model,
+                    "use_vad": self.use_vad
                 }
             )
         )
 
-    @staticmethod
-    def bytes_to_float_array(audio_bytes):
-        """
-        Convert audio data from bytes to a NumPy float array.
-        
-        It assumes that the audio data is in 16-bit PCM format. The audio data is normalized to 
-        have values between -1 and 1.
-
-        Args:
-            audio_bytes (bytes): Audio data in bytes.
-
-        Returns:
-            np.ndarray: A NumPy array containing the audio data as float values normalized between -1 and 1.
-        """
-        raw_data = np.frombuffer(buffer=audio_bytes, dtype=np.int16)
-        return raw_data.astype(np.float32) / 32768.0
-
     def send_packet_to_server(self, message):
         """
         Send an audio packet to the server using WebSocket.
@@ -291,62 +214,6 @@ class Client:
         except Exception as e:
             print(e)
 
-    def play_file(self, filename):
-        """
-        Play an audio file and send it to the server for processing.
-        
-        Reads an audio file, plays it through the audio output, and simultaneously sends
-        the audio data to the server for processing. It uses PyAudio to create an audio 
-        stream for playback. The audio data is read from the file in chunks, converted to 
-        floating-point format, and sent to the server using WebSocket communication.
-        This method is typically used when you want to process pre-recorded audio and send it
-        to the server in real-time.
-
-        Args:
-            filename (str): The path to the audio file to be played and sent to the server.
-        """
-        
-        # read audio and create pyaudio stream
-        with wave.open(filename, "rb") as wavfile:
-            self.stream = self.p.open(
-                format=self.p.get_format_from_width(wavfile.getsampwidth()),
-                channels=wavfile.getnchannels(),
-                rate=wavfile.getframerate(),
-                input=True,
-                output=True,
-                frames_per_buffer=self.chunk,
-            )
-            try:
-                while self.recording:
-                    data = wavfile.readframes(self.chunk)
-                    if data == b"":
-                        break
-
-                    audio_array = self.bytes_to_float_array(data)
-                    self.send_packet_to_server(audio_array.tobytes())
-                    self.stream.write(data)
-
-                wavfile.close()
-
-                assert self.last_response_recieved
-                while time.time() - self.last_response_recieved < self.disconnect_if_no_response_for:
-                    continue
-
-                if self.server_backend == "faster_whisper":
-                    self.write_srt_file(self.srt_file_path)
-                self.stream.close()
-                self.close_websocket()
-
-            except KeyboardInterrupt:
-                wavfile.close()
-                self.stream.stop_stream()
-                self.stream.close()
-                self.p.terminate()
-                self.close_websocket()
-                if self.server_backend == "faster_whisper":
-                    self.write_srt_file(self.srt_file_path)
-                print("[INFO]: Keyboard interrupt.")
-
     def close_websocket(self):
         """
         Close the WebSocket connection and join the WebSocket thread.
@@ -374,6 +241,318 @@ class Client:
         """
         return self.client_socket
 
+    def write_srt_file(self, output_path="output.srt"):
+        """
+        Writes out the transcript in .srt format.
+
+        Args:
+            message (output_path, optional): The path to the target file.  Default is "output.srt".
+
+        """
+        if self.server_backend == "faster_whisper":
+            if (self.last_segment):
+                self.transcript.append(self.last_segment)
+            utils.create_srt_file(self.transcript, output_path)
+
+    def wait_before_disconnect(self):
+        """Waits a bit before disconnecting in order to process pending responses."""
+        assert self.last_response_received
+        while time.time() - self.last_response_received < self.disconnect_if_no_response_for:
+            continue
+
+
+class TranscriptionTeeClient:
+    """
+    Client for handling audio recording, streaming, and transcription tasks via one or more
+    WebSocket connections.
+
+    Acts as a high-level client for audio transcription tasks using a WebSocket connection. It can be used
+    to send audio data for transcription to one or more servers, and receive transcribed text segments.
+    Args:
+        clients (list): one or more previously initialized Client instances
+
+    Attributes:
+        clients (list): the underlying Client instances responsible for handling WebSocket connections.
+    """
+    def __init__(self, clients, save_output_recording=False, output_recording_filename="./output_recording.wav"):
+        self.clients = clients
+        if not self.clients:
+            raise Exception("At least one client is required.")
+        self.chunk = 4096
+        self.format = pyaudio.paInt16
+        self.channels = 1
+        self.rate = 16000
+        self.record_seconds = 60000
+        self.save_output_recording = save_output_recording
+        self.output_recording_filename = output_recording_filename
+        self.frames = b""
+        self.p = pyaudio.PyAudio()
+        try:
+            self.stream = self.p.open(
+                format=self.format,
+                channels=self.channels,
+                rate=self.rate,
+                input=True,
+                frames_per_buffer=self.chunk,
+            )
+        except OSError as error:
+            print(f"[WARN]: Unable to access microphone. {error}")
+            self.stream = None
+
+    def __call__(self, audio=None, rtsp_url=None, hls_url=None, save_file=None):
+        """
+        Start the transcription process.
+
+        Initiates the transcription process by connecting to the server via a WebSocket. It waits for the server
+        to be ready to receive audio data and then sends audio for transcription. If an audio file is provided, it
+        will be played and streamed to the server; otherwise, it will perform live recording.
+
+        Args:
+            audio (str, optional): Path to an audio file for transcription. Default is None, which triggers live recording.
+
+        """
+        assert sum(
+            source is not None for source in [audio, rtsp_url, hls_url]
+        ) <= 1, 'You must provide only one selected source'
+
+        print("[INFO]: Waiting for server ready ...")
+        for client in self.clients:
+            while not client.recording:
+                if client.waiting or client.server_error:
+                    self.close_all_clients()
+                    return
+
+        print("[INFO]: Server Ready!")
+        if hls_url is not None:
+            self.process_hls_stream(hls_url, save_file)
+        elif audio is not None:
+            resampled_file = utils.resample(audio)
+            self.play_file(resampled_file)
+        elif rtsp_url is not None:
+            self.process_rtsp_stream(rtsp_url)
+        else:
+            self.record()
+
+    def close_all_clients(self):
+        """Closes all client websockets."""
+        for client in self.clients:
+            client.close_websocket()
+
+    def write_all_clients_srt(self):
+        """Writes out .srt files for all clients."""
+        for client in self.clients:
+            client.write_srt_file(client.srt_file_path)
+
+    def multicast_packet(self, packet, unconditional=False):
+        """
+        Sends an identical packet via all clients.
+
+        Args:
+            packet (bytes): The audio data packet in bytes to be sent.
+            unconditional (bool, optional): If true, send regardless of whether clients are recording.  Default is False.
+        """
+        for client in self.clients:
+            if (unconditional or client.recording):
+                client.send_packet_to_server(packet)
+
+    def play_file(self, filename):
+        """
+        Play an audio file and send it to the server for processing.
+
+        Reads an audio file, plays it through the audio output, and simultaneously sends
+        the audio data to the server for processing. It uses PyAudio to create an audio
+        stream for playback. The audio data is read from the file in chunks, converted to
+        floating-point format, and sent to the server using WebSocket communication.
+        This method is typically used when you want to process pre-recorded audio and send it
+        to the server in real-time.
+
+        Args:
+            filename (str): The path to the audio file to be played and sent to the server.
+        """
+
+        # read audio and create pyaudio stream
+        with wave.open(filename, "rb") as wavfile:
+            self.stream = self.p.open(
+                format=self.p.get_format_from_width(wavfile.getsampwidth()),
+                channels=wavfile.getnchannels(),
+                rate=wavfile.getframerate(),
+                input=True,
+                output=True,
+                frames_per_buffer=self.chunk,
+            )
+            try:
+                while any(client.recording for client in self.clients):
+                    data = wavfile.readframes(self.chunk)
+                    if data == b"":
+                        break
+
+                    audio_array = self.bytes_to_float_array(data)
+                    self.multicast_packet(audio_array.tobytes())
+                    self.stream.write(data)
+
+                wavfile.close()
+
+                for client in self.clients:
+                    client.wait_before_disconnect()
+                self.multicast_packet(Client.END_OF_AUDIO.encode('utf-8'), True)
+                self.write_all_clients_srt()
+                self.stream.close()
+                self.close_all_clients()
+
+            except KeyboardInterrupt:
+                wavfile.close()
+                self.stream.stop_stream()
+                self.stream.close()
+                self.p.terminate()
+                self.close_all_clients()
+                self.write_all_clients_srt()
+                print("[INFO]: Keyboard interrupt.")
+
+    def process_rtsp_stream(self, rtsp_url):
+        """
+        Connect to an RTSP source, process the audio stream, and send it for trascription.
+
+        Args:
+            rtsp_url (str): The URL of the RTSP stream source.
+        """
+        process = self.get_rtsp_ffmpeg_process(rtsp_url)
+        self.handle_ffmpeg_process(process, stream_type='RTSP')
+
+    def process_hls_stream(self, hls_url, save_file):
+        """
+        Connect to an HLS source, process the audio stream, and send it for transcription.
+
+        Args:
+            hls_url (str): The URL of the HLS stream source.
+            save_file （str, optional): Local path to save the network stream.
+        """
+        process = self.get_hls_ffmpeg_process(hls_url, save_file)
+        self.handle_ffmpeg_process(process, stream_type='HLS')
+
+    def handle_ffmpeg_process(self, process, stream_type):
+        print(f"[INFO]: Connecting to {stream_type} stream...")
+        try:
+            # Process the stream
+            while True:
+                in_bytes = process.stdout.read(self.chunk * 2)  # 2 bytes per sample
+                if not in_bytes:
+                    break
+                audio_array = self.bytes_to_float_array(in_bytes)
+                self.multicast_packet(audio_array.tobytes())
+
+        except Exception as e:
+            print(f"[ERROR]: Failed to connect to {stream_type} stream: {e}")
+        finally:
+            self.close_all_clients()
+            self.write_all_clients_srt()
+            if process:
+                process.kill()
+
+        print(f"[INFO]: {stream_type} stream processing finished.")
+
+    def get_rtsp_ffmpeg_process(self, rtsp_url):
+        return (
+            ffmpeg
+            .input(rtsp_url, threads=0)
+            .output('-', format='s16le', acodec='pcm_s16le', ac=1, ar=self.rate)
+            .run_async(pipe_stdout=True, pipe_stderr=True)
+        )
+
+    def get_hls_ffmpeg_process(self, hls_url, save_file):
+        if save_file is None:
+            process = (
+                ffmpeg
+                .input(hls_url, threads=0)
+                .output('-', format='s16le', acodec='pcm_s16le', ac=1, ar=self.rate)
+                .run_async(pipe_stdout=True, pipe_stderr=True)
+            )
+        else:
+            input = ffmpeg.input(hls_url, threads=0)
+            output_file = input.output(save_file, acodec='copy', vcodec='copy').global_args('-loglevel', 'quiet')
+            output_std = input.output('-', format='s16le', acodec='pcm_s16le', ac=1, ar=self.rate)
+            process = (
+                ffmpeg.merge_outputs(output_file, output_std)
+                .run_async(pipe_stdout=True, pipe_stderr=True)
+            )
+
+        return process
+
+    def save_chunk(self, n_audio_file):
+        """
+        Saves the current audio frames to a WAV file in a separate thread.
+
+        Args:
+        n_audio_file (int): The index of the audio file which determines the filename.
+                            This helps in maintaining the order and uniqueness of each chunk.
+        """
+        t = threading.Thread(
+            target=self.write_audio_frames_to_file,
+            args=(self.frames[:], f"chunks/{n_audio_file}.wav",),
+        )
+        t.start()
+
+    def finalize_recording(self, n_audio_file):
+        """
+        Finalizes the recording process by saving any remaining audio frames,
+        closing the audio stream, and terminating the process.
+
+        Args:
+        n_audio_file (int): The file index to be used if there are remaining audio frames to be saved.
+                            This index is incremented before use if the last chunk is saved.
+        """
+        if self.save_output_recording and len(self.frames):
+            self.write_audio_frames_to_file(
+                self.frames[:], f"chunks/{n_audio_file}.wav"
+            )
+            n_audio_file += 1
+        self.stream.stop_stream()
+        self.stream.close()
+        self.p.terminate()
+        self.close_all_clients()
+        if self.save_output_recording:
+            self.write_output_recording(n_audio_file)
+        self.write_all_clients_srt()
+
+    def record(self):
+        """
+        Record audio data from the input stream and save it to a WAV file.
+
+        Continuously records audio data from the input stream, sends it to the server via a WebSocket
+        connection, and simultaneously saves it to multiple WAV files in chunks. It stops recording when
+        the `RECORD_SECONDS` duration is reached or when the `RECORDING` flag is set to `False`.
+
+        Audio data is saved in chunks to the "chunks" directory. Each chunk is saved as a separate WAV file.
+        The recording will continue until the specified duration is reached or until the `RECORDING` flag is set to `False`.
+        The recording process can be interrupted by sending a KeyboardInterrupt (e.g., pressing Ctrl+C). After recording,
+        the method combines all the saved audio chunks into the specified `out_file`.
+        """
+        n_audio_file = 0
+        if self.save_output_recording:
+            if os.path.exists("chunks"):
+                shutil.rmtree("chunks")
+            os.makedirs("chunks")
+        try:
+            for _ in range(0, int(self.rate / self.chunk * self.record_seconds)):
+                if not any(client.recording for client in self.clients):
+                    break
+                data = self.stream.read(self.chunk, exception_on_overflow=False)
+                self.frames += data
+
+                audio_array = self.bytes_to_float_array(data)
+
+                self.multicast_packet(audio_array.tobytes())
+
+                # save frames if more than a minute
+                if len(self.frames) > 60 * self.rate:
+                    if self.save_output_recording:
+                        self.save_chunk(n_audio_file)
+                        n_audio_file += 1
+                    self.frames = b""
+            self.write_all_clients_srt()
+
+        except KeyboardInterrupt:
+            self.finalize_recording(n_audio_file)
+
     def write_audio_frames_to_file(self, frames, file_name):
         """
         Write audio frames to a WAV file.
@@ -393,104 +572,7 @@ class Client:
             wavfile.setframerate(self.rate)
             wavfile.writeframes(frames)
 
-    def process_hls_stream(self, hls_url):
+    def write_output_recording(self, n_audio_file):
-        """
-        Connect to an HLS source, process the audio stream, and send it for transcription.
-
-        Args:
-            hls_url (str): The URL of the HLS stream source.
-        """
-        print("[INFO]: Connecting to HLS stream...")
-        process = None  # Initialize process to None
-
-        try:
-            # Connecting to the HLS stream using ffmpeg-python
-            process = (
-                ffmpeg
-                .input(hls_url, threads=0)
-                .output('-', format='s16le', acodec='pcm_s16le', ac=1, ar=self.rate)
-                .run_async(pipe_stdout=True, pipe_stderr=True)
-            )
-
-            # Process the stream
-            while True:
-                in_bytes = process.stdout.read(self.chunk * 2)  # 2 bytes per sample
-                if not in_bytes:
-                    break
-                audio_array = self.bytes_to_float_array(in_bytes)
-                self.send_packet_to_server(audio_array.tobytes())
-
-        except Exception as e:
-            print(f"[ERROR]: Failed to connect to HLS stream: {e}")
-        finally:
-            if process:
-                process.kill()
-
-        print("[INFO]: HLS stream processing finished.")
-
-
-    def record(self, out_file="output_recording.wav"):
-        """
-        Record audio data from the input stream and save it to a WAV file.
-
-        Continuously records audio data from the input stream, sends it to the server via a WebSocket
-        connection, and simultaneously saves it to multiple WAV files in chunks. It stops recording when
-        the `RECORD_SECONDS` duration is reached or when the `RECORDING` flag is set to `False`.
-
-        Audio data is saved in chunks to the "chunks" directory. Each chunk is saved as a separate WAV file.
-        The recording will continue until the specified duration is reached or until the `RECORDING` flag is set to `False`.
-        The recording process can be interrupted by sending a KeyboardInterrupt (e.g., pressing Ctrl+C). After recording, 
-        the method combines all the saved audio chunks into the specified `out_file`.
-
-        Args:
-            out_file (str, optional): The name of the output WAV file to save the entire recording. Default is "output_recording.wav".
-
-        """
-        n_audio_file = 0
-        if not os.path.exists("chunks"):
-            os.makedirs("chunks", exist_ok=True)
-        try:
-            for _ in range(0, int(self.rate / self.chunk * self.record_seconds)):
-                if not self.recording:
-                    break
-                data = self.stream.read(self.chunk, exception_on_overflow = False)
-                self.frames += data
-
-                audio_array = Client.bytes_to_float_array(data)
-
-                self.send_packet_to_server(audio_array.tobytes())
-
-                # save frames if more than a minute
-                if len(self.frames) > 60 * self.rate:
-                    t = threading.Thread(
-                        target=self.write_audio_frames_to_file,
-                        args=(
-                            self.frames[:],
-                            f"chunks/{n_audio_file}.wav",
-                        ),
-                    )
-                    t.start()
-                    n_audio_file += 1
-                    self.frames = b""
-            if self.server_backend == "faster_whisper":
-                self.write_srt_file(self.srt_file_path)
-
-        except KeyboardInterrupt:
-            if len(self.frames):
-                self.write_audio_frames_to_file(
-                    self.frames[:], f"chunks/{n_audio_file}.wav"
-                )
-                n_audio_file += 1
-            self.stream.stop_stream()
-            self.stream.close()
-            self.p.terminate()
-            self.close_websocket()
-
-            self.write_output_recording(n_audio_file, out_file)
-            if self.server_backend == "faster_whisper":
-                self.write_srt_file(self.srt_file_path)
-
-    def write_output_recording(self, n_audio_file, out_file):
         """
         Combine and save recorded audio chunks into a single WAV file.
 
@@ -509,7 +591,7 @@ class Client:
             for i in range(n_audio_file)
             if os.path.exists(f"chunks/{i}.wav")
         ]
-        with wave.open(out_file, "wb") as wavfile:
+        with wave.open(self.output_recording_filename, "wb") as wavfile:
             wavfile: wave.Wave_write
             wavfile.setnchannels(self.channels)
             wavfile.setsampwidth(2)
@@ -524,15 +606,31 @@ class Client:
                 # remove this file
                 os.remove(in_file)
         wavfile.close()
+        # clean up temporary directory to store chunks
+        if os.path.exists("chunks"):
+            shutil.rmtree("chunks")
 
-    def write_srt_file(self, output_path="output.srt"):
+    @staticmethod
-        self.transcript.append(self.last_segment)
+    def bytes_to_float_array(audio_bytes):
-        create_srt_file(self.transcript, output_path)
-
-
-class TranscriptionClient:
         """
-    Client for handling audio transcription tasks via a WebSocket connection.
+        Convert audio data from bytes to a NumPy float array.
+
+        It assumes that the audio data is in 16-bit PCM format. The audio data is normalized to
+        have values between -1 and 1.
+
+        Args:
+            audio_bytes (bytes): Audio data in bytes.
+
+        Returns:
+            np.ndarray: A NumPy array containing the audio data as float values normalized between -1 and 1.
+        """
+        raw_data = np.frombuffer(buffer=audio_bytes, dtype=np.int16)
+        return raw_data.astype(np.float32) / 32768.0
+
+
+class TranscriptionClient(TranscriptionTeeClient):
+    """
+    Client for handling audio transcription tasks via a single WebSocket connection.
 
     Acts as a high-level client for audio transcription tasks using a WebSocket connection. It can be used
     to send audio data for transcription to a server and receive transcribed text segments.
@@ -542,6 +640,9 @@ class TranscriptionClient:
         port (int): The port number to connect to on the server.
         lang (str, optional): The primary language for transcription. Default is None, which defaults to English ('en').
         translate (bool, optional): Indicates whether translation tasks are required (default is False).
+        save_output_recording (bool, optional): Indicates whether to save recording from microphone.
+        output_recording_filename (str, optional): File to save the output recording.
+        output_transcription_path (str, optional): File to save the output transcription.
 
     Attributes:
         client (Client): An instance of the underlying Client class responsible for handling the WebSocket connection.
@@ -553,38 +654,26 @@ class TranscriptionClient:
         transcription_client()
         ```
     """
-    def __init__(self,
+    def __init__(
+        self,
         host,
         port,
         lang=None,
         translate=False,
         model="small",
+        use_vad=True,
+        save_output_recording=False,
+        output_recording_filename="./output_recording.wav",
+        output_transcription_path="./output.srt"
     ):
-        self.client = Client(host, port, lang, translate, model)
+        self.client = Client(host, port, lang, translate, model, srt_file_path=output_transcription_path, use_vad=use_vad)
-
+        if save_output_recording and not output_recording_filename.endswith(".wav"):
-    def __call__(self, audio=None, hls_url=None):
+            raise ValueError(f"Please provide a valid `output_recording_filename`: {output_recording_filename}")
-        """
+        if not output_transcription_path.endswith(".srt"):
-        Start the transcription process.
+            raise ValueError(f"Please provide a valid `output_transcription_path`: {output_transcription_path}. The file extension should be `.srt`.")
-
+        TranscriptionTeeClient.__init__(
-        Initiates the transcription process by connecting to the server via a WebSocket. It waits for the server
+            self,
-        to be ready to receive audio data and then sends audio for transcription. If an audio file is provided, it 
+            [self.client],
-        will be played and streamed to the server; otherwise, it will perform live recording.
+            save_output_recording=save_output_recording,
-
+            output_recording_filename=output_recording_filename
-        Args:
+        )
-            audio (str, optional): Path to an audio file for transcription. Default is None, which triggers live recording.
-                   
-        """
-        print("[INFO]: Waiting for server ready ...")
-        while not self.client.recording:
-            if self.client.waiting or self.client.server_error:
-                self.client.close_websocket()
-                return
-
-        print("[INFO]: Server Ready!")
-        if hls_url is not None:
-            self.client.process_hls_stream(hls_url)
-        elif audio is not None:
-            resampled_file = resample(audio)
-            self.client.play_file(resampled_file)
-        else:
-            self.client.record()

whisper_live/tensorrt_utils.py

@@ -214,7 +214,7 @@ def store_transcripts(filename: Pathlike, texts: Iterable[Tuple[str, str,
             print(f"{cut_id}:\thyp={hyp}", file=f)
 
 
-def write_error_stats(
+def write_error_stats(                                              # noqa: C901
     f: TextIO,
     test_set_name: str,
     results: List[Tuple[str, str]],

whisper_live/transcriber.py

@@ -1,22 +1,22 @@
 # original https://github.com/guillaumekln/faster-whisper/blob/master/faster_whisper/transcribe.py
 
 import itertools
+import json
 import logging
 import os
 import zlib
-import json
-from inspect import signature
 
+from inspect import signature
 from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union
 
 import ctranslate2
 import numpy as np
 import tokenizers
 
-from faster_whisper.audio import decode_audio
+from faster_whisper.audio import decode_audio, pad_or_trim
 from faster_whisper.feature_extractor import FeatureExtractor
 from faster_whisper.tokenizer import _LANGUAGE_CODES, Tokenizer
-from faster_whisper.utils import download_model, format_timestamp, get_logger
+from faster_whisper.utils import download_model, format_timestamp, get_end, get_logger
 from faster_whisper.vad import (
     SpeechTimestampsMap,
     VadOptions,
@@ -68,6 +68,9 @@ class TranscriptionOptions(NamedTuple):
     word_timestamps: bool
     prepend_punctuations: str
     append_punctuations: str
+    max_new_tokens: Optional[int]
+    clip_timestamps: Union[str, List[float]]
+    hallucination_silence_threshold: Optional[float]
 
 
 class TranscriptionInfo(NamedTuple):
@@ -96,8 +99,8 @@ class WhisperModel:
 
         Args:
           model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en,
-            small, small.en, medium, medium.en, large-v1, large-v2, large-v3, or large), a path to a converted
+            small, small.en, medium, medium.en, large-v1, large-v2, large-v3, or large), a path to a
-            model directory, or a CTranslate2-converted Whisper model ID from the Hugging Face Hub.
+            converted model directory, or a CTranslate2-converted Whisper model ID from the HF Hub.
             When a size or a model ID is configured, the converted model is downloaded
             from the Hugging Face Hub.
           device: Device to use for computation ("cpu", "cuda", "auto").
@@ -180,7 +183,7 @@ class WhisperModel:
 
         return config
 
-    def transcribe(
+    def transcribe(                                                         # noqa: C901
         self,
         audio: Union[str, BinaryIO, np.ndarray],
         language: Optional[str] = None,
@@ -215,6 +218,10 @@ class WhisperModel:
         append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
         vad_filter: bool = False,
         vad_parameters: Optional[Union[dict, VadOptions]] = None,
+        max_new_tokens: Optional[int] = None,
+        chunk_length: Optional[int] = None,
+        clip_timestamps: Union[str, List[float]] = "0",
+        hallucination_silence_threshold: Optional[float] = None,
     ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
         """Transcribes an input file.
 
@@ -266,6 +273,16 @@ class WhisperModel:
             https://github.com/snakers4/silero-vad.
           vad_parameters: Dictionary of Silero VAD parameters or VadOptions class (see available
             parameters and default values in the class `VadOptions`).
+          max_new_tokens: Maximum number of new tokens to generate per-chunk. If not set,
+            the maximum will be set by the default max_length.
+          chunk_length: The length of audio segments. If it is not None, it will overwrite the
+            default chunk_length of the FeatureExtractor.
+          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 tuple with:
@@ -315,7 +332,10 @@ class WhisperModel:
         else:
             speech_chunks = None
 
-        features = self.feature_extractor(audio)
+        if audio.shape[0] == 0:
+            return None, None
+
+        features = self.feature_extractor(audio, chunk_length=chunk_length)
 
         encoder_output = None
         all_language_probs = None
@@ -381,6 +401,9 @@ class WhisperModel:
             word_timestamps=word_timestamps,
             prepend_punctuations=prepend_punctuations,
             append_punctuations=append_punctuations,
+            max_new_tokens=max_new_tokens,
+            clip_timestamps=clip_timestamps,
+            hallucination_silence_threshold=hallucination_silence_threshold,
         )
 
         segments = self.generate_segments(features, tokenizer, options, encoder_output)
@@ -408,8 +431,33 @@ class WhisperModel:
         encoder_output: Optional[ctranslate2.StorageView] = None,
     ) -> Iterable[Segment]:
         content_frames = features.shape[-1] - self.feature_extractor.nb_max_frames
+        content_duration = float(content_frames * self.feature_extractor.time_per_frame)
+
+        if isinstance(options.clip_timestamps, str):
+            TranscriptionOptions.clip_timestamps = [
+                float(ts)
+                for ts in (
+                    options.clip_timestamps.split(",")
+                    if options.clip_timestamps
+                    else []
+                )
+            ]
+        seek_points: List[int] = [
+            round(ts * self.frames_per_second) for ts in options.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 = "\"'“¿([{-\"'.。,，!！?？:：”)]}、"
+
         idx = 0
-        seek = 0
+        clip_idx = 0
+        seek = seek_clips[clip_idx][0]
         all_tokens = []
         prompt_reset_since = 0
 
@@ -423,13 +471,34 @@ class WhisperModel:
 
         last_speech_timestamp = 0.0
         all_segments = []
-        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_clip_end > content_frames:
+                seek_clip_end = content_frames
+            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 = seek * self.feature_extractor.time_per_frame
-            segment = features[:, seek : seek + self.feature_extractor.nb_max_frames]
+            window_end_time = float(
-            segment_size = min(
+                (seek + self.feature_extractor.nb_max_frames)
-                self.feature_extractor.nb_max_frames, content_frames - seek
+                * self.feature_extractor.time_per_frame
             )
+            segment_size = min(
+                self.feature_extractor.nb_max_frames,
+                content_frames - seek,
+                seek_clip_end - seek,
+            )
+            segment = features[:, seek:seek + segment_size]
             segment_duration = segment_size * self.feature_extractor.time_per_frame
+            segment = pad_or_trim(segment, self.feature_extractor.nb_max_frames)
 
             if self.logger.isEnabledFor(logging.DEBUG):
                 self.logger.debug(
@@ -481,10 +550,33 @@ class WhisperModel:
             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)
+
             single_timestamp_ending = (
                 len(tokens) >= 2
-                and tokens[-2] < tokenizer.timestamp_begin
+                and tokens[-2] < tokenizer.timestamp_begin <= tokens[-1]
-                and tokens[-1] >= tokenizer.timestamp_begin
             )
 
             consecutive_timestamps = [
@@ -567,18 +659,62 @@ class WhisperModel:
                     last_speech_timestamp=last_speech_timestamp,
                 )
 
-                word_end_timestamps = [
+                if not single_timestamp_ending:
-                    w["end"] for s in current_segments for w in s["words"]
+                    last_word_end = get_end(current_segments)
-                ]
+                    if last_word_end is not None and last_word_end > time_offset:
-                if len(word_end_timestamps) > 0:
+                        seek = round(last_word_end * self.frames_per_second)
-                    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) * self.frames_per_second
-                    )
 
-                    if seek_shift > 0:
+                # skip silence before possible hallucinations
-                        seek = previous_seek + seek_shift
+                if options.hallucination_silence_threshold is not None:
+                    threshold = options.hallucination_silence_threshold
+
+                    # 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 * self.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"])
+                                    * self.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
 
             for segment in current_segments:
                 tokens = segment["tokens"]
@@ -646,6 +782,21 @@ class WhisperModel:
         max_initial_timestamp_index = int(
             round(options.max_initial_timestamp / self.time_precision)
         )
+        if options.max_new_tokens is not None:
+            max_length = len(prompt) + options.max_new_tokens
+        else:
+            max_length = self.max_length
+
+        if max_length > self.max_length:
+            raise ValueError(
+                f"The length of the prompt is {len(prompt)}, and the `max_new_tokens` "
+                f"{max_length - len(prompt)}. Thus, the combined length of the prompt "
+                f"and `max_new_tokens` is: {max_length}. This exceeds the "
+                f"`max_length` of the Whisper model: {self.max_length}. "
+                "You should either reduce the length of your prompt, or "
+                "reduce the value of `max_new_tokens`, "
+                f"so that their combined length is less that {self.max_length}."
+            )
 
         for temperature in options.temperatures:
             if temperature > 0:
@@ -667,7 +818,7 @@ class WhisperModel:
                 length_penalty=options.length_penalty,
                 repetition_penalty=options.repetition_penalty,
                 no_repeat_ngram_size=options.no_repeat_ngram_size,
-                max_length=self.max_length,
+                max_length=max_length,
                 return_scores=True,
                 return_no_speech_prob=True,
                 suppress_blank=options.suppress_blank,
@@ -725,6 +876,8 @@ class WhisperModel:
             if (
                 options.no_speech_threshold is not None
                 and result.no_speech_prob > options.no_speech_threshold
+                and options.log_prob_threshold is not None
+                and avg_logprob < options.log_prob_threshold
             ):
                 needs_fallback = False  # silence
 
@@ -735,6 +888,13 @@ class WhisperModel:
             decode_result = max(
                 below_cr_threshold_results or all_results, key=lambda x: x[1]
             )
+            # to pass final temperature for prompt_reset_on_temperature
+            decode_result = (
+                decode_result[0],
+                decode_result[1],
+                temperature,
+                decode_result[3],
+            )
 
         return decode_result
 
@@ -749,7 +909,7 @@ class WhisperModel:
 
         if previous_tokens:
             prompt.append(tokenizer.sot_prev)
-            prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
+            prompt.extend(previous_tokens[-(self.max_length // 2 - 1):])
 
         prompt.extend(tokenizer.sot_sequence)
 
@@ -791,6 +951,7 @@ class WhisperModel:
         word_durations = np.array([word["end"] - word["start"] for word 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.
@@ -912,6 +1073,13 @@ class WhisperModel:
         words, word_tokens = tokenizer.split_to_word_tokens(
             text_tokens + [tokenizer.eot]
         )
+        if len(word_tokens) <= 1:
+            # return on eot only
+            # >>> np.pad([], (1, 0))
+            # array([0.])
+            # This results in crashes when we lookup jump_times with float, like
+            # IndexError: arrays used as indices must be of integer (or boolean) type
+            return []
         word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0))
         if len(word_boundaries) <= 1:
             return []

whisper_live/transcriber_tensorrt.py

@@ -1,17 +1,14 @@
-import argparse
 import json
 import re
-import time
 from collections import OrderedDict
 from pathlib import Path
-from typing import Dict, Iterable, List, Optional, TextIO, Tuple, Union
+from typing import Union
 
 import torch
 import numpy as np
+import torch.nn.functional as F
 from whisper.tokenizer import get_tokenizer
-from whisper_live.tensorrt_utils import (mel_filters, store_transcripts,
+from whisper_live.tensorrt_utils import (mel_filters, load_audio_wav_format, pad_or_trim, load_audio)
-                           write_error_stats, load_audio_wav_format,
-                           pad_or_trim, load_audio)
 
 import tensorrt_llm
 import tensorrt_llm.logger as logger
@@ -56,12 +53,21 @@ class WhisperEncoding:
         return session
 
     def get_audio_features(self, mel):
-        inputs = OrderedDict()
-        output_list = []
 
-        inputs.update({'x': mel})
+        input_lengths = torch.tensor(
-        output_list.append(
+            [mel.shape[2] // 2 for _ in range(mel.shape[0])],
-            TensorInfo('x', str_dtype_to_trt(self.dtype), mel.shape))
+            dtype=torch.int32,
+            device=mel.device)
+
+        inputs = OrderedDict()
+        inputs['x'] = mel
+        inputs['input_lengths'] = input_lengths
+
+        output_list = [
+            TensorInfo('x', str_dtype_to_trt(self.dtype), mel.shape),
+            TensorInfo('input_lengths', str_dtype_to_trt('int32'),
+                       input_lengths.shape)
+        ]
 
         output_info = (self.session).infer_shapes(output_list)
 
@@ -106,6 +112,8 @@ class WhisperDecoding:
             decoder_engine_buffer = f.read()
 
         decoder_model_config = ModelConfig(
+            max_batch_size=self.decoder_config['max_batch_size'],
+            max_beam_width=self.decoder_config['max_beam_width'],
             num_heads=self.decoder_config['num_heads'],
             num_kv_heads=self.decoder_config['num_heads'],
             hidden_size=self.decoder_config['hidden_size'],
@@ -146,6 +154,10 @@ class WhisperDecoding:
                                              device='cuda')
         decoder_max_input_length = torch.max(decoder_input_lengths).item()
 
+        cross_attention_mask = torch.ones(
+            [encoder_outputs.shape[0], 1,
+             encoder_outputs.shape[1]]).int().cuda()
+
         # generation config
         sampling_config = SamplingConfig(end_id=eot_id,
                                          pad_id=eot_id,
@@ -166,6 +178,7 @@ class WhisperDecoding:
             sampling_config,
             encoder_output=encoder_outputs,
             encoder_input_lengths=encoder_input_lengths,
+            cross_attention_mask=cross_attention_mask,
         )
         torch.cuda.synchronize()
 
@@ -176,16 +189,8 @@ class WhisperDecoding:
 
 class WhisperTRTLLM(object):
 
-    def __init__(
+    def __init__(self, engine_dir, assets_dir=None, device=None, is_multilingual=False,
-        self,
+                 language="en", task="transcribe"):
-        engine_dir,
-        debug_mode=False,
-        assets_dir=None,
-        device=None,
-        is_multilingual=False,
-        language="en",
-        task="transcribe"
-        ):
         world_size = 1
         runtime_rank = tensorrt_llm.mpi_rank()
         runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
@@ -212,7 +217,7 @@ class WhisperTRTLLM(object):
         self,
         audio: Union[str, np.ndarray, torch.Tensor],
         padding: int = 0,
-        return_duration = True
+        return_duration=True
     ):
         """
         Compute the log-Mel spectrogram of
@@ -242,8 +247,7 @@ class WhisperTRTLLM(object):
                     audio, _ = load_audio_wav_format(audio)
                 else:
                     audio = load_audio(audio)
-            assert isinstance(audio,
+            assert isinstance(audio, np.ndarray), f"Unsupported audio type: {type(audio)}"
-                            np.ndarray), f"Unsupported audio type: {type(audio)}"
             duration = audio.shape[-1] / SAMPLE_RATE
             audio = pad_or_trim(audio, N_SAMPLES)
             audio = audio.astype(np.float32)
@@ -254,14 +258,9 @@ class WhisperTRTLLM(object):
         if padding > 0:
             audio = F.pad(audio, (0, padding))
         window = torch.hann_window(N_FFT).to(audio.device)
-        stft = torch.stft(audio,
+        stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
-                        N_FFT,
-                        HOP_LENGTH,
-                        window=window,
-                        return_complex=True)
         magnitudes = stft[..., :-1].abs()**2
 
-        
         mel_spec = self.filters @ magnitudes
 
         log_spec = torch.clamp(mel_spec, min=1e-10).log10()
@@ -272,7 +271,6 @@ class WhisperTRTLLM(object):
         else:
             return log_spec
 
-
     def process_batch(
             self,
             mel,

whisper_live/utils.py

@@ -0,0 +1,71 @@
+import os
+import textwrap
+import scipy
+import ffmpeg
+import numpy as np
+
+
+def clear_screen():
+    """Clears the console screen."""
+    os.system("cls" if os.name == "nt" else "clear")
+
+
+def print_transcript(text):
+    """Prints formatted transcript text."""
+    wrapper = textwrap.TextWrapper(width=60)
+    for line in wrapper.wrap(text="".join(text)):
+        print(line)
+
+
+def format_time(s):
+    """Convert seconds (float) to SRT time format."""
+    hours = int(s // 3600)
+    minutes = int((s % 3600) // 60)
+    seconds = int(s % 60)
+    milliseconds = int((s - int(s)) * 1000)
+    return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
+
+
+def create_srt_file(segments, output_file):
+    with open(output_file, 'w', encoding='utf-8') as srt_file:
+        segment_number = 1
+        for segment in segments:
+            start_time = format_time(float(segment['start']))
+            end_time = format_time(float(segment['end']))
+            text = segment['text']
+
+            srt_file.write(f"{segment_number}\n")
+            srt_file.write(f"{start_time} --> {end_time}\n")
+            srt_file.write(f"{text}\n\n")
+
+            segment_number += 1
+
+
+def resample(file: str, sr: int = 16000):
+    """
+    # https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/audio.py#L22
+    Open an audio file and read as mono waveform, resampling as necessary,
+    save the resampled audio
+
+    Args:
+        file (str): The audio file to open
+        sr (int): The sample rate to resample the audio if necessary
+
+    Returns:
+        resampled_file (str): The resampled audio file
+    """
+    try:
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except ffmpeg.Error as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+    np_buffer = np.frombuffer(out, dtype=np.int16)
+
+    resampled_file = f"{file.split('.')[0]}_resampled.wav"
+    scipy.io.wavfile.write(resampled_file, sr, np_buffer.astype(np.int16))
+    return resampled_file

whisper_live/vad.py

@@ -10,9 +10,7 @@ import onnxruntime
 class VoiceActivityDetection():
 
     def __init__(self, force_onnx_cpu=True):
-        print("downloading ONNX model...")
         path = self.download()
-        print("loading session")
 
         opts = onnxruntime.SessionOptions()
         opts.log_severity_level = 3
@@ -20,13 +18,11 @@ class VoiceActivityDetection():
         opts.inter_op_num_threads = 1
         opts.intra_op_num_threads = 1
 
-        print("loading onnx model")
         if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
             self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts)
         else:
             self.session = onnxruntime.InferenceSession(path, providers=['CUDAExecutionProvider'], sess_options=opts)
 
-        print("reset states")
         self.reset_states()
         self.sample_rates = [8000, 16000]
 
@@ -38,7 +34,7 @@ class VoiceActivityDetection():
 
         if sr != 16000 and (sr % 16000 == 0):
             step = sr // 16000
-            x = x[:,::step]
+            x = x[:, ::step]
             sr = 16000
 
         if sr not in self.sample_rates:
@@ -98,7 +94,7 @@ class VoiceActivityDetection():
         return stacked.cpu()
 
     @staticmethod
-    def download(model_url="https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx"):
+    def download(model_url="https://github.com/snakers4/silero-vad/raw/v4.0/files/silero_vad.onnx"):
         target_dir = os.path.expanduser("~/.cache/whisper-live/")
 
         # Ensure the target directory exists
@@ -110,9 +106,37 @@ class VoiceActivityDetection():
         # Check if the model file already exists
         if not os.path.exists(model_filename):
             # If it doesn't exist, download the model using wget
-            print("Downloading VAD ONNX model...")
             try:
                 subprocess.run(["wget", "-O", model_filename, model_url], check=True)
             except subprocess.CalledProcessError:
                 print("Failed to download the model using wget.")
         return model_filename
+
+
+class VoiceActivityDetector:
+    def __init__(self, threshold=0.5, frame_rate=16000):
+        """
+        Initializes the VoiceActivityDetector with a voice activity detection model and a threshold.
+
+        Args:
+            threshold (float, optional): The probability threshold for detecting voice activity. Defaults to 0.5.
+        """
+        self.model = VoiceActivityDetection()
+        self.threshold = threshold
+        self.frame_rate = frame_rate
+
+    def __call__(self, audio_frame):
+        """
+        Determines if the given audio frame contains speech by comparing the detected speech probability against
+        the threshold.
+
+        Args:
+            audio_frame (np.ndarray): The audio frame to be analyzed for voice activity. It is expected to be a
+                                      NumPy array of audio samples.
+
+        Returns:
+            bool: True if the speech probability exceeds the threshold, indicating the presence of voice activity;
+                  False otherwise.
+        """
+        speech_prob = self.model(torch.from_numpy(audio_frame), self.frame_rate).item()
+        return speech_prob > self.threshold