Bump version v0.8.0

Fix crash when no --files provided; use microphone input instead

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-22.04
     strategy:
       matrix:
-        python-version: [3.8, 3.9, '3.10', 3.11, 3.12]
+        python-version: [3.9, '3.10', 3.11, 3.12]
     steps:
       - uses: actions/checkout@v2
       
@@ -25,7 +25,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Cache Python dependencies
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: |
             ~/.cache/pip
@@ -52,7 +52,7 @@ jobs:
     runs-on: ubuntu-22.04
     strategy:
       matrix:
-        python-version: [3.8, 3.9, '3.10', 3.11, 3.12]
+        python-version: [3.9, '3.10', 3.11, 3.12]
 
     steps:
       - uses: actions/checkout@v2
@@ -99,35 +99,6 @@ jobs:
           push: true
           tags: ghcr.io/collabora/whisperlive-cpu:latest
   
-  build-and-push-docker-tensorrt:
-    needs: [run-tests, check-code-format]
-    timeout-minutes: 60
-    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
@@ -157,6 +128,35 @@ jobs:
           push: true
           tags: ghcr.io/collabora/whisperlive-gpu:latest
 
+  build-and-push-docker-openvino:
+    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.openvino
+          push: true
+          tags: ghcr.io/collabora/whisperlive-openvino:latest
+
   publish-to-pypi:
     needs: [run-tests, check-code-format]
     runs-on: ubuntu-22.04
@@ -164,20 +164,20 @@ jobs:
     steps:
       - uses: actions/checkout@v2
 
-      - name: Set up Python 3.8
+      - name: Set up Python 3.9
         uses: actions/setup-python@v2
         with:
-          python-version: 3.8
+          python-version: 3.9
 
       - name: Cache Python dependencies
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: |
             ~/.cache/pip
             !~/.cache/pip/log
-          key: ubuntu-latest-pip-3.8-${{ hashFiles('requirements/server.txt', 'requirements/client.txt') }}
+          key: ubuntu-latest-pip-3.9-${{ hashFiles('requirements/server.txt', 'requirements/client.txt') }}
           restore-keys: |
-            ubuntu-latest-pip-3.8-
+            ubuntu-latest-pip-3.9-
 
       - name: Install system dependencies
         run: sudo apt-get update && sudo apt-get install -y portaudio19-dev

Audio-Transcription-Chrome/README.md

@@ -27,6 +27,7 @@ To capture the audio in the current tab, we used the chrome `tabCapture` API to
 When using the Audio Transcription extension, you have the following options:
  - **Use Collabora Server**: We provide a demo server which runs the whisper small model.
  - **Language**: Select the target language for transcription or translation. You can choose from a variety of languages supported by OpenAI-whisper.
+ - **Download SRT file at Stop Capture**: Select if you want to download the srt file for the session at stop capture.
  - **Task:** Choose the specific task to perform on the audio. You can select either "transcribe" for transcription or "translate" to translate the audio to English.
  - **Model Size**: Select the whisper model size to run the server with.
 

Audio-Transcription-Chrome/audiopreprocessor.js

@@ -0,0 +1,77 @@
+class AudioPreProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+    this.sampleRate = sampleRate || 48000;
+    this.targetSampleRate = 16000;
+    this.inputSamplesNeeded = this.sampleRate * 0.5; // 0.5s
+    this.inputBuffer = new Float32Array(this.inputSamplesNeeded);
+    this.inputWriteOffset = 0;
+    this.processCount = 0;
+    this.audioDetectedCount = 0;
+  }
+
+  process(inputs, outputs) {
+    this.processCount++;
+    
+    const input = inputs[0];
+    const output = outputs[0];
+   
+    if (!input || input.length === 0) {
+      return true;
+    }
+
+    for (let channel = 0; channel < Math.min(input.length, output.length); channel++) {
+      if (input[channel] && output[channel]) {
+        output[channel].set(input[channel]);
+      }
+    }
+
+    let monoInput;
+    if (input.length === 1) {
+      monoInput = input[0];
+    } else if (input.length >= 2) {
+      monoInput = new Float32Array(input[0].length);
+      for (let i = 0; i < input[0].length; i++) {
+        monoInput[i] = (input[0][i] + (input[1] ? input[1][i] : 0)) * 0.5;
+      }
+    } else {
+      return true;
+    }
+
+    if (!monoInput || monoInput.length === 0) {
+      return true;
+    }
+
+    let inputOffset = 0;
+    while (inputOffset < monoInput.length) {
+      const remainingBuffer = this.inputSamplesNeeded - this.inputWriteOffset;
+      const toCopy = Math.min(remainingBuffer, monoInput.length - inputOffset);
+      this.inputBuffer.set(monoInput.subarray(inputOffset, inputOffset + toCopy), this.inputWriteOffset);
+
+      this.inputWriteOffset += toCopy;
+      inputOffset += toCopy;
+
+      if (this.inputWriteOffset === this.inputSamplesNeeded) {
+        const downsampled = this.downsampleTo16kHz(this.inputBuffer);
+        this.port.postMessage(downsampled);
+
+        this.inputWriteOffset = 0;
+      }
+    }
+
+    return true;
+  }
+
+  downsampleTo16kHz(inputBuffer) {
+    const ratio = this.sampleRate / this.targetSampleRate;
+    const length = Math.floor(inputBuffer.length / ratio);
+    const result = new Float32Array(length);
+    for (let i = 0; i < length; i++) {
+      const idx = Math.floor(i * ratio);
+      result[i] = inputBuffer[idx];
+    }
+    return result;
+  }
+}
+
+registerProcessor('audiopreprocessor', AudioPreProcessor);

Audio-Transcription-Chrome/background.js

@@ -159,6 +159,7 @@ async function startCapture(options) {
           task: options.task,
           modelSize: options.modelSize,
           useVad: options.useVad,
+          saveCaptions: options.saveCaptions,
         },
       });
     } else {
@@ -174,14 +175,14 @@ async function startCapture(options) {
  * Stops the capture process and performs cleanup.
  * @returns {Promise<void>} - A Promise that resolves when the capture process is stopped successfully.
  */
-async function stopCapture() {
+async function stopCapture(options) {
   const optionTabId = await getLocalStorageValue("optionTabId");
   const currentTabId = await getLocalStorageValue("currentTabId");
 
   if (optionTabId) {
     res = await sendMessageToTab(currentTabId, {
       type: "STOP",
-      data: { currentTabId: currentTabId },
+      data: { currentTabId: currentTabId, saveCaptions: options.saveCaptions },
     });
     await removeChromeTab(optionTabId);
   }
@@ -196,7 +197,7 @@ chrome.runtime.onMessage.addListener(async (message) => {
   if (message.action === "startCapture") {
     startCapture(message);
   } else if (message.action === "stopCapture") {
-    stopCapture();
+    stopCapture(message);
   } else if (message.action === "updateSelectedLanguage") {
     const detectedLanguage = message.detectedLanguage;
     chrome.runtime.sendMessage({ action: "updateSelectedLanguage", detectedLanguage });
@@ -204,7 +205,7 @@ chrome.runtime.onMessage.addListener(async (message) => {
   } else if (message.action === "toggleCaptureButtons") {
     chrome.runtime.sendMessage({ action: "toggleCaptureButtons", data: false });
     chrome.storage.local.set({ capturingState: { isCapturing: false } })
-    stopCapture();
+    stopCapture({saveCaptions: message.saveCaptions});
   }
 });
 

Audio-Transcription-Chrome/content.js

@@ -1,10 +1,45 @@
-
-
 var elem_container = null;
 var elem_text = null;
 
 var segments = [];
 var text_segments = [];
+var allSegments = [];
+var lastIncompleteSegment = null;
+
+function formatTime(seconds) {
+  const date = new Date(seconds * 1000);
+  const hh = String(date.getUTCHours()).padStart(2, '0');
+  const mm = String(date.getUTCMinutes()).padStart(2, '0');
+  const ss = String(date.getUTCSeconds()).padStart(2, '0');
+  const mmm = String(date.getUTCMilliseconds()).padStart(3, '0');
+  return `${hh}:${mm}:${ss},${mmm}`;
+}
+
+function generateSRT() {
+    return allSegments
+    .map((seg, i) => {
+    const start = formatTime(seg.start);
+    const end   = formatTime(seg.end);
+    const text  = seg.text.trim().replace(/[\r\n]+/g, ' ');
+    return `${i + 1}\n${start} --> ${end}\n${text}`;
+    })
+    .join('\n\n');
+}
+
+function downloadSRT() {
+    console.log("downloadSRT called");
+    console.log("Total segments for SRT:", allSegments.length);
+    const srtBlob = new Blob([generateSRT()], { type: 'text/srt;charset=utf-8' });
+    const url = URL.createObjectURL(srtBlob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = 'captions.srt';
+    a.style.display = 'none';
+    document.body.appendChild(a);
+    a.click();
+    URL.revokeObjectURL(url);
+    document.body.removeChild(a);
+}
 
 function initPopupElement() {
   if (document.getElementById('popupElement')) {
@@ -32,7 +67,7 @@ function initPopupElement() {
   closePopupButton.style.cursor = 'pointer';
   closePopupButton.addEventListener('click', async () => {
     popupContainer.style.display = 'none';
-    await browser.runtime.sendMessage({ action: 'toggleCaptureButtons', data: false });
+    await chrome.runtime.sendMessage({ action: 'toggleCaptureButtons', data: false });
   });
   buttonContainer.appendChild(closePopupButton);
   popupContainer.appendChild(buttonContainer);
@@ -169,8 +204,25 @@ function remove_element() {
 
 chrome.runtime.onMessage.addListener((request, sender, sendResponse) => {
     const { type, data } = request;
-    
-    if (type === "STOP") {
+    const saveCaptions = data.saveCaptions;
+
+    if (type === "STOP") {        
+        if (saveCaptions === true) {
+            // If there is a last incomplete segment, push it to allSegments
+            if (lastIncompleteSegment && lastIncompleteSegment.text && lastIncompleteSegment.text.trim() !== "") {
+                // Apply same Python logic: check if transcript is empty OR start >= last end
+                if (allSegments.length === 0 || parseFloat(lastIncompleteSegment.start) >= parseFloat(allSegments[allSegments.length - 1].end)) {
+                    allSegments.push({
+                        start: lastIncompleteSegment.start,
+                        end: lastIncompleteSegment.end,
+                        text: lastIncompleteSegment.text
+                    });
+                    console.log("Added final incomplete segment");
+                }
+            }
+        
+            downloadSRT();
+        }
         remove_element();
         sendResponse({data: "STOPPED"});
         return true;
@@ -184,53 +236,77 @@ chrome.runtime.onMessage.addListener((request, sender, sendResponse) => {
 
     init_element();
 
-    message = JSON.parse(data);
-    message = message["segments"];
-
-    var text = '';
-    for (var i = 0; i < message.length; i++) {
-        text += message[i].text + ' ';
-    }
-    text = text.replace(/(\r\n|\n|\r)/gm, "");
-    
-    var elem = document.getElementById('t3');
-    elem.innerHTML = text;
-
-    var line_height_style = getStyle('t3', 'line-height');
-    var line_height = parseInt(line_height_style.substring(0, line_height_style.length - 2));
-    var divHeight = elem.offsetHeight;
-    var lines = divHeight / line_height;
-
-    text_segments = [];
-    text_segments = get_lines(elem, line_height);
-    
-    elem.innerHTML = '';
-
-    if (text_segments.length > 2) {
-        for (var i = 0; i < 3; i++) {
-            document.getElementById('t' + i).innerHTML = text_segments[text_segments.length - 3 + i];
+    try {
+        const message = JSON.parse(data.data);
+        const segments = message["segments"];
+        
+        if (saveCaptions === true) {
+            segments.forEach(seg => {
+                if (seg.completed === true && 
+                    (allSegments.length === 0 || parseFloat(seg.start) >= parseFloat(allSegments[allSegments.length - 1].end))) {
+                    allSegments.push({
+                        start: seg.start,
+                        end: seg.end,
+                        text: seg.text
+                    });
+                    
+                    lastIncompleteSegment = null;
+                } else if (seg.completed !== true) {
+                    lastIncompleteSegment = seg;
+                }
+            });
         }
-    } else {
-        for (var i = 0; i < 3; i++) {
-            document.getElementById('t' + i).innerHTML = '';
+        var text = '';
+        for (var i = 0; i < segments.length; i++) {
+            text += segments[i].text + ' ';
         }
-    }
+        text = text.replace(/(\r\n|\n|\r)/gm, "");
+        
+        var elem = document.getElementById('t3');
+        if (elem) {
+            elem.innerHTML = text;
 
-    if (text_segments.length <= 2) {
-        for (var i = 0; i < text_segments.length; i++) {
-            document.getElementById('t' + i).innerHTML = text_segments[i];
-        }
-    } else {
-        for (var i = 0; i < 3; i++) {
-            document.getElementById('t' + i).innerHTML = text_segments[text_segments.length - 3 + i];
-        }
-    }
+            var line_height_style = getStyle('t3', 'line-height');
+            var line_height = parseInt(line_height_style.substring(0, line_height_style.length - 2));
+            var divHeight = elem.offsetHeight;
+            var lines = divHeight / line_height;
 
-    for (var i = 1; i < 3; i++)
-    {
-        var parent_elem = document.getElementById('t' + (i - 1));
-        var elem = document.getElementById('t' + i);
-        elem.style.top = parent_elem.offsetHeight + parent_elem.offsetTop + 'px';
+            text_segments = [];
+            text_segments = get_lines(elem, line_height);
+            
+            elem.innerHTML = '';
+
+            if (text_segments.length > 2) {
+                for (var i = 0; i < 3; i++) {
+                    document.getElementById('t' + i).innerHTML = text_segments[text_segments.length - 3 + i];
+                }
+            } else {
+                for (var i = 0; i < 3; i++) {
+                    document.getElementById('t' + i).innerHTML = '';
+                }
+            }
+
+            if (text_segments.length <= 2) {
+                for (var i = 0; i < text_segments.length; i++) {
+                    document.getElementById('t' + i).innerHTML = text_segments[i];
+                }
+            } else {
+                for (var i = 0; i < 3; i++) {
+                    document.getElementById('t' + i).innerHTML = text_segments[text_segments.length - 3 + i];
+                }
+            }
+
+            for (var i = 1; i < 3; i++)
+            {
+                var parent_elem = document.getElementById('t' + (i - 1));
+                var elem = document.getElementById('t' + i);
+                if (parent_elem && elem) {
+                    elem.style.top = parent_elem.offsetHeight + parent_elem.offsetTop + 'px';
+                }
+            }
+        }
+    } catch (error) {
+        console.error("Error processing message:", error);
     }
 
     sendResponse({});

Audio-Transcription-Chrome/manifest.json

@@ -1,14 +1,20 @@
-  {
+{
     "manifest_version": 3,
 
     "name": "Audio Transcription",
     "version": "1.0.0",
     "description": "This extension captures the audio on the current tab, sends it to a server for transcription and shows the transcription in Real-time.",
-    
+
     "options_page": "options.html",
     "background": {
         "service_worker": "background.js"
     },
+    "web_accessible_resources": [
+        {
+        "resources": ["audiopreprocessor.js"],
+        "matches": ["<all_urls>"]
+        }
+    ],
     "permissions": [
         "storage",
         "activeTab",

Audio-Transcription-Chrome/options.js

@@ -31,41 +31,6 @@ function sendMessageToTab(tabId, data) {
   });
 }
 
-
-/**
- * Resamples the audio data to a target sample rate of 16kHz.
- * @param {Array|ArrayBuffer|TypedArray} audioData - The input audio data.
- * @param {number} [origSampleRate=44100] - The original sample rate of the audio data.
- * @returns {Float32Array} The resampled audio data at 16kHz.
- */
-function resampleTo16kHZ(audioData, origSampleRate = 44100) {
-  // Convert the audio data to a Float32Array
-  const data = new Float32Array(audioData);
-
-  // Calculate the desired length of the resampled data
-  const targetLength = Math.round(data.length * (16000 / origSampleRate));
-
-  // Create a new Float32Array for the resampled data
-  const resampledData = new Float32Array(targetLength);
-
-  // Calculate the spring factor and initialize the first and last values
-  const springFactor = (data.length - 1) / (targetLength - 1);
-  resampledData[0] = data[0];
-  resampledData[targetLength - 1] = data[data.length - 1];
-
-  // Resample the audio data
-  for (let i = 1; i < targetLength - 1; i++) {
-    const index = i * springFactor;
-    const leftIndex = Math.floor(index).toFixed();
-    const rightIndex = Math.ceil(index).toFixed();
-    const fraction = index - leftIndex;
-    resampledData[i] = data[leftIndex] + (data[rightIndex] - data[leftIndex]) * fraction;
-  }
-
-  // Return the resampled data
-  return resampledData;
-}
-
 function generateUUID() {
   let dt = new Date().getTime();
   const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {
@@ -76,24 +41,99 @@ function generateUUID() {
   return uuid;
 }
 
+// Global variables for audio processing
+let audioContext = null;
+let preNode = null;
+let socket = null;
+let isServerReady = false;
+let currentStream = null;
+let currentOptions = null;
+
+// AudioWorklet URL - make sure this path matches your manifest.json
+const WORKLET_URL = chrome.runtime.getURL('audiopreprocessor.js');
+
+async function initAudioWorklet(stream) {
+  audioContext = new AudioContext();
+  if (audioContext.state === 'suspended') {
+    await audioContext.resume();
+  }
+
+  try {
+    await audioContext.audioWorklet.addModule(WORKLET_URL);
+    preNode = new AudioWorkletNode(audioContext, 'audiopreprocessor');
+    const mediaStream = audioContext.createMediaStreamSource(stream);
+    
+    mediaStream.connect(preNode);
+    preNode.connect(audioContext.destination);
+    preNode.port.onmessage = (event) => {
+      const data = event.data;
+      
+      
+      const audio16k = data; // Float32Array @ 16 kHz
+      
+      if (socket && socket.readyState === WebSocket.OPEN && isServerReady) {
+        socket.send(audio16k);
+      }
+    };
+        
+    // Test if we can hear audio (this will help verify the audio path)
+    
+  } catch (error) {
+    console.error("Error initializing AudioWorklet:", error);
+    throw error;
+  }
+}
+
+function cleanupAudio() {
+  
+  if (preNode) {
+    preNode.port.onmessage = null;
+    preNode.disconnect();
+    preNode = null;
+  }
+  
+  if (audioContext) {
+    audioContext.close();
+    audioContext = null;
+  }
+
+  if (currentStream) {
+    currentStream.getTracks().forEach(track => {
+      track.stop();
+      console.log("Stopped track:", track.kind);
+    });
+    currentStream = null;
+  }
+}
 
 /**
  * Starts recording audio from the captured tab.
  * @param {Object} option - The options object containing the currentTabId.
  */
 async function startRecord(option) {
+  currentOptions = option;
   const stream = await captureTabAudio();
   const uuid = generateUUID();
 
   if (stream) {
-    // call when the stream inactive
+    currentStream = stream;
     stream.oninactive = () => {
+      cleanupAudio();
       window.close();
     };
-    const socket = new WebSocket(`ws://${option.host}:${option.port}/`);
-    let isServerReady = false;
+
+    try {
+      await initAudioWorklet(stream);
+    } catch (error) {
+      console.error("Failed to initialize AudioWorklet:", error);
+      return;
+    }
+
+    socket = new WebSocket(`ws://${option.host}:${option.port}/`);
+    isServerReady = false;
     let language = option.language;
-    socket.onopen = function(e) { 
+
+    socket.onopen = function(e) {
       socket.send(
         JSON.stringify({
           uid: uuid,
@@ -129,7 +169,6 @@ async function startRecord(option) {
         language = data["language"];
         
         // send message to popup.js to update dropdown
-        // console.log(language);
         chrome.runtime.sendMessage({
           action: "updateSelectedLanguage",
           detectedLanguage: language,
@@ -139,43 +178,33 @@ async function startRecord(option) {
       }
 
       if (data["message"] === "DISCONNECT"){
-        chrome.runtime.sendMessage({ action: "toggleCaptureButtons", data: false })        
+        chrome.runtime.sendMessage({ action: "toggleCaptureButtons", data: false, saveCaptions: option.saveCaptions });        
         return;
       }
 
-      res = await sendMessageToTab(option.currentTabId, {
+      const res = await sendMessageToTab(option.currentTabId, {
         type: "transcript",
-        data: event.data,
+        data: {
+          data: event.data,
+          saveCaptions: option.saveCaptions,
+        },
       });
     };
 
-    
-    const audioDataCache = [];
-    const context = new AudioContext();
-    const mediaStream = context.createMediaStreamSource(stream);
-    const recorder = context.createScriptProcessor(4096, 1, 1);
-
-    recorder.onaudioprocess = async (event) => {
-      if (!context || !isServerReady) return;
-
-      const inputData = event.inputBuffer.getChannelData(0);
-      const audioData16kHz = resampleTo16kHZ(inputData, context.sampleRate);
-
-      audioDataCache.push(inputData);
-
-      socket.send(audioData16kHz);
+    socket.onclose = () => {
+      cleanupAudio();
+    };
+
+    socket.onerror = (error) => {
+      cleanupAudio();
     };
 
-    // Prevent page mute
-    mediaStream.connect(recorder);
-    recorder.connect(context.destination);
-    mediaStream.connect(context.destination);
-    // }
   } else {
     window.close();
   }
 }
 
+
 /**
  * Listener for incoming messages from the extension's background script.
  * @param {Object} request - The message request object.

Audio-Transcription-Chrome/popup.html

@@ -19,6 +19,10 @@
     <input type="checkbox" id="useVadCheckbox">
     <label for="useVadCheckbox">Use Voice Activity Detection</label>
   </div>
+  <div class="checkbox-container">
+    <input type="checkbox" id="saveCaptionsCheckbox">
+    <label for="saveCaptions">Download SRT file at Stop Capture</label>
+  </div>
   <div class="dropdown-container">
     <label for="languageDropdown">Select Language:</label>
     <select id="languageDropdown">

Audio-Transcription-Chrome/popup.js

@@ -5,6 +5,7 @@ document.addEventListener("DOMContentLoaded", function () {
 
   const useServerCheckbox = document.getElementById("useServerCheckbox");
   const useVadCheckbox = document.getElementById("useVadCheckbox");
+  const saveCaptionsCheckbox = document.getElementById("saveCaptionsCheckbox");
   const languageDropdown = document.getElementById('languageDropdown');
   const taskDropdown = document.getElementById('taskDropdown');
   const modelSizeDropdown = document.getElementById('modelSizeDropdown');
@@ -38,6 +39,12 @@ document.addEventListener("DOMContentLoaded", function () {
     }
   });
 
+  chrome.storage.local.get("saveCaptionsState", ({ saveCaptionsState }) => {
+    if (saveCaptionsState !== undefined) {
+      saveCaptionsCheckbox.checked = saveCaptionsState;
+    }
+  });
+
   chrome.storage.local.get("selectedLanguage", ({ selectedLanguage: storedLanguage }) => {
     if (storedLanguage !== undefined) {
       languageDropdown.value = storedLanguage;
@@ -88,6 +95,7 @@ document.addEventListener("DOMContentLoaded", function () {
         task: selectedTask,
         modelSize: selectedModelSize,
         useVad: useVadCheckbox.checked,
+        saveCaptions: saveCaptionsCheckbox.checked,
       }, () => {
         // Update capturing state in storage and toggle the buttons
         chrome.storage.local.set({ capturingState: { isCapturing: true } }, () => {
@@ -105,7 +113,11 @@ document.addEventListener("DOMContentLoaded", function () {
     }
 
     // Send a message to the background script to stop capturing
-    chrome.runtime.sendMessage({ action: "stopCapture" }, () => {
+    chrome.runtime.sendMessage(
+      { 
+        action: "stopCapture",
+        saveCaptions: saveCaptionsCheckbox.checked,
+      }, () => {
       // Update capturing state in storage and toggle the buttons
       chrome.storage.local.set({ capturingState: { isCapturing: false } }, () => {
         toggleCaptureButtons(false);
@@ -128,6 +140,7 @@ document.addEventListener("DOMContentLoaded", function () {
     stopButton.disabled = !isCapturing;
     useServerCheckbox.disabled = isCapturing;
     useVadCheckbox.disabled = isCapturing;
+    saveCaptionsCheckbox.disabled = isCapturing;
     modelSizeDropdown.disabled = isCapturing;
     languageDropdown.disabled = isCapturing;
     taskDropdown.disabled = isCapturing; 
@@ -146,6 +159,11 @@ document.addEventListener("DOMContentLoaded", function () {
     chrome.storage.local.set({ useVadState });
   });
 
+  saveCaptionsCheckbox.addEventListener("change", () => {
+    const saveCaptionsState = saveCaptionsCheckbox.checked;
+    chrome.storage.local.set({ saveCaptionsState });
+  });
+
   languageDropdown.addEventListener('change', function() {
     if (languageDropdown.value === "") {
       selectedLanguage = null;

Audio-Transcription-Firefox/README.md

@@ -25,6 +25,7 @@ To capture the audio in the current tab, we used the chrome `tabCapture` API to
 When using the Audio Transcription extension, you have the following options:
  - **Use Collabora Server**: We provide a demo server which runs the whisper small model.
  - **Language**: Select the target language for transcription or translation. You can choose from a variety of languages supported by OpenAI-whisper.
+ - **Download SRT file at Stop Capture**: Select if you want to download the srt file for the session at stop capture.
  - **Task:** Choose the specific task to perform on the audio. You can select either "transcribe" for transcription or "translate" to translate the audio to English.
   - **Model Size**: Select the whisper model size to run the server with.
 

Audio-Transcription-Firefox/audiopreprocessor.js

@@ -0,0 +1,70 @@
+class AudioPreProcessor extends AudioWorkletProcessor {
+    constructor() {
+      super();
+      this.sampleRate = sampleRate || 48000;
+      this.targetSampleRate = 16000;
+      this.inputSamplesNeeded = this.sampleRate * 0.5;
+      this.inputBuffer = new Float32Array(this.inputSamplesNeeded);
+      this.inputWriteOffset = 0;
+    }
+  
+    process(inputs, outputs) {
+      const input = inputs[0]; 
+      const output = outputs[0];
+      if (!input || input.length === 0) {
+        return true;
+      }
+      for (let channel = 0; channel < Math.min(input.length, output.length); channel++) {
+        if (input[channel] && output[channel]) {
+          output[channel].set(input[channel]);
+        }
+      }
+
+      let monoInput;
+      if (input.length === 1) {
+        monoInput = input[0];
+      } else if (input.length > 1) {
+        monoInput = new Float32Array(input[0].length);
+        for (let channel = 0; channel < input.length; channel++) {
+          monoInput.set(input[channel], 0);
+        }
+      }
+
+      if (!monoInput) {
+        return true;
+      }
+  
+      let inputOffset = 0;
+      while (inputOffset < monoInput.length) {
+        const remainingBuffer = this.inputSamplesNeeded - this.inputWriteOffset;
+        const toCopy = Math.min(remainingBuffer, monoInput.length - inputOffset);
+        this.inputBuffer.set(monoInput.subarray(inputOffset, inputOffset + toCopy), this.inputWriteOffset);
+
+        this.inputWriteOffset += toCopy;
+        inputOffset += toCopy;
+
+        if (this.inputWriteOffset === this.inputSamplesNeeded) {
+          const downsampled = this.downsampleTo16kHz(this.inputBuffer);
+          this.port.postMessage(downsampled);
+
+          this.inputWriteOffset = 0;
+        }
+      }
+
+      return true;
+    }
+  
+    downsampleTo16kHz(inputBuffer) {
+      const ratio = this.sampleRate / this.targetSampleRate;
+      const length = Math.floor(inputBuffer.length / ratio); 
+      const result = new Float32Array(length);
+      for (let i = 0; i < length; i++) {
+        const idx = Math.floor(i * ratio);
+        result[i] = inputBuffer[idx];
+      }
+      return result;
+    }
+  }
+  
+  registerProcessor('audiopreprocessor', AudioPreProcessor);
+  

Audio-Transcription-Firefox/content.js

@@ -1,144 +1,162 @@
 let socket = null;
 let isCapturing = false;
-let mediaStream = null;
 let audioContext = null;
-let scriptProcessor = null;
 let language = null;
-
 let isPaused = false;
+let preNode = null;
+let allSegments = [];
+let lastIncompleteSegment = null;
 
-const mediaElements = document.querySelectorAll('video, audio');
-mediaElements.forEach((mediaElement) => {
-  mediaElement.addEventListener('play', handlePlaybackStateChange);
-  mediaElement.addEventListener('pause', handlePlaybackStateChange);
-});
-
-
-function handlePlaybackStateChange(event) {
-  isPaused = event.target.paused;
+function formatTime(seconds) {
+  const date = new Date(seconds * 1000);
+  const hh = String(date.getUTCHours()).padStart(2, '0');
+  const mm = String(date.getUTCMinutes()).padStart(2, '0');
+  const ss = String(date.getUTCSeconds()).padStart(2, '0');
+  const mmm = String(date.getUTCMilliseconds()).padStart(3, '0');
+  return `${hh}:${mm}:${ss},${mmm}`;
 }
 
+function generateSRT() {
+    return allSegments
+    .map((seg, i) => {
+    const start = formatTime(seg.start);
+    const end   = formatTime(seg.end);
+    const text  = seg.text.trim().replace(/[\r\n]+/g, ' ');
+    return `${i + 1}\n${start} --> ${end}\n${text}`;
+    })
+    .join('\n\n');
+}
+
+function downloadSRT() {
+    const srtBlob = new Blob([generateSRT()], { type: 'text/srt;charset=utf-8' });
+    const url = URL.createObjectURL(srtBlob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = 'captions.srt';
+    a.style.display = 'none';
+    document.body.appendChild(a);
+    a.click();
+    URL.revokeObjectURL(url);
+    document.body.removeChild(a);
+}
+
+
 function generateUUID() {
   let dt = new Date().getTime();
-  const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {
+  return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
     const r = (dt + Math.random() * 16) % 16 | 0;
     dt = Math.floor(dt / 16);
     return (c === 'x' ? r : (r & 0x3 | 0x8)).toString(16);
   });
-  return uuid;
 }
 
 
-/**
- * Resamples the audio data to a target sample rate of 16kHz.
- * @param {Array|ArrayBuffer|TypedArray} audioData - The input audio data.
- * @param {number} [origSampleRate=44100] - The original sample rate of the audio data.
- * @returns {Float32Array} The resampled audio data at 16kHz.
- */
-function resampleTo16kHZ(audioData, origSampleRate = 44100) {
-  // Convert the audio data to a Float32Array
-  const data = new Float32Array(audioData);
+document.querySelectorAll('video, audio').forEach(el => {
+  el.addEventListener('play', () => { isPaused = false; });
+  el.addEventListener('pause', () => { isPaused = true; });
+});
 
-  // Calculate the desired length of the resampled data
-  const targetLength = Math.round(data.length * (16000 / origSampleRate));
 
-  // Create a new Float32Array for the resampled data
-  const resampledData = new Float32Array(targetLength);
+function setupMessageHandler() {
+  if (preNode) {
+    preNode.port.onmessage = e => {
+      const audio16k = e.data; 
+      if (isCapturing && socket && socket.readyState === WebSocket.OPEN && !isPaused) {
+        socket.send(audio16k);
+      }
+    };
+  }
+}
 
-  // Calculate the spring factor and initialize the first and last values
-  const springFactor = (data.length - 1) / (targetLength - 1);
-  resampledData[0] = data[0];
-  resampledData[targetLength - 1] = data[data.length - 1];
 
-  // Resample the audio data
-  for (let i = 1; i < targetLength - 1; i++) {
-    const index = i * springFactor;
-    const leftIndex = Math.floor(index).toFixed();
-    const rightIndex = Math.ceil(index).toFixed();
-    const fraction = index - leftIndex;
-    resampledData[i] = data[leftIndex] + (data[rightIndex] - data[leftIndex]) * fraction;
+const WORKLET_URL = browser.runtime.getURL('audiopreprocessor.js');
+
+async function initAudioWorklet() {
+  if (audioContext && preNode) {
+    setupMessageHandler();
+    return;
+  }
+  audioContext = new AudioContext();
+  await audioContext.audioWorklet.addModule(WORKLET_URL);
+
+  preNode = new AudioWorkletNode(audioContext, 'audiopreprocessor');
+  document.querySelectorAll('audio, video').forEach(el => {
+    let src;
+    try {
+      src = audioContext.createMediaElementSource(el);
+    } catch(e) {
+      console.warn('Could not create MediaElementSource for', el, e);
+      return;
+    }
+    src.connect(preNode);
+    src.connect(audioContext.destination);
+  });
+
+  preNode.connect(audioContext.destination);
+
+  setupMessageHandler();
+}
+
+async function startRecording(data) {
+  if (!audioContext) {
+    await initAudioWorklet();
   }
 
-  // Return the resampled data
-  return resampledData;
+  const uid = generateUUID();
+  socket = new WebSocket(`ws://${data.host}:${data.port}/`);
+  language = data.language;
+
+  socket.onopen = () => {
+    socket.send(JSON.stringify({
+      uid,
+      language: data.language,
+      task: data.task,
+      model: data.modelSize,
+      use_vad: data.useVad
+    }));
+  };
+
+  let serverReady = false;
+  socket.onmessage = async event => {
+    const msg = JSON.parse(event.data);
+    if (msg.uid !== uid) return;
+
+    if (msg.status === 'WAIT') {
+      await browser.runtime.sendMessage({ action: 'showPopup', data: msg.message });
+      return;
+    }
+    if (!serverReady && msg.message === 'SERVER_READY') {
+      serverReady = true;
+      return;
+    }
+    if (!language && msg.language) {
+      language = msg.language;
+      await browser.runtime.sendMessage({ action: 'updateSelectedLanguage', data: language });
+      return;
+    }
+    if (msg.message === 'DISCONNECT') {
+      await browser.runtime.sendMessage({ action: 'toggleCaptureButtons' });
+      return;
+    }
+    if (msg.segments) {
+      await browser.runtime.sendMessage({ action: 'transcript', data: {data: event.data, saveCaption: data.saveCaption} });
+    }
+  };
+
+  isCapturing = true;
 }
 
-function startRecording(data) {
-    socket = new WebSocket(`ws://${data.host}:${data.port}/`);
-    language = data.language;
+function stopRecording() {
+  isCapturing = false;
+  if (socket) {
+    socket.close();
+    socket = null;
+  }
 
-    const uuid = generateUUID();
-    socket.onopen = function(e) { 
-      socket.send(
-        JSON.stringify({
-            uid: uuid,
-            language: data.language,
-            task: data.task,
-            model: data.modelSize,
-            use_vad: data.useVad
-        })
-      );
-    };
-
-    let isServerReady = false;
-    socket.onmessage = async (event) => {
-      const data = JSON.parse(event.data);
-      if (data["uid"] !== uuid)
-        return;
-      
-      if (data["status"] === "WAIT"){
-        await browser.runtime.sendMessage({ action: "showPopup", data: data["message"] })
-        return;
-      }
-      
-      if (!isServerReady && data["message"] === "SERVER_READY"){
-        isServerReady = true;
-        return;
-      }
-
-      if (language === null ){
-        language = data["language"];
-        await browser.runtime.sendMessage({ action: "updateSelectedLanguage", data: language })      
-        return
-      }
-
-      if (data["message"] === "DISCONNECT"){
-        await browser.runtime.sendMessage({ action: "toggleCaptureButtons", data: false })        
-        return
-      }
-
-      await browser.runtime.sendMessage({ action: "transcript", data: event.data })
-          .catch(function(error) {
-            console.error("Error sending message:", error);
-          });
-    };
-
-  // Access the audio stream from the current tab
-  navigator.mediaDevices.getUserMedia({ audio: true })
-    .then(function(stream) {
-      // Create a new MediaRecorder instance
-      const audioDataCache = [];
-      audioContext = new AudioContext();
-      mediaStream = audioContext.createMediaStreamSource(stream);
-      recorder = audioContext.createScriptProcessor(4096, 1, 1);
-
-      recorder.onaudioprocess = async (event) => {
-        if (!audioContext || !isCapturing || !isServerReady || isPaused) return;
-
-        const inputData = event.inputBuffer.getChannelData(0);
-        const audioData16kHz = resampleTo16kHZ(inputData, audioContext.sampleRate);
-
-        audioDataCache.push(inputData);
-        
-        socket.send(audioData16kHz);
-      };
-
-      // Prevent page mute
-      mediaStream.connect(recorder);
-      recorder.connect(audioContext.destination);
-    })
+  remove_element();
 }
 
+
 var elem_container = null;
 var elem_text = null;
 
@@ -308,6 +326,8 @@ function remove_element() {
 
 browser.runtime.onMessage.addListener((request, sender, sendResponse) => {
   const { action, data } = request;
+  const saveCaption = data.saveCaption || false;
+
   if (action === "startCapture") {
       isCapturing = true;
       startRecording(data);
@@ -318,12 +338,20 @@ browser.runtime.onMessage.addListener((request, sender, sendResponse) => {
         socket.close();
         socket = null;
     }
-    
-    if (audioContext) {
-        audioContext.close();
-        audioContext = null;
-        mediaStream = null;
-        recorder = null;
+
+
+    if (saveCaption === true) {
+      if (lastIncompleteSegment && lastIncompleteSegment.text && lastIncompleteSegment.text.trim() !== "") {
+          if (allSegments.length === 0 || parseFloat(lastIncompleteSegment.start) >= parseFloat(allSegments[allSegments.length - 1].end)) {
+              allSegments.push({
+                  start: lastIncompleteSegment.start,
+                  end: lastIncompleteSegment.end,
+                  text: lastIncompleteSegment.text
+              });
+          }
+      }
+  
+      downloadSRT();
     }
 
     remove_element();
@@ -337,8 +365,25 @@ browser.runtime.onMessage.addListener((request, sender, sendResponse) => {
   } else if (action === "show_transcript"){
     if (!isCapturing) return;
     init_element();    
-    message = JSON.parse(data);
+    message = JSON.parse(data.data);
     message = message["segments"];
+
+    if (saveCaption === true) {
+      message.forEach(seg => {
+          if (seg.completed === true && 
+              (allSegments.length === 0 || parseFloat(seg.start) >= parseFloat(allSegments[allSegments.length - 1].end))) {
+              allSegments.push({
+                  start: seg.start,
+                  end: seg.end,
+                  text: seg.text
+              });
+              
+              lastIncompleteSegment = null;
+          } else if (seg.completed !== true) {
+              lastIncompleteSegment = seg;
+          }
+      });
+    }
     
     var text = '';
     for (var i = 0; i < message.length; i++) {

Audio-Transcription-Firefox/manifest.json

@@ -8,6 +8,9 @@
       "activeTab",
       "<all_urls>"
     ],
+    "web_accessible_resources": [
+      "audiopreprocessor.js"
+    ],
     "background": {
       "scripts": ["background.js"],
       "persistent": false

Audio-Transcription-Firefox/popup.html

@@ -19,6 +19,10 @@
     <input type="checkbox" id="useVadCheckbox">
     <label for="useVadCheckbox">Use Voice Activity Detection</label>
   </div>
+  <div class="checkbox-container">
+    <input type="checkbox" id="saveCaptionCheckbox">
+    <label for="saveCaption">Download SRT file at Stop Capture</label>
+  </div>
   <textarea id="waitTextBox" style="display: none;"></textarea>
   <div class="dropdown-container">
     <label for="languageDropdown">Select Language:</label>

Audio-Transcription-Firefox/popup.js

@@ -4,6 +4,7 @@ document.addEventListener("DOMContentLoaded", function() {
 
   const useServerCheckbox = document.getElementById("useServerCheckbox");
   const useVadCheckbox = document.getElementById("useVadCheckbox");
+  const saveCaptionCheckbox = document.getElementById("saveCaptionCheckbox");
   const languageDropdown = document.getElementById('languageDropdown');
   const taskDropdown = document.getElementById('taskDropdown');
   const modelSizeDropdown = document.getElementById('modelSizeDropdown');
@@ -41,6 +42,12 @@ document.addEventListener("DOMContentLoaded", function() {
     }
   });
 
+  browser.storage.local.get("saveCaptionState", ({ saveCaptionState }) => {
+    if (saveCaptionState !== undefined) {
+      saveCaptionCheckbox.checked = saveCaptionState;
+    }
+  });
+
   browser.storage.local.get("selectedLanguage", ({ selectedLanguage: storedLanguage }) => {
     if (storedLanguage !== undefined) {
       languageDropdown.value = storedLanguage;
@@ -85,6 +92,7 @@ document.addEventListener("DOMContentLoaded", function() {
               task: selectedTask,
               modelSize: selectedModelSize,
               useVad: useVadCheckbox.checked,
+              saveCaption: saveCaptionCheckbox.checked,
             } 
           });
         toggleCaptureButtons(true);
@@ -101,7 +109,7 @@ document.addEventListener("DOMContentLoaded", function() {
   stopButton.addEventListener("click", function() {
     browser.tabs.query({ active: true, currentWindow: true })
       .then(function(tabs) {
-        browser.tabs.sendMessage(tabs[0].id, { action: "stopCapture" })
+        browser.tabs.sendMessage(tabs[0].id, { action: "stopCapture", data: {saveCaption: saveCaptionCheckbox.checked, } })
           .then(function(response) {
             toggleCaptureButtons(false);
             browser.storage.local.set({ capturingState: { isCapturing: false } })
@@ -124,6 +132,7 @@ document.addEventListener("DOMContentLoaded", function() {
     stopButton.disabled = !isCapturing;
     useServerCheckbox.disabled = isCapturing;
     useVadCheckbox.disabled = isCapturing;
+    saveCaptionCheckbox.disabled = isCapturing;
     modelSizeDropdown.disabled = isCapturing;
     languageDropdown.disabled = isCapturing;
     taskDropdown.disabled = isCapturing; 
@@ -142,6 +151,11 @@ document.addEventListener("DOMContentLoaded", function() {
     browser.storage.local.set({ useVadState });
   });
 
+  saveCaptionCheckbox.addEventListener("change", () => {
+    const saveCaptionState = saveCaptionCheckbox.checked;
+    browser.storage.local.set({ saveCaptionState });
+  });
+
   languageDropdown.addEventListener('change', function() {
     if (languageDropdown.value === "") {
       selectedLanguage = null;

Audio-Transcription-iOS/AudioStream.swift

@@ -0,0 +1,229 @@
+//  AudioStream.swift
+//  Lecture2Quiz
+//
+//  Created by ParkMazorika on 4/27/25.
+//
+
+import AVFoundation
+
+/// Streams audio input to a WebSocket after converting and normalizing.
+class AudioStreamer {
+    private let engine = AVAudioEngine()
+    private let inputNode: AVAudioInputNode
+    private var inputFormat: AVAudioFormat?
+    private var isPaused: Bool = false
+    private var audioWebSocket: AudioWebSocket?
+    private var partialBuffer = Data()
+    private var isStreaming: Bool = false
+
+    private var bufferSize: AVAudioFrameCount = 1600  // ~100ms of audio
+    private var sampleRate: Double = 16000
+    private var channels: UInt32 = 1
+
+    private var converter: AVAudioConverter?
+
+    init(webSocket: AudioWebSocket) {
+        self.inputNode = engine.inputNode
+        self.audioWebSocket = webSocket
+
+        let inputFormat = inputNode.outputFormat(forBus: 0)
+        print("Input format: \(inputFormat)")
+
+        let outputFormat = AVAudioFormat(
+            commonFormat: .pcmFormatInt16,
+            sampleRate: 16000,
+            channels: 1,
+            interleaved: true
+        )!
+
+        self.converter = AVAudioConverter(from: inputFormat, to: outputFormat)
+        self.inputFormat = outputFormat
+    }
+
+    /// Configures the audio session for recording.
+    func configureAudioSession() {
+        let session = AVAudioSession.sharedInstance()
+        do {
+            try session.setCategory(.playAndRecord, mode: .default, options: [.allowBluetooth, .defaultToSpeaker])
+            try session.setPreferredSampleRate(48000)
+            try session.setPreferredInputNumberOfChannels(1)
+            try session.setMode(.videoChat)
+            try session.setActive(true, options: .notifyOthersOnDeactivation)
+            sampleRate = session.sampleRate
+            channels = UInt32(session.inputNumberOfChannels)
+            print("Sample rate: \(sampleRate)")
+            print("Input channels: \(channels)")
+        } catch {
+            print("Failed to configure audio session: \(error.localizedDescription)")
+        }
+    }
+
+    /// Starts capturing and streaming audio data.
+    func startStreaming() {
+        guard !isStreaming else {
+            print("Already streaming.")
+            return
+        }
+
+        configureAudioSession()
+
+        let format = AVAudioFormat(
+            commonFormat: .pcmFormatFloat32,
+            sampleRate: 48000,
+            channels: channels,
+            interleaved: true
+        )
+
+        guard let hardwareFormat = format else {
+            print("Failed to create audio format.")
+            return
+        }
+
+        self.inputFormat = hardwareFormat
+
+        inputNode.installTap(onBus: 0, bufferSize: bufferSize, format: hardwareFormat) { [weak self] buffer, _ in
+            self?.processAudioBuffer(buffer)
+        }
+
+        do {
+            try engine.start()
+            isStreaming = true
+            print("AVAudioEngine started.")
+        } catch {
+            print("Failed to start AVAudioEngine: \(error.localizedDescription)")
+        }
+    }
+
+    /// Converts and sends the audio buffer to the server via WebSocket.
+    func processAudioBuffer(_ buffer: AVAudioPCMBuffer) {
+        guard let converter = self.converter else {
+            print("Audio converter is nil.")
+            return
+        }
+
+        if let floatChannelData = buffer.floatChannelData {
+            let frameLength = Int(buffer.frameLength)
+            let channelData = Array(UnsafeBufferPointer(start: floatChannelData.pointee, count: frameLength))
+            let rms = sqrt(channelData.map { $0 * $0 }.reduce(0, +) / Float(frameLength))
+            print("Audio RMS: \(rms)")
+            if rms < 0.001 {
+                print("Warning: Input volume is too low.")
+            }
+        }
+
+        let outputFormat = AVAudioFormat(
+            commonFormat: .pcmFormatInt16,
+            sampleRate: 16000,
+            channels: 1,
+            interleaved: true
+        )!
+
+        guard let newBuffer = AVAudioPCMBuffer(pcmFormat: outputFormat, frameCapacity: 1600) else {
+            print("Failed to allocate PCM buffer.")
+            return
+        }
+
+        let inputBlock: AVAudioConverterInputBlock = { _, outStatus in
+            outStatus.pointee = .haveData
+            return buffer
+        }
+
+        var error: NSError?
+        converter.convert(to: newBuffer, error: &error, withInputFrom: inputBlock)
+
+        if let error = error {
+            print("Audio conversion failed: \(error.localizedDescription)")
+            return
+        }
+
+        print("Converted buffer frameLength: \(newBuffer.frameLength), sampleRate: \(newBuffer.format.sampleRate)")
+
+        if let audioData = convertToFloat32BytesLikePython(newBuffer) {
+            var completeData = partialBuffer + audioData
+            let chunkSize = 4096
+
+            while completeData.count >= chunkSize {
+                let chunk = completeData.prefix(chunkSize)
+                audioWebSocket?.sendDataToServer(chunk)
+                print("Sent 4096 bytes of audio.")
+                completeData.removeFirst(chunkSize)
+            }
+
+            partialBuffer = completeData
+        }
+    }
+
+    /// Converts the audio buffer to Float32 Data with RMS normalization and soft clipping.
+    func convertToFloat32BytesLikePython(_ buffer: AVAudioPCMBuffer) -> Data? {
+        guard let int16ChannelData = buffer.int16ChannelData else {
+            print("int16ChannelData is nil.")
+            return nil
+        }
+
+        let frameLength = Int(buffer.frameLength)
+        let channelPointer = int16ChannelData.pointee
+
+        var floatArray = [Float32](repeating: 0, count: frameLength)
+        for i in 0..<frameLength {
+            let int16Value = channelPointer[i]
+            floatArray[i] = Float32(Int16(littleEndian: int16Value)) / 32768.0
+        }
+
+        let rms = sqrt(floatArray.map { $0 * $0 }.reduce(0, +) / Float(frameLength))
+        let targetRMS: Float32 = 0.25
+        let gain = targetRMS / max(rms, 0.00001)
+
+        print("Original RMS: \(rms), applied gain: \(gain)")
+
+        for i in 0..<frameLength {
+            let scaled = floatArray[i] * gain
+            let clipped = tanh(scaled * 3.0)
+            floatArray[i] = clipped
+        }
+
+        let floatData = Data(bytes: floatArray, count: frameLength * MemoryLayout<Float32>.size)
+
+        if let minVal = floatArray.min(), let maxVal = floatArray.max() {
+            print("Float32 value range after normalization: \(minVal)...\(maxVal)")
+        }
+
+        print("Converted to Float32 data: \(floatData.count) bytes")
+        return floatData
+    }
+
+    /// Pauses audio streaming by removing the input tap.
+    func pauseStreaming() {
+        guard !isPaused else { return }
+        inputNode.removeTap(onBus: 0)
+        isPaused = true
+        print("Audio streaming paused.")
+    }
+
+    /// Resumes audio streaming by reinstalling the input tap.
+    func resumeStreaming() {
+        guard isPaused else { return }
+        guard let inputFormat = inputFormat else {
+            print("inputFormat is nil.")
+            return
+        }
+
+        inputNode.installTap(onBus: 0, bufferSize: bufferSize, format: inputFormat) { [weak self] buffer, _ in
+            self?.processAudioBuffer(buffer)
+        }
+        isPaused = false
+        print("Audio streaming resumed.")
+    }
+
+    /// Stops the AVAudioEngine and resets streaming state.
+    func stopStreaming() {
+        guard isStreaming else {
+            print("Already stopped.")
+            return
+        }
+
+        inputNode.removeTap(onBus: 0)
+        engine.stop()
+        isStreaming = false
+        print("AVAudioEngine stopped.")
+    }
+}

Audio-Transcription-iOS/AudioWebSocket.swift

@@ -0,0 +1,256 @@
+//
+//  RecordingViewModel.swift
+//  Lecture2Quiz
+//
+//  Created by ParkMazorika on 4/27/25.
+//
+
+
+import Foundation
+
+/// WebSocket client that connects to a transcription server and handles streaming, JSON messages, and retries.
+class AudioWebSocket: NSObject, URLSessionWebSocketDelegate {
+    private var webSocketTask: URLSessionWebSocketTask?
+    private var urlSession: URLSession!
+    private let host: String
+    private let port: Int
+    private var retryCount = 0
+    private let maxRetries = 3
+    private var uid: String
+    private let modelSize: String
+    private var pingTimer: Timer?
+    private var processedTexts = Set<String>()
+
+    var onServerReady: (() -> Void)?
+    var onTranscriptionReceived: ((String) -> Void)?
+
+    init(host: String, port: Int, modelSize: String = "medium") {
+        self.host = host
+        self.port = port
+        self.uid = UUID().uuidString
+        self.modelSize = modelSize
+        super.init()
+
+        self.urlSession = URLSession(
+            configuration: .default,
+            delegate: self,
+            delegateQueue: .main
+        )
+        connect()
+    }
+
+    /// Establishes a WebSocket connection with the configured server.
+    private func connect() {
+        guard retryCount <= maxRetries else {
+            print("Maximum reconnect attempts exceeded.")
+            return
+        }
+
+        let socketURL = port == 443 || port == 80
+            ? "wss://\(host)"
+            : "wss://\(host):\(port)"
+
+        guard let url = URL(string: socketURL) else {
+            print("Invalid URL: \(socketURL)")
+            return
+        }
+
+        webSocketTask = urlSession.webSocketTask(with: url)
+        webSocketTask?.resume()
+        print("Attempting WebSocket connection: \(socketURL)")
+
+        listen()
+        sendInitialJSON()
+        startPing()
+    }
+
+    /// Sends the initial JSON payload to identify and configure the session.
+    private func sendInitialJSON() {
+        let jsonPayload: [String: Any] = [
+            "uid": uid,
+            "language": "en",
+            "task": "transcribe",
+            "model": modelSize,
+            "use_vad": true,
+            "max_clients": 4,
+            "max_connection_time": 600
+        ]
+
+        do {
+            let jsonData = try JSONSerialization.data(withJSONObject: jsonPayload, options: [])
+            let jsonString = String(data: jsonData, encoding: .utf8) ?? ""
+            print("Sending config JSON: \(jsonString)")
+
+            webSocketTask?.send(.string(jsonString)) { [weak self] error in
+                if let error = error {
+                    print("Failed to send config JSON: \(error.localizedDescription)")
+                    self?.reconnect()
+                } else {
+                    print("Config JSON sent successfully.")
+                }
+            }
+        } catch {
+            print("JSON serialization error: \(error.localizedDescription)")
+        }
+    }
+
+    /// Sends audio data to the server.
+    func sendDataToServer(_ data: Data) {
+        guard isConnected else {
+            print("Not connected - skipping data send.")
+            reconnect()
+            return
+        }
+
+        webSocketTask?.send(.data(data)) { [weak self] error in
+            if let error = error {
+                print("Failed to send audio data: \(error.localizedDescription)")
+                self?.reconnect()
+            } else {
+                print("Sent audio data: \(data.count) bytes")
+            }
+        }
+    }
+
+    /// Returns true if the WebSocket is currently connected.
+    internal var isConnected: Bool {
+        webSocketTask?.state == .running
+    }
+
+    /// Attempts reconnection with exponential backoff.
+    private func reconnect() {
+        retryCount += 1
+        stopPing()
+        let delay = min(5.0, pow(2.0, Double(retryCount)))
+
+        DispatchQueue.global().asyncAfter(deadline: .now() + delay) { [weak self] in
+            print("Reconnecting... (\(self?.retryCount ?? 0)/\(self?.maxRetries ?? 0))")
+            self?.connect()
+        }
+    }
+
+    /// Starts listening for incoming messages from the server.
+    private func listen() {
+        webSocketTask?.receive { [weak self] result in
+            switch result {
+            case .success(let message):
+                self?.handleMessage(message)
+                self?.listen()
+            case .failure(let error):
+                print("Receive error: \(error.localizedDescription)")
+                self?.reconnect()
+            }
+        }
+    }
+
+    /// Handles incoming WebSocket messages (text or binary).
+    private func handleMessage(_ message: URLSessionWebSocketTask.Message) {
+        switch message {
+        case .data(let data):
+            print("Received binary data: \(data.count) bytes")
+
+        case .string(let text):
+            print("Received text message: \(text)")
+
+            guard let data = text.data(using: .utf8) else { return }
+
+            do {
+                if let json = try JSONSerialization.jsonObject(with: data) as? [String: Any] {
+                    if let status = json["status"] as? String {
+                        handleStatusMessage(status: status, message: json["message"] as? String)
+                        return
+                    }
+
+                    if let message = json["message"] as? String, message == "SERVER_READY" {
+                        print("Server is ready.")
+                        onServerReady?()
+                        return
+                    }
+
+                    if let segments = json["segments"] as? [[String: Any]] {
+                        let wrapped = ["segments": segments]
+                        let segmentData = try JSONSerialization.data(withJSONObject: wrapped, options: [])
+                        let segmentString = String(data: segmentData, encoding: .utf8)!
+                        onTranscriptionReceived?(segmentString)
+                        print("Transcription segments forwarded.")
+                    }
+                }
+            } catch {
+                print("JSON parsing error: \(error.localizedDescription)")
+            }
+
+        @unknown default:
+            print("Unknown message type received.")
+        }
+    }
+
+    /// Handles status message JSON from the server.
+    private func handleStatusMessage(status: String, message: String?) {
+        switch status {
+        case "WAIT":
+            print("Waiting: \(message ?? "")")
+        case "ERROR":
+            print("Error: \(message ?? "")")
+        case "WARNING":
+            print("Warning: \(message ?? "")")
+        default:
+            print("\(status): \(message ?? "")")
+        }
+    }
+
+    /// Sends the "END_OF_AUDIO" signal to the server.
+    func sendEndOfAudio() {
+        guard isConnected else {
+            print("Not connected - skipping END_OF_AUDIO.")
+            return
+        }
+
+        webSocketTask?.send(.string("END_OF_AUDIO")) { error in
+            if let error = error {
+                print("Failed to send END_OF_AUDIO: \(error.localizedDescription)")
+            } else {
+                print("END_OF_AUDIO sent.")
+            }
+        }
+    }
+
+    /// Gracefully closes the WebSocket connection.
+    func closeConnection() {
+        stopPing()
+        webSocketTask?.cancel(with: .normalClosure, reason: nil)
+        retryCount = maxRetries
+        print("WebSocket closed.")
+    }
+
+    /// Starts periodic ping to keep the WebSocket alive.
+    private func startPing() {
+        stopPing()
+        pingTimer = Timer.scheduledTimer(withTimeInterval: 15.0, repeats: true) { [weak self] _ in
+            self?.webSocketTask?.sendPing { error in
+                if let error = error {
+                    print("Ping failed: \(error.localizedDescription)")
+                } else {
+                    print("Ping sent successfully.")
+                }
+            }
+        }
+        RunLoop.main.add(pingTimer!, forMode: .common)
+    }
+
+    /// Stops the periodic ping timer.
+    private func stopPing() {
+        pingTimer?.invalidate()
+        pingTimer = nil
+    }
+
+    /// Called when the WebSocket is closed by the server.
+    func urlSession(_ session: URLSession,
+                    webSocketTask: URLSessionWebSocketTask,
+                    didCloseWith closeCode: URLSessionWebSocketTask.CloseCode,
+                    reason: Data?) {
+        let reasonString = String(data: reason ?? Data(), encoding: .utf8) ?? "No reason"
+        print("WebSocket closed - code: \(closeCode.rawValue), reason: \(reasonString)")
+        stopPing()
+        reconnect()
+    }
+}

Audio-Transcription-iOS/ContentView.swift

@@ -0,0 +1,99 @@
+//
+//  ContentView.swift
+//  WhisperLive_iOS_Client
+//
+//  Created by ParkMazorika on 6/17/25.
+//
+
+import SwiftUI
+
+/// A standalone view for recording and real-time transcription display.
+struct RecordingView: View {
+    var onDismiss: () -> Void
+    @StateObject private var recordingViewModel = AudioViewModel()
+    @State private var showSubmitView = false
+
+    var body: some View {
+        VStack(spacing: 0) {
+            // Stop button (only visible when recording)
+            HStack {
+                Spacer()
+                if recordingViewModel.isRecording {
+                    Button("Stop Recording") {
+                        recordingViewModel.stopRecording()
+                        recordingViewModel.finalizeTranscription()
+                        showSubmitView = true
+                    }
+                    .font(.headline)
+                    .padding()
+                    .foregroundColor(.gray)
+                }
+            }
+
+            // Transcription display
+            ScrollView {
+                VStack(spacing: 8) {
+                    ForEach(recordingViewModel.transcriptionList.indices, id: \.self) { index in
+                        Text(recordingViewModel.transcriptionList[index])
+                            .padding()
+                            .frame(maxWidth: .infinity, alignment: .leading)
+                            .background(Color.gray.opacity(0.1))
+                            .cornerRadius(8)
+                            .font(.system(size: 14, weight: .semibold))
+                    }
+                }
+                .padding(.horizontal)
+            }
+
+            Divider().padding(.top, 8)
+
+            // Timer and Record/Pause/Resume button
+            VStack(spacing: 16) {
+                Text(recordingViewModel.timeLabel)
+                    .font(.system(size: 40))
+
+                Button(action: {
+                    if recordingViewModel.isRecording {
+                        recordingViewModel.isPaused
+                            ? recordingViewModel.resumeRecording()
+                            : recordingViewModel.pauseRecording()
+                    } else {
+                        recordingViewModel.startRecording()
+                    }
+                }) {
+                    Image(systemName: recordingViewModel.isRecording
+                          ? (recordingViewModel.isPaused ? "play.circle.fill" : "pause.circle.fill")
+                          : "mic.circle.fill")
+                        .font(.system(size: 50))
+                        .foregroundStyle(.black)
+                }
+            }
+            .padding(.bottom, 40)
+        }
+        .padding(.top)
+        .background(Color(.systemBackground))
+        .overlay(
+            Group {
+                if recordingViewModel.isLoading {
+                    ZStack {
+                        Color.black.opacity(0.4).ignoresSafeArea()
+                        ProgressView("Processing...")
+                            .padding()
+                            .background(Color.white)
+                            .cornerRadius(10)
+                    }
+                }
+            }
+        )
+        .sheet(isPresented: $showSubmitView) {
+            //anotherView
+        }
+    }
+}
+
+#Preview("Recording View") {
+    RecordingView {
+        // Dummy dismiss handler
+        print("RecordingView dismissed")
+    }
+}

Audio-Transcription-iOS/README.md

@@ -0,0 +1,109 @@
+# Audio-Transcription-iOS
+
+This is an iOS client for [WhisperLive](https://github.com/collabora/WhisperLive), a real-time speech-to-text server based on OpenAI Whisper.  
+The app streams microphone audio to a WhisperLive server via WebSocket and displays live transcription results in real time.
+
+> ⚠️ This client is designed to work specifically with the [WhisperLive Python WebSocket server](https://github.com/collabora/WhisperLive?tab=readme-ov-file#running-the-server).  
+> Make sure the server is running and reachable from your iOS device.
+
+## Features
+
+- Real-time microphone capture with AVAudioEngine
+- Streaming to WhisperLive backend using WebSocket
+- Displays transcription as segments arrive
+- Start / Pause / Resume / Stop recording with SwiftUI interface
+- Final transcription view on stop
+
+## Requirements
+
+- iOS 15.0+
+- Swift 5.8+
+- AVFoundation (for microphone)
+- Working WhisperLive WebSocket server
+
+## Getting Started
+
+1. Clone the repository (your fork):
+
+    ```bash
+    git clone https://github.com/yourusername/whisperlive.git
+    cd whisperlive/Audio-Transcription-iOS
+    ```
+
+2. Open the `.xcodeproj` or `.xcodeworkspace` in Xcode
+
+3. Add the following to your `Info.plist`:
+
+    ```xml
+    <key>NSMicrophoneUsageDescription</key>
+    <string>This app requires microphone access for transcription.</string>
+    ```
+
+4. Run the app on a physical device (recommended)
+
+## Running on a Physical Device (with Free Apple ID)
+
+You can run this app on a real iPhone without a paid Apple Developer account. Follow these steps:
+
+### 1. Register a Free Apple ID in Xcode
+
+1. Open Xcode ▸ Settings… (or Preferences) ▸ **Accounts**
+2. Click the **+** button ▸ Select **Apple ID**
+3. Sign in with your Apple ID (a free one is fine)
+4. A "Personal Team" will be created automatically
+
+> ✅ You can deploy up to 3 apps on a physical device using a free Apple ID with a 7-day provisioning profile.
+
+---
+
+### 2. Set Up Signing in Your Project
+
+1. In Xcode, select your **project** in the Project Navigator
+2. Go to **TARGETS ▸ YourAppName ▸ Signing & Capabilities**
+3. Set **Team** to your Personal Team
+4. Set a unique **Bundle Identifier** (e.g., `com.yourname.whisperlive`)
+5. Make sure **Automatically manage signing** is checked
+6. If a red warning appears, click **"Resolve Issues"**
+
+---
+
+### 3. Connect and Trust Your iPhone
+
+1. Connect your iPhone via USB
+2. When prompted, tap **“Trust This Computer”** on your iPhone
+3. Make sure your iPhone appears in Xcode's device list
+
+---
+
+### 4. Enable Developer Mode on iPhone
+
+1. Press the **Build (▶︎)** button in Xcode
+2. Your iPhone will ask to enable **Developer Mode**
+3. On iPhone, go to:  
+   **Settings ▸ Privacy & Security ▸ Developer Mode**
+4. Enable it and restart the device if required
+
+---
+
+Now you can run and debug the app on your real device!
+
+
+
+## Folder Structure
+```
+Audio-Transcription-iOS/
+├── AudioViewModel.swift
+├── AudioStreamer.swift
+├── AudioWebSocket.swift
+├── RecordingView.swift
+├── WhisperLive_iOS_ClientApp.swift
+├── Info.plist
+├── README.md
+```
+
+## License
+
+MIT  
+This iOS client is provided as an open-source example to complement WhisperLive's real-time transcription ecosystem.
+
+

Audio-Transcription-iOS/RecordingViewModel.swift

@@ -0,0 +1,174 @@
+//
+//  RecordingViewModel.swift
+//  Lecture2Quiz
+//
+//  Created by ParkMazorika on 4/27/25.
+//
+
+import AVFoundation
+import Combine
+
+/// Represents a segment of transcribed audio with start/end timestamps and completion flag.
+struct TranscriptionSegment: Identifiable, Equatable {
+    var id = UUID()
+    var start: Double
+    var end: Double
+    var text: String
+    var completed: Bool
+}
+
+/// ViewModel responsible for managing audio recording and transcription logic.
+class AudioViewModel: ObservableObject {
+    @Published var isRecording = false            // Indicates if recording is active
+    @Published var isPaused = false               // Indicates if recording is currently paused
+    @Published var timeLabel = "00:00"            // Timer label formatted as mm:ss
+    @Published var transcriptionList: [String] = []  // Live transcription output
+    @Published var isLoading = false              // True while waiting for server response
+    @Published var finalScript: String = ""       // Final script from completed segments
+
+    private var timer: Timer?
+    private var elapsedTime: Int = 0
+
+    private var audioStreamer: AudioStreamer?     // Handles audio capture and streaming
+    private var audioWebSocket: AudioWebSocket?   // Manages WebSocket communication
+
+    private var segments: [TranscriptionSegment] = []  // Stores all transcription segments
+
+    init() {}
+
+    /// Starts audio recording and initializes WebSocket + AVAudioEngine.
+    func startRecording() {
+        let audioAPIUrl = "your server url"
+        audioWebSocket = AudioWebSocket(host: audioAPIUrl, port: 443)
+        audioStreamer = AudioStreamer(webSocket: audioWebSocket!)
+
+        isLoading = true
+
+        // Handle server transcription message
+        audioWebSocket?.onTranscriptionReceived = { [weak self] text in
+            self?.handleRawTranscriptionJSON(text)
+        }
+
+        // When server sends SERVER_READY
+        audioWebSocket?.onServerReady = { [weak self] in
+            guard let self = self else { return }
+            DispatchQueue.main.async {
+                self.isLoading = false
+                self.isRecording = true
+                self.isPaused = false
+                self.timeLabel = "00:00"
+                self.elapsedTime = 0
+                self.startTimer()
+                self.audioStreamer?.startStreaming()
+            }
+        }
+    }
+
+    /// Pauses the recording and stops the timer.
+    func pauseRecording() {
+        isPaused = true
+        audioStreamer?.pauseStreaming()
+        timer?.invalidate()
+    }
+
+    /// Resumes recording and restarts the timer.
+    func resumeRecording() {
+        isPaused = false
+        audioStreamer?.resumeStreaming()
+        startTimer()
+    }
+
+    /// Stops recording and finalizes connection to server.
+    func stopRecording() {
+        isRecording = false
+        isPaused = false
+        timer?.invalidate()
+
+        audioStreamer?.stopStreaming()
+        audioWebSocket?.sendEndOfAudio()
+        audioWebSocket?.onTranscriptionReceived = nil
+        audioWebSocket?.closeConnection()
+    }
+
+    /// Starts the recording timer (1-second interval).
+    private func startTimer() {
+        timer = Timer.scheduledTimer(withTimeInterval: 1.0, repeats: true) { _ in
+            self.elapsedTime += 1
+            let minutes = self.elapsedTime / 60
+            let seconds = self.elapsedTime % 60
+            self.timeLabel = String(format: "%02d:%02d", minutes, seconds)
+        }
+    }
+
+    /// Finalizes the transcription by joining all completed segments into one string.
+    func finalizeTranscription() {
+        isLoading = false
+        let completedText = segments
+            .filter { $0.completed }
+            .map { $0.text.trimmingCharacters(in: .whitespaces) }
+            .joined(separator: " ")
+        finalScript = completedText
+        print("Final transcript:\n\(finalScript)")
+    }
+
+    /// Handles incoming JSON from the server and updates UI state.
+    /// Supports both full JSON and raw string cases.
+    func handleRawTranscriptionJSON(_ jsonString: String) {
+        let trimmed = jsonString.trimmingCharacters(in: .whitespacesAndNewlines)
+        guard let data = trimmed.data(using: .utf8) else { return }
+
+        if trimmed.hasPrefix("{") {
+            // Parse JSON containing segment list
+            do {
+                if let dict = try JSONSerialization.jsonObject(with: data) as? [String: Any],
+                   let segmentDicts = dict["segments"] as? [[String: Any]] {
+
+                    for item in segmentDicts {
+                        guard let startStr = item["start"] as? String,
+                              let endStr = item["end"] as? String,
+                              let text = item["text"] as? String,
+                              let completed = item["completed"] as? Bool,
+                              let start = Double(startStr),
+                              let end = Double(endStr) else { continue }
+
+                        let newSegment = TranscriptionSegment(start: start, end: end, text: text, completed: completed)
+
+                        // Overwrite if already exists, else append
+                        if let index = self.segments.firstIndex(where: { $0.start == start }) {
+                            self.segments[index] = newSegment
+                        } else {
+                            self.segments.append(newSegment)
+                        }
+                    }
+
+                    // Update the UI
+                    DispatchQueue.main.async {
+                        let completedTexts = self.segments
+                            .filter { $0.completed }
+                            .sorted(by: { $0.start < $1.start })
+                            .map { $0.text.trimmingCharacters(in: .whitespaces) }
+
+                        let pendingText = self.segments
+                            .filter { !$0.completed }
+                            .sorted(by: { $0.start < $1.start })
+                            .map { $0.text.trimmingCharacters(in: .whitespaces) }
+                            .last ?? ""
+
+                        self.transcriptionList = completedTexts + (pendingText.isEmpty ? [] : [pendingText])
+                        self.finalScript = self.transcriptionList.joined(separator: " ")
+                    }
+                }
+            } catch {
+                print("JSON parsing error: \(error)")
+            }
+        } else {
+            // Handle raw text line
+            DispatchQueue.main.async {
+                if self.transcriptionList.last != trimmed {
+                    self.transcriptionList.append(trimmed)
+                    self.finalScript = self.transcriptionList.joined(separator: " ")
+                }
+            }
+        }
+    }
+}

Audio-Transcription-iOS/WhisperLive-iOS-Client-Info.plist

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
+<plist version="1.0">
+<dict>
+	<key>NSMicrophoneUsageDescription</key>
+	<string>This app requires microphone access for voice transcription.</string>
+</dict>
+</plist>

Audio-Transcription-iOS/WhisperLive_iOS_ClientApp.swift

@@ -0,0 +1,20 @@
+//
+//  WhisperLive_iOS_ClientApp.swift
+//  WhisperLive_iOS_Client
+//
+//  Created by 바견규 on 6/17/25.
+//
+
+import SwiftUI
+
+@main
+struct WhisperLive_iOS_ClientApp: App {
+    var body: some Scene {
+        WindowGroup {
+            RecordingView {
+                // Handle dismiss action here, or leave it empty for now
+                print("RecordingView dismissed")
+            }
+        }
+    }
+}

README.md

@@ -3,6 +3,8 @@
 <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>
+  <a href="https://www.youtube.com/watch?v=0f5oiG4oPWQ"><img
+  src="https://img.youtube.com/vi/0f5oiG4oPWQ/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>
@@ -11,8 +13,19 @@ This project is a real-time transcription application that uses the OpenAI Whisp
 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](#installation)
+- [Getting Started](#getting-started)
+- [Running the Server](#running-the-server)
+- [Running the Client](#running-the-client)
+- [Browser Extensions](#browser-extensions)
+- [Whisper Live Server in Docker](#whisper-live-server-in-docker)
+- [Future Work](#future-work)
+- [Blog Posts](#blog-posts)
+- [Contact](#contact)
+- [Citations](#citations)
+
 ## Installation
-- Install PyAudio
+- Install PortAudio
 ```bash
  bash scripts/setup.sh
 ```
@@ -22,22 +35,53 @@ input from microphone and pre-recorded audio files.
  pip install whisper-live
 ```
 
+
+- Install 3.12 venv on Fedora
+
+```bash
+sudo dnf install -y python3.12 python3.12-pip
+python3.12 -m venv whisper_env
+source whisper_env/bin/activate
+```
+
+
+### OpenAI REST interface
+
+#### Server
+
+```bash
+python3 run_server.py --port 9090 --backend faster_whisper --max_clients 4 --max_connection_time 600 --enable_rest --cors-origins="http://localhost:8080,http://127.0.0.1:8080"
+```
+
+#### Client
+
+```bash
+python3 client_openai.py $AUDIO_FILE
+```
+
+
+
 ### Setting up NVIDIA/TensorRT-LLM for TensorRT backend
 - Please follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) for setup of [NVIDIA/TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) and for building Whisper-TensorRT engine.
 
 ## Getting Started
-The server supports two backends `faster_whisper` and `tensorrt`. If running `tensorrt` backend follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md)
+The server supports 3 backends `faster_whisper`, `tensorrt` and `openvino`. If running `tensorrt` backend follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md)
 
 ### Running the Server
 - [Faster Whisper](https://github.com/SYSTRAN/faster-whisper) backend
 ```bash
 python3 run_server.py --port 9090 \
-                      --backend faster_whisper
+                      --backend faster_whisper \
+                      --max_clients 4 \
+                      --max_connection_time 600
   
-# running with custom model
+# running with custom model and cache_dir to save auto-converted ctranslate2 models
 python3 run_server.py --port 9090 \
                       --backend faster_whisper \
-                      -fw "/path/to/custom/faster/whisper/model"
+                      --max_clients 4 \
+                      --max_connection_time 600 \
+                      -fw "/path/to/custom/faster/whisper/model" \
+                      -c ~/.cache/whisper-live/
 ```
 
 - TensorRT backend. Currently, we recommend to only use the docker setup for TensorRT. Follow [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) which works as expected. Make sure to build your TensorRT Engines before running the server with TensorRT backend.
@@ -45,14 +89,29 @@ python3 run_server.py --port 9090 \
 # Run English only model
 python3 run_server.py -p 9090 \
                       -b tensorrt \
-                      -trt /home/TensorRT-LLM/examples/whisper/whisper_small_en
+                      -trt /home/TensorRT-LLM/examples/whisper/whisper_small_en \
+                      --max_clients 4 \
+                      --max_connection_time 600
 
 # Run Multilingual model
 python3 run_server.py -p 9090 \
                       -b tensorrt \
                       -trt /home/TensorRT-LLM/examples/whisper/whisper_small \
-                      -m
+                      -m \
+                      --max_clients 4 \
+                      --max_connection_time 600
 ```
+- Use `--max_clients` option to restrict the number of clients the server should allow. Defaults to 4.
+- Use `--max_connection_time` options to limit connection time for a client in seconds. Defaults to 600.
+- WhisperLive now supports the [OpenVINO](https://github.com/openvinotoolkit/openvino) backend for efficient inference on Intel CPUs, iGPU and dGPUs. Currently, we tested the models uploaded to [huggingface by OpenVINO](https://huggingface.co/OpenVINO?search_models=whisper).
+  - > **Docker Recommended:** Running WhisperLive with OpenVINO inside Docker automatically enables GPU support (iGPU/dGPU) without requiring additional host setup.
+  - > **Native (non-Docker) Use:** If you prefer running outside Docker, ensure the Intel drivers and OpenVINO runtime are installed and properly configured on your system. Refer to the documentation for [installing OpenVINO](https://docs.openvino.ai/2025/get-started/install-openvino.html?PACKAGE=OPENVINO_BASE&VERSION=v_2025_0_0&OP_SYSTEM=LINUX&DISTRIBUTION=PIP#).
+
+```
+python3 run_server.py -p 9090 -b openvino
+```
+
+
 #### 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
@@ -70,16 +129,24 @@ If you don't want this, set `--no_single_model`.
 
 
 ### Running the Client
-- Initializing the client with below parameters:
+
+Use the below command to run the client:
+```bash
+python3 run_client.py --files <audio-file-name>
+```
+This will connect to the localhost server running on port 9090 by default. Use flags `--server` and `--port` to use different configurations. The above command will transcribe audio file provided with `--files` flag.
+
+
+Here are the details of client instance implemented in `run_client.py` script:
   - `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`.
-  - `max_clients`: Specifies the maximum number of clients the server should allow. Defaults to 4.
-  - `max_connection_time`: Maximum connection time for each client in seconds. Defaults to 600.
   - `mute_audio_playback`: Whether to mute audio playback when transcribing an audio file. Defaults to False.
+  - `enable_translation`: Start translation thread on the server (from any to any).
+  - `target_language`: Server translation thread's target translation language.
 
 ```python
 from whisper_live.client import TranscriptionClient
@@ -92,9 +159,9 @@ client = TranscriptionClient(
   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
-  max_clients=4,
-  max_connection_time=600,
   mute_audio_playback=False,                          # Only used for file input, False by Default
+  enable_translation=True,
+  target_language="hi",
 )
 ```
 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.
@@ -121,7 +188,13 @@ client(hls_url="http://as-hls-ww-live.akamaized.net/pool_904/live/ww/bbc_1xtra/b
 
 ## Browser Extensions
 - 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.
+- 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 https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md
+
+## iOS Client
+
+Use WhisperLive on iOS with our native iOS client.  
+Refer to [`ios-client`](https://github.com/collabora/WhisperLive/tree/main/Audio-Transcription-iOS) and [`ios-client/README.md`](https://github.com/collabora/WhisperLive/blob/main/Audio-Transcription-iOS/README.md) for setup and usage instructions.
+
 
 ## Whisper Live Server in Docker
 - GPU
@@ -130,9 +203,10 @@ client(hls_url="http://as-hls-ww-live.akamaized.net/pool_904/live/ww/bbc_1xtra/b
   docker run -it --gpus all -p 9090:9090 ghcr.io/collabora/whisperlive-gpu:latest
   ```
 
-  - TensorRT. 
+  - TensorRT. Refer to [TensorRT_whisper readme](https://github.com/collabora/WhisperLive/blob/main/TensorRT_whisper.md) for setup and more tensorrt backend configurations.
   ```bash
-  docker run -p 9090:9090 --runtime=nvidia --gpus all --entrypoint /bin/bash -it ghcr.io/collabora/whisperlive-tensorrt
+  docker build . -f docker/Dockerfile.tensorrt -t whisperlive-tensorrt
+  docker run -p 9090:9090 --runtime=nvidia --entrypoint /bin/bash -it whisperlive-tensorrt
 
   # Build small.en engine
   bash build_whisper_tensorrt.sh /app/TensorRT-LLM-examples small.en        # float16
@@ -147,20 +221,30 @@ client(hls_url="http://as-hls-ww-live.akamaized.net/pool_904/live/ww/bbc_1xtra/b
                         --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_en_int4"
   ```
 
+  - OpenVINO
+  ```
+  docker run -it --device=/dev/dri -p 9090:9090 ghcr.io/collabora/whisperlive-openvino
+  ```
+
 - CPU
-```bash
-docker run -it -p 9090:9090 ghcr.io/collabora/whisperlive-cpu: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.
+  - Faster-whisper
+  ```bash
+  docker run -it -p 9090:9090 ghcr.io/collabora/whisperlive-cpu:latest
+  ```
 
 ## Future Work
-- [ ] Add translation to other languages on top of transcription.
-- [x] TensorRT backend for Whisper.
+- [x] Add translation to other languages on top of transcription.
+
+## Blog Posts
+- [Transforming speech technology with WhisperLive](https://www.collabora.com/news-and-blog/blog/2024/05/28/transforming-speech-technology-with-whisperlive/)
+- [WhisperFusion: Ultra-low latency conversations with an AI chatbot](https://www.collabora.com/news-and-blog/news-and-events/whisperfusion-ultra-low-latency-conversations-with-an-ai-chatbot.html) powered by WhisperLive
+- [Breaking language barriers 2.0: Moving closer towards fully reliable, production-ready Hindi ASR](https://www.collabora.com/news-and-blog/news-and-events/breaking-language-barriers-20-moving-closer-production-ready-hindi-asr.html) which is used in WhisperLive for hindi.
 
 ## Contact
 
 We are available to help you with both Open Source and proprietary AI projects. You can reach us via the Collabora website or [vineet.suryan@collabora.com](mailto:vineet.suryan@collabora.com) and [marcus.edel@collabora.com](mailto:marcus.edel@collabora.com).
 
+
 ## Citations
 ```bibtex
 @article{Whisper

TensorRT_whisper.md

@@ -1,6 +1,6 @@
 # WhisperLive-TensorRT
 We have only tested the TensorRT backend in docker so, we recommend docker for a smooth TensorRT backend setup.
-**Note**: We use `tensorrt_llm==0.15.0.dev2024111200`
+**Note**: We use `tensorrt_llm==0.18.2`
 
 ## Installation
 - Install [docker](https://docs.docker.com/engine/install/)
@@ -8,7 +8,8 @@ We have only tested the TensorRT backend in docker so, we recommend docker for a
 
 - Run WhisperLive TensorRT in docker
 ```bash
-docker run -p 9090:9090 --runtime=nvidia --gpus all --entrypoint /bin/bash -it ghcr.io/collabora/whisperlive-tensorrt:latest
+docker build . -f docker/Dockerfile.tensorrt -t whisperlive-tensorrt
+docker run -p 9090:9090 --runtime=nvidia --gpus all --entrypoint /bin/bash -it whisperlive-tensorrt
 ```
 
 ## Whisper TensorRT Engine
@@ -36,3 +37,11 @@ python3 run_server.py --port 9090 \
                       --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_float16" \
                       --trt_multilingual
 ```
+
+By default trt_backend uses cpp_session, to use python session pass `--trt_py_session` to run_server.py
+```bash
+python3 run_server.py --port 9090 \
+                      --backend tensorrt \
+                      --trt_model_path "/app/TensorRT-LLM-examples/whisper/whisper_small_float16" \
+                      --trt_py_session
+```

client_openai.py

@@ -0,0 +1,38 @@
+import sys
+import requests
+
+if len(sys.argv) < 2:
+    print("Usage: python transcribe_file.py <path_to_audio_file>")
+    sys.exit(1)
+
+audio_file = sys.argv[1]
+
+# Configuration
+host = "localhost"
+port = 8000  # Default REST port; change if you used --rest_port
+url = f"http://{host}:{port}/v1/audio/transcriptions"
+model = "small"  # Or "whisper-1" (mapped to small internally)
+language = "en"  # Or "hi" for Hindi
+response_format = "json"  # Options: "json", "text", "verbose_json", "srt", "vtt"
+
+# Prepare the request
+files = {"file": open(audio_file, "rb")}
+data = {
+    "model": model,
+    "language": language,
+    "response_format": response_format,
+    # Optional: Add "prompt" for style guidance, "temperature" (0-1), etc.
+}
+
+# Send the request
+response = requests.post(url, files=files, data=data)
+
+if response.status_code == 200:
+    if response_format == "json" or response_format == "verbose_json":
+        result = response.json()
+        print("Transcript:", result.get("text", "No text found"))
+        # If you need translation, post-process here (e.g., using another API like Google Translate)
+    else:
+        print("Transcript:", response.text)
+else:
+    print("Error:", response.status_code, response.json().get("error", "Unknown error"))

docker/Dockerfile.openvino

@@ -0,0 +1,19 @@
+FROM openvino/ubuntu22_runtime:latest
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+USER root
+
+RUN apt update && apt install -y portaudio19-dev python-is-python3 && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --no-cache-dir -U "pip>=24"
+
+RUN mkdir /app
+WORKDIR /app
+
+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", "--backend", "openvino"]

docker/Dockerfile.tensorrt

@@ -1,19 +1,19 @@
-FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS base
+FROM nvidia/cuda:12.8.1-base-ubuntu22.04 AS base
 
 ARG DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get update && apt-get install -y \
     python3.10 python3-pip openmpi-bin libopenmpi-dev git git-lfs wget \
+    && apt install python-is-python3 \
+    && pip install --upgrade pip setuptools \
     && rm -rf /var/lib/apt/lists/*
 
 FROM base AS devel
-RUN pip3 install --no-cache-dir -U tensorrt_llm==0.15.0.dev2024111200 --extra-index-url https://pypi.nvidia.com
+RUN pip install --no-cache-dir -U tensorrt_llm==0.18.2 --extra-index-url https://pypi.nvidia.com
 WORKDIR /app
-RUN git clone https://github.com/NVIDIA/TensorRT-LLM.git && cd TensorRT-LLM && \
-    git checkout c629546ce429623c8a163633095230154a6f0574 && cd ../ && \
-    mv TensorRT-LLM/examples ./TensorRT-LLM-examples && \
-    rm -rf TensorRT-LLM
-
+RUN git clone -b v0.18.2 https://github.com/NVIDIA/TensorRT-LLM.git \
+    && mv TensorRT-LLM/examples ./TensorRT-LLM-examples \
+    && rm -rf TensorRT-LLM
 
 FROM devel AS release
 WORKDIR /app
@@ -25,7 +25,6 @@ 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
-RUN pip install pynvml==11.5.0
 COPY whisper_live ./whisper_live
 COPY scripts/build_whisper_tensorrt.sh .
 COPY run_server.py .

requirements/server.txt

@@ -1,4 +1,4 @@
-faster-whisper==1.1.0
+faster-whisper==1.2.0
 websockets
 onnxruntime==1.17.0
 numba
@@ -9,5 +9,19 @@ av
 jiwer
 evaluate
 numpy<2
-openai-whisper==20240930
-tokenizers==0.20.3
+openai-whisper==20250625
+tokenizers==0.20.3
+transformers[torch]
+sentencepiece
+
+# openvino
+librosa
+openvino
+openvino-genai
+openvino-tokenizers
+optimum 
+optimum-intel
+
+fastapi
+uvicorn
+python-multipart

run_client.py

@@ -0,0 +1,98 @@
+from pathlib import Path
+import sys
+from whisper_live.client import TranscriptionClient
+import argparse
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--port', '-p',
+                          type=int,
+                          default=9090,
+                          help="Websocket port to run the server on.")
+    parser.add_argument('--server', '-s',
+                          type=str,
+                          default='localhost',
+                          help='hostname or ip address of server')
+    parser.add_argument('--files', '-f',
+                          type=str,
+                          nargs='+',
+                          help='Files to transcribe, separated by spaces. '
+                              'If not provided, will use microphone input.')
+    parser.add_argument('--output_file', '-o',
+                          type=str,
+                          default='./output_recording.wav',
+                          help='output recording filename, only used for microphone input.')
+    parser.add_argument('--model', '-m',
+                          type=str,
+                          default='small',
+                          help='Model to use for transcription, e.g., "tiny, small.en, large-v3".')
+    parser.add_argument('--lang', '-l',
+                          type=str,
+                          default='en',
+                          help='Language code for transcription, e.g., "en" for English.')
+    parser.add_argument('--translate', '-t',
+                          action='store_true',
+                          help='Enable translation of the transcription output.')
+    parser.add_argument('--mute_audio_playback', '-a',
+                          action='store_true',
+                          help='Mute audio playback during transcription.') 
+    parser.add_argument('--save_output_recording', '-r',
+                          action='store_true',
+                          help='Save the output recording, only used for microphone input.')
+    parser.add_argument('--enable_translation',
+                          action='store_true',
+                          help='Enable translation of the transcription output.')
+    parser.add_argument('--target_language', '-tl',
+                          type=str,
+                          default='fr',
+                          help='Target language for translation, e.g., "fr" for French.')
+    parser.add_argument('--enable_timestamps',
+                          action='store_true',
+                          help='Show transcription with timestamps')
+    parser.add_argument('--n_display_segments',
+                          type=int,
+                          default=4,
+                          help='Number of transcript segments to display in terminal (default: 4).')
+
+    args = parser.parse_args()
+
+    client = TranscriptionClient(
+        args.server,
+        args.port,
+        lang=args.lang,
+        translate=args.translate,
+        model=args.model,                                  # also support hf_model => `Systran/faster-whisper-small`
+        use_vad=True,
+        save_output_recording=args.save_output_recording,  # Only used for microphone input, False by Default
+        output_recording_filename=args.output_file,        # Only used for microphone input
+        mute_audio_playback=args.mute_audio_playback,      # Only used for file input, False by Default
+        enable_translation=args.enable_translation,        # Enable translation of the transcription output
+        target_language=args.target_language,              # Target language for translation, e.g., "fr
+        enable_timestamps=args.enable_timestamps,
+        display_segments=args.n_display_segments,
+    )
+
+    if args.files is None:
+        client()
+        sys.exit(0)
+
+    # Validate audio files
+    valid_files = []
+    for file_path in args.files:
+        path = Path(file_path)
+        if path.exists() and path.is_file():
+            valid_files.append(str(path))
+        else:
+            print(f"Warning: File not found: {file_path}")
+
+    if not valid_files:
+        print("Error: No valid audio files found!")
+        sys.exit(1)
+
+    print(f"Found {len(valid_files)} audio file(s) to stream:")
+    for file_path in valid_files:
+        print(f"  - {file_path}")
+
+    for f in valid_files:
+        client(f)

run_server.py

@@ -1,5 +1,14 @@
 import argparse
 import os
+import threading
+import logging
+from fastapi import FastAPI
+from fastapi import UploadFile, Form
+import uvicorn
+import tempfile
+import shutil
+import json
+from starlette.responses import PlainTextResponse, JSONResponse
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -10,7 +19,7 @@ if __name__ == "__main__":
     parser.add_argument('--backend', '-b',
                         type=str,
                         default='faster_whisper',
-                        help='Backends from ["tensorrt", "faster_whisper"]')
+                        help='Backends from ["tensorrt", "faster_whisper", "openvino"]')
     parser.add_argument('--faster_whisper_custom_model_path', '-fw',
                         type=str, default=None,
                         help="Custom Faster Whisper Model")
@@ -21,6 +30,9 @@ if __name__ == "__main__":
     parser.add_argument('--trt_multilingual', '-m',
                         action="store_true",
                         help='Boolean only for TensorRT model. True if multilingual.')
+    parser.add_argument('--trt_py_session',
+                        action="store_true",
+                        help='Boolean only for TensorRT model. Use python session or cpp session, By default uses Cpp.')
     parser.add_argument('--omp_num_threads', '-omp',
                         type=int,
                         default=1,
@@ -28,6 +40,50 @@ if __name__ == "__main__":
     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.')
+    parser.add_argument('--max_clients',
+                        type=int,
+                        default=4,
+                        help='Maximum clients supported by the server.')
+    parser.add_argument('--max_connection_time',
+                        type=int,
+                        default=300,
+                        help='The maximum duration (in seconds) a client can stay connected. Defaults to 300 seconds (5 minutes)')
+    parser.add_argument('--cache_path', '-c',
+                        type=str,
+                        default="~/.cache/whisper-live/",
+                        help='Path to cache the converted ctranslate2 models.')
+    parser.add_argument(
+        "--rest_port", type=int, default=8000, help="Port for the REST API server."
+    )
+    parser.add_argument(
+        "--enable_rest",
+        action="store_true",
+        help="Enable the OpenAI-compatible REST API endpoint.",
+    )
+    parser.add_argument(
+        '--cors-origins',
+        type=str,
+        default=None,
+        help="Comma-separated list of allowed CORS origins (e.g., 'http://localhost:3000,http://example.com'). Defaults to localhost/127.0.0.1 on the WebSocket port."
+    )
+    parser.add_argument(
+        '--batch_inference',
+        action='store_true',
+        help='Enable batched GPU inference for concurrent sessions. '
+             'Batches multiple sessions into a single GPU call for higher throughput.'
+    )
+    parser.add_argument(
+        '--batch_max_size',
+        type=int,
+        default=8,
+        help='Maximum batch size for batched inference (default: 8).'
+    )
+    parser.add_argument(
+        '--batch_window_ms',
+        type=int,
+        default=50,
+        help='Maximum time in ms to wait for batch to fill (default: 50).'
+    )
     args = parser.parse_args()
 
     if args.backend == "tensorrt":
@@ -46,5 +102,15 @@ if __name__ == "__main__":
         faster_whisper_custom_model_path=args.faster_whisper_custom_model_path,
         whisper_tensorrt_path=args.trt_model_path,
         trt_multilingual=args.trt_multilingual,
+        trt_py_session=args.trt_py_session,
         single_model=not args.no_single_model,
-    )
+        max_clients=args.max_clients,
+        max_connection_time=args.max_connection_time,
+        cache_path=args.cache_path,
+        rest_port=args.rest_port,
+        enable_rest=args.enable_rest,
+        cors_origins=args.cors_origins,
+        batch_enabled=args.batch_inference,
+        batch_max_size=args.batch_max_size,
+        batch_window_ms=args.batch_window_ms,
+    )

scripts/build_whisper_tensorrt.sh

@@ -54,7 +54,7 @@ download_and_build_model() {
     local inference_precision="float16"
     local weight_only_precision="${2:-float16}"
     local max_beam_width=4
-    local max_batch_size=1
+    local max_batch_size=4
 
     echo "Downloading $model_name..."
     # wget --directory-prefix=assets "$model_url"
@@ -80,7 +80,6 @@ download_and_build_model() {
         --checkpoint_dir "${checkpoint_dir}/encoder" \
         --output_dir "${output_dir}/encoder" \
         --moe_plugin disable \
-        --enable_xqa disable \
         --max_batch_size "$max_batch_size" \
         --gemm_plugin disable \
         --bert_attention_plugin "$inference_precision" \
@@ -92,11 +91,10 @@ download_and_build_model() {
         --checkpoint_dir "${checkpoint_dir}/decoder" \
         --output_dir "${output_dir}/decoder" \
         --moe_plugin disable \
-        --enable_xqa disable \
         --max_beam_width "$max_beam_width" \
         --max_batch_size "$max_batch_size" \
-        --max_seq_len 200 \
-        --max_input_len 14 \
+        --max_seq_len 225 \
+        --max_input_len 32 \
         --max_encoder_input_len 3000 \
         --gemm_plugin "$inference_precision" \
         --bert_attention_plugin "$inference_precision" \

scripts/setup.sh

@@ -1,3 +1,32 @@
-#! /bin/bash
+#!/bin/bash
 
-apt-get install portaudio19-dev wget -y
+# Detect the operating system
+if [[ "$OSTYPE" == "darwin"* ]]; then
+    # macOS
+    echo "Detected macOS, using Homebrew for installation"
+
+    # Check if Homebrew is installed
+    if ! command -v brew &> /dev/null; then
+        echo "Homebrew not found. Please install Homebrew first: https://brew.sh/"
+        exit 1
+    fi
+
+    # Install packages using Homebrew
+    brew install portaudio wget
+elif [[ "$OSTYPE" == "linux-gnu"* ]]; then
+    # Linux
+    if [[ -f /etc/os-release ]]; then
+        source /etc/os-release
+    fi
+
+    if [[ "${ID:-}" == "fedora" ]]; then
+        echo "Detected Fedora, using dnf for installation"
+        dnf install -y portaudio-devel wget
+    else
+        echo "Detected Linux (assuming Debian/Ubuntu), using apt-get for installation"
+        apt-get install -y portaudio19-dev wget
+    fi
+else
+    echo "Unsupported operating system: $OSTYPE"
+    exit 1
+fi

setup.py

@@ -43,18 +43,25 @@ setup(
     ),
     install_requires=[
         "PyAudio",
-        "faster-whisper==1.1.0",
+        "faster-whisper==1.2.0",
         "torch",
         "torchaudio",
         "websockets",
-        "onnxruntime==1.16.0",
+        "onnxruntime==1.17.0",
         "scipy",
         "websocket-client",
         "numba",
-        "openai-whisper==20240930",
+        "openai-whisper==20250625",
         "kaldialign",
         "soundfile",
-        "tokenizers==0.20.3"
+        "tokenizers==0.20.3",
+        "librosa",
+        "numpy==1.26.4",
+        "openvino",
+        "openvino-genai",
+        "openvino-tokenizers",
+        "optimum", 
+        "optimum-intel",
     ],
-    python_requires=">=3.8"
+    python_requires=">=3.9"
 )

tests/test_batch_inference.py

@@ -0,0 +1,163 @@
+import time
+import unittest
+from unittest import mock
+from unittest.mock import MagicMock
+
+import numpy as np
+
+from whisper_live.batch_inference import BatchInferenceWorker, BatchRequest
+
+
+class TestBatchInferenceWorker(unittest.TestCase):
+    def setUp(self):
+        self.mock_transcriber = MagicMock()
+        self.worker = BatchInferenceWorker(
+            transcriber=self.mock_transcriber,
+            max_batch_size=8,
+            batch_window_ms=200,
+        )
+        self.worker.start()
+
+    def tearDown(self):
+        self.worker.stop()
+
+    def _make_audio(self, duration_s=1.0):
+        return np.random.randn(int(16000 * duration_s)).astype(np.float32)
+
+    def test_single_request_uses_transcribe(self):
+        """Single request should fall back to transcriber.transcribe()."""
+        fake_segment = MagicMock()
+        fake_info = MagicMock()
+        self.mock_transcriber.transcribe.return_value = ([fake_segment], fake_info)
+
+        req = BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+        self.worker.submit(req)
+        req.future.wait(timeout=5)
+
+        self.assertTrue(req.future.is_set())
+        self.assertIsNone(req.error)
+        self.assertEqual(req.result, [fake_segment])
+        self.assertEqual(req.info, fake_info)
+        self.mock_transcriber.transcribe.assert_called_once()
+
+    @mock.patch('whisper_live.batch_inference.get_suppressed_tokens', return_value=[-1])
+    @mock.patch('whisper_live.batch_inference.Tokenizer')
+    def test_multiple_requests_batched(self, mock_tokenizer_cls, mock_suppress):
+        """Multiple concurrent requests should go through the batched GPU path."""
+        # Mock tokenizer
+        mock_tok = MagicMock()
+        mock_tok.decode.return_value = "hello world"
+        mock_tokenizer_cls.return_value = mock_tok
+
+        # Mock feature extractor
+        self.mock_transcriber.feature_extractor.return_value = np.zeros(
+            (80, 3000), dtype=np.float32
+        )
+        self.mock_transcriber.feature_extractor.sampling_rate = 16000
+
+        # Mock encode
+        self.mock_transcriber.encode.return_value = np.zeros(
+            (3, 1500, 512), dtype=np.float32
+        )
+
+        # Mock model.generate — one result per item
+        gen_result = MagicMock()
+        gen_result.sequences_ids = [[50257, 50362, 1234, 50256]]
+        gen_result.scores = [np.float32(-1.0)]
+        gen_result.no_speech_prob = 0.1
+        self.mock_transcriber.model.generate.return_value = [gen_result] * 3
+
+        # Mock remaining model attributes
+        self.mock_transcriber.model.is_multilingual = False
+        self.mock_transcriber.max_length = 448
+        self.mock_transcriber.frames_per_second = 50
+        self.mock_transcriber.get_prompt.return_value = [50258]
+        self.mock_transcriber._split_segments_by_timestamps.return_value = (
+            [{"start": 0.0, "end": 1.0, "tokens": [1234], "seek": 0}],
+            None,
+            None,
+        )
+
+        requests = [
+            BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+            for _ in range(3)
+        ]
+        for req in requests:
+            self.worker.submit(req)
+        for req in requests:
+            req.future.wait(timeout=5)
+
+        for req in requests:
+            self.assertTrue(req.future.is_set())
+            self.assertIsNone(req.error)
+            self.assertIsNotNone(req.result)
+
+        # Verify the batched encode path was used (not transcribe)
+        self.mock_transcriber.encode.assert_called()
+        self.mock_transcriber.transcribe.assert_not_called()
+
+    def test_error_propagation(self):
+        """Transcriber errors should propagate to the request without crashing the worker."""
+        self.mock_transcriber.transcribe.side_effect = RuntimeError("GPU OOM")
+
+        req = BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+        self.worker.submit(req)
+        req.future.wait(timeout=5)
+
+        self.assertTrue(req.future.is_set())
+        self.assertIsInstance(req.error, RuntimeError)
+        self.assertIn("GPU OOM", str(req.error))
+
+        # Worker should still be alive — submit another request
+        self.mock_transcriber.transcribe.side_effect = None
+        self.mock_transcriber.transcribe.return_value = ([MagicMock()], MagicMock())
+
+        req2 = BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+        self.worker.submit(req2)
+        req2.future.wait(timeout=5)
+
+        self.assertIsNone(req2.error)
+        self.assertIsNotNone(req2.result)
+
+    def test_worker_stop(self):
+        """Worker thread should exit cleanly when stop() is called."""
+        self.assertTrue(self.worker._thread.is_alive())
+        self.worker.stop()
+        self.assertFalse(self.worker._thread.is_alive())
+
+    def test_batch_respects_max_size(self):
+        """Batches should not exceed max_batch_size."""
+        self.worker.stop()  # Stop the default worker
+
+        observed_batch_sizes = []
+        original_process = BatchInferenceWorker._process_batch
+
+        def tracking_process(self_inner, batch):
+            observed_batch_sizes.append(len(batch))
+            original_process(self_inner, batch)
+
+        self.worker = BatchInferenceWorker(
+            transcriber=self.mock_transcriber,
+            max_batch_size=2,
+            batch_window_ms=100,
+        )
+
+        self.mock_transcriber.transcribe.return_value = ([MagicMock()], MagicMock())
+
+        with mock.patch.object(
+            BatchInferenceWorker, '_process_batch', tracking_process
+        ):
+            self.worker.start()
+
+            requests = [
+                BatchRequest(audio=self._make_audio(), language="en", use_vad=False)
+                for _ in range(4)
+            ]
+            for req in requests:
+                self.worker.submit(req)
+            for req in requests:
+                req.future.wait(timeout=5)
+
+        for size in observed_batch_sizes:
+            self.assertLessEqual(size, 2)
+        self.assertTrue(all(req.future.is_set() for req in requests))

tests/test_client.py

@@ -49,8 +49,12 @@ class TestClientCallbacks(BaseTestCase):
             "task": self.client.task,
             "model": self.client.model,
             "use_vad": True,
-            "max_clients": 4,
-            "max_connection_time": 600,
+            "send_last_n_segments": 10,
+            "no_speech_thresh": 0.45,
+            "clip_audio": False,
+            "same_output_threshold": 10,
+            "enable_translation": False,
+            "target_language": "fr",
         })
         self.client.on_open(self.mock_ws_app)
         self.mock_ws_app.send.assert_called_with(expected_message)

tests/test_server.py

@@ -42,6 +42,8 @@ class TestGetWaitTime(unittest.TestCase):
 class TestServerConnection(unittest.TestCase):
     def setUp(self):
         self.server = TranscriptionServer()
+        self.server.client_manager = ClientManager(max_clients=4, max_connection_time=600)
+        self.server.cache_path = "~/.cache/whisper-live/"
 
     @mock.patch('websockets.WebSocketCommonProtocol')
     def test_connection(self, mock_websocket):

tests/test_vad.py

@@ -1,6 +1,6 @@
 import unittest
 import numpy as np
-from whisper_live.tensorrt_utils import load_audio
+from whisper_live.transcriber.tensorrt_utils import load_audio
 from whisper_live.vad import VoiceActivityDetector
 
 

whisper_live/__init__.py

@@ -0,0 +1,3 @@
+from whisper_live.__version__ import __version__
+
+__all__ = ['__version__']

whisper_live/__version__.py

@@ -1 +1 @@
-__version__ = "0.6.2"
+__version__ = "0.8.0"

whisper_live/backend/base.py

@@ -0,0 +1,379 @@
+import json
+import logging
+import threading
+import time
+import queue
+import numpy as np
+
+
+class ServeClientBase(object):
+    RATE = 16000
+    SERVER_READY = "SERVER_READY"
+    DISCONNECT = "DISCONNECT"
+
+    client_uid: str
+    """A unique identifier for the client."""
+    websocket: object
+    """The WebSocket connection for the client."""
+    send_last_n_segments: int
+    """Number of most recent segments to send to the client."""
+    no_speech_thresh: float
+    """Segments with no speech probability above this threshold will be discarded."""
+    clip_audio: bool
+    """Whether to clip audio with no valid segments."""
+    same_output_threshold: int
+    """Number of repeated outputs before considering it as a valid segment."""
+
+    def __init__(
+        self,
+        client_uid,
+        websocket,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+        translation_queue=None,
+    ):
+        self.client_uid = client_uid
+        self.websocket = websocket
+        self.send_last_n_segments = send_last_n_segments
+        self.no_speech_thresh = no_speech_thresh
+        self.clip_audio = clip_audio
+        self.same_output_threshold = same_output_threshold
+
+        self.frames = b""
+        self.timestamp_offset = 0.0
+        self.frames_np = None
+        self.frames_offset = 0.0
+        self.text = []
+        self.current_out = ""
+        self.prev_out = ""
+        self.exit = False
+        self.same_output_count = 0
+        self.transcript = []
+        self.end_time_for_same_output = None
+        self.translation_queue = translation_queue
+
+        # threading
+        self.lock = threading.Lock()
+
+    def speech_to_text(self):
+        """
+        Process an audio stream in an infinite loop, continuously transcribing the speech.
+
+        This method continuously receives audio frames, performs real-time transcription, and sends
+        transcribed segments to the client via a WebSocket connection.
+
+        If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
+        It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
+        are sent to the client in real-time, and a history of segments is maintained to provide context.
+
+        Raises:
+            Exception: If there is an issue with audio processing or WebSocket communication.
+
+        """
+        while True:
+            if self.exit:
+                logging.info("Exiting speech to text thread")
+                break
+
+            if self.frames_np is None:
+                continue
+
+            if self.clip_audio:
+                self.clip_audio_if_no_valid_segment()
+
+            input_bytes, duration = self.get_audio_chunk_for_processing()
+            if duration < 1.0:
+                time.sleep(0.1)     # wait for audio chunks to arrive
+                continue
+            try:
+                input_sample = input_bytes.copy()
+                result = self.transcribe_audio(input_sample)
+
+                if result is None or self.language is None:
+                    self.timestamp_offset += duration
+                    time.sleep(0.25)    # wait for voice activity, result is None when no voice activity
+                    continue
+                self.handle_transcription_output(result, duration)
+
+            except Exception as e:
+                logging.error(f"[ERROR]: Failed to transcribe audio chunk: {e}")
+                time.sleep(0.01)
+
+    def transcribe_audio(self):
+        raise NotImplementedError
+
+    def handle_transcription_output(self, result, duration):
+        raise NotImplementedError
+    
+    def format_segment(self, start, end, text, completed=False):
+        """
+        Formats a transcription segment with precise start and end times alongside the transcribed text.
+
+        Args:
+            start (float): The start time of the transcription segment in seconds.
+            end (float): The end time of the transcription segment in seconds.
+            text (str): The transcribed text corresponding to the segment.
+
+        Returns:
+            dict: A dictionary representing the formatted transcription segment, including
+                'start' and 'end' times as strings with three decimal places and the 'text'
+                of the transcription.
+        """
+        return {
+            'start': "{:.3f}".format(start),
+            'end': "{:.3f}".format(end),
+            'text': text,
+            'completed': completed
+        }
+
+    def add_frames(self, frame_np):
+        """
+        Add audio frames to the ongoing audio stream buffer.
+
+        This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
+        of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
+        to prevent excessive memory usage.
+
+        If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
+        of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
+        audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.
+
+        Args:
+            frame_np (numpy.ndarray): The audio frame data as a NumPy array.
+
+        """
+        self.lock.acquire()
+        if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
+            self.frames_offset += 30.0
+            self.frames_np = self.frames_np[int(30*self.RATE):]
+            # check timestamp offset(should be >= self.frame_offset)
+            # this basically means that there is no speech as timestamp offset hasnt updated
+            # and is less than frame_offset
+            if self.timestamp_offset < self.frames_offset:
+                self.timestamp_offset = self.frames_offset
+        if self.frames_np is None:
+            self.frames_np = frame_np.copy()
+        else:
+            self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0)
+        self.lock.release()
+
+    def clip_audio_if_no_valid_segment(self):
+        """
+        Update the timestamp offset based on audio buffer status.
+        Clip audio if the current chunk exceeds 30 seconds, this basically implies that
+        no valid segment for the last 30 seconds from whisper
+        """
+        with self.lock:
+            if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE:
+                duration = self.frames_np.shape[0] / self.RATE
+                self.timestamp_offset = self.frames_offset + duration - 5
+
+    def get_audio_chunk_for_processing(self):
+        """
+        Retrieves the next chunk of audio data for processing based on the current offsets.
+
+        Calculates which part of the audio data should be processed next, based on
+        the difference between the current timestamp offset and the frame's offset, scaled by
+        the audio sample rate (RATE). It then returns this chunk of audio data along with its
+        duration in seconds.
+
+        Returns:
+            tuple: A tuple containing:
+                - input_bytes (np.ndarray): The next chunk of audio data to be processed.
+                - duration (float): The duration of the audio chunk in seconds.
+        """
+        with self.lock:
+            samples_take = max(0, (self.timestamp_offset - self.frames_offset) * self.RATE)
+            input_bytes = self.frames_np[int(samples_take):].copy()
+        duration = input_bytes.shape[0] / self.RATE
+        return input_bytes, duration
+
+    def prepare_segments(self, last_segment=None):
+        """
+        Prepares the segments of transcribed text to be sent to the client.
+
+        This method compiles the recent segments of transcribed text, ensuring that only the
+        specified number of the most recent segments are included. It also appends the most
+        recent segment of text if provided (which is considered incomplete because of the possibility
+        of the last word being truncated in the audio chunk).
+
+        Args:
+            last_segment (str, optional): The most recent segment of transcribed text to be added
+                                          to the list of segments. Defaults to None.
+
+        Returns:
+            list: A list of transcribed text segments to be sent to the client.
+        """
+        segments = []
+        if len(self.transcript) >= self.send_last_n_segments:
+            segments = self.transcript[-self.send_last_n_segments:].copy()
+        else:
+            segments = self.transcript.copy()
+        if last_segment is not None:
+            segments = segments + [last_segment]
+        return segments
+
+    def get_audio_chunk_duration(self, input_bytes):
+        """
+        Calculates the duration of the provided audio chunk.
+
+        Args:
+            input_bytes (numpy.ndarray): The audio chunk for which to calculate the duration.
+
+        Returns:
+            float: The duration of the audio chunk in seconds.
+        """
+        return input_bytes.shape[0] / self.RATE
+
+    def send_transcription_to_client(self, segments):
+        """
+        Sends the specified transcription segments to the client over the websocket connection.
+
+        This method formats the transcription segments into a JSON object and attempts to send
+        this object to the client. If an error occurs during the send operation, it logs the error.
+
+        Returns:
+            segments (list): A list of transcription segments to be sent to the client.
+        """
+        try:
+            self.websocket.send(
+                json.dumps({
+                    "uid": self.client_uid,
+                    "segments": segments,
+                })
+            )
+        except Exception as e:
+            logging.error(f"[ERROR]: Sending data to client: {e}")
+
+    def disconnect(self):
+        """
+        Notify the client of disconnection and send a disconnect message.
+
+        This method sends a disconnect message to the client via the WebSocket connection to notify them
+        that the transcription service is disconnecting gracefully.
+
+        """
+        self.websocket.send(json.dumps({
+            "uid": self.client_uid,
+            "message": self.DISCONNECT
+        }))
+
+    def cleanup(self):
+        """
+        Perform cleanup tasks before exiting the transcription service.
+
+        This method performs necessary cleanup tasks, including stopping the transcription thread, marking
+        the exit flag to indicate the transcription thread should exit gracefully, and destroying resources
+        associated with the transcription process.
+
+        """
+        logging.info("Cleaning up.")
+        self.exit = True
+    
+    def get_segment_no_speech_prob(self, segment):
+        return getattr(segment, "no_speech_prob", 0)
+
+    def get_segment_start(self, segment):
+        return getattr(segment, "start", getattr(segment, "start_ts", 0))
+
+    def get_segment_end(self, segment):
+        return getattr(segment, "end", getattr(segment, "end_ts", 0))
+
+    def update_segments(self, segments, duration):
+        """
+        Processes the segments from Whisper and updates the transcript.
+        Uses helper methods to account for differences between backends.
+        
+        Args:
+            segments (list): List of segments returned by the transcriber.
+            duration (float): Duration of the current audio chunk.
+        
+        Returns:
+            dict or None: The last processed segment (if any).
+        """
+        offset = None
+        self.current_out = ''
+        last_segment = None
+
+        # Process complete segments only if there are more than one
+        # and if the last segment's no_speech_prob is below the threshold.
+        if len(segments) > 1 and self.get_segment_no_speech_prob(segments[-1]) <= self.no_speech_thresh:
+            for s in segments[:-1]:
+                text_ = s.text
+                self.text.append(text_)
+                with self.lock:
+                    start = self.timestamp_offset + self.get_segment_start(s)
+                    end = self.timestamp_offset + min(duration, self.get_segment_end(s))
+                if start >= end:
+                    continue
+                if self.get_segment_no_speech_prob(s) > self.no_speech_thresh:
+                    continue
+                completed_segment = self.format_segment(start, end, text_, completed=True)
+                self.transcript.append(completed_segment)
+
+                if self.translation_queue:
+                    try:
+                        self.translation_queue.put(completed_segment.copy(), timeout=0.1)
+                    except queue.Full:
+                        logging.warning("Translation queue is full, skipping segment")
+                offset = min(duration, self.get_segment_end(s))
+
+        # Process the last segment if its no_speech_prob is acceptable.
+        if self.get_segment_no_speech_prob(segments[-1]) <= self.no_speech_thresh:
+            self.current_out += segments[-1].text
+            with self.lock:
+                last_segment = self.format_segment(
+                    self.timestamp_offset + self.get_segment_start(segments[-1]),
+                    self.timestamp_offset + min(duration, self.get_segment_end(segments[-1])),
+                    self.current_out,
+                    completed=False
+                )
+
+        # Handle repeated output logic.
+        if self.current_out.strip() == self.prev_out.strip() and self.current_out != '':
+            self.same_output_count += 1
+
+            # if we remove the audio because of same output on the nth reptition we might remove the 
+            # audio thats not yet transcribed so, capturing the time when it was repeated for the first time
+            if self.end_time_for_same_output is None:
+                self.end_time_for_same_output = self.get_segment_end(segments[-1])
+            time.sleep(0.1)  # wait briefly for any new voice activity
+        else:
+            self.same_output_count = 0
+            self.end_time_for_same_output = None
+
+        # If the same incomplete segment is repeated too many times,
+        # append it to the transcript and update the offset.
+        if self.same_output_count > self.same_output_threshold:
+            if not self.text or self.text[-1].strip().lower() != self.current_out.strip().lower():
+                self.text.append(self.current_out)
+                with self.lock:
+                    completed_segment = self.format_segment(
+                        self.timestamp_offset,
+                        self.timestamp_offset + min(duration, self.end_time_for_same_output),
+                        self.current_out,
+                        completed=True
+                    )
+                    self.transcript.append(completed_segment)
+
+                    if self.translation_queue:
+                        try:
+                            self.translation_queue.put(completed_segment.copy(), timeout=0.1)
+                        except queue.Full:
+                            logging.warning("Translation queue is full, skipping segment")
+
+            self.current_out = ''
+            offset = min(duration, self.end_time_for_same_output)
+            self.same_output_count = 0
+            last_segment = None
+            self.end_time_for_same_output = None
+        else:
+            self.prev_out = self.current_out
+
+        if offset is not None:
+            with self.lock:
+                self.timestamp_offset += offset
+
+        return last_segment

whisper_live/backend/faster_whisper_backend.py

@@ -0,0 +1,257 @@
+import os
+import json
+import logging
+import threading
+import time
+import torch
+import ctranslate2
+from huggingface_hub import snapshot_download
+
+from whisper_live.transcriber.transcriber_faster_whisper import WhisperModel
+from whisper_live.backend.base import ServeClientBase
+
+
+class ServeClientFasterWhisper(ServeClientBase):
+    SINGLE_MODEL = None
+    SINGLE_MODEL_LOCK = threading.Lock()
+    BATCH_WORKER = None
+
+    def __init__(
+        self,
+        websocket,
+        task="transcribe",
+        device=None,
+        language=None,
+        client_uid=None,
+        model="small.en",
+        initial_prompt=None,
+        vad_parameters=None,
+        use_vad=True,
+        single_model=False,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=7,
+        cache_path="~/.cache/whisper-live/",
+        translation_queue=None,
+    ):
+        """
+        Initialize a ServeClient instance.
+        The Whisper model is initialized based on the client's language and device availability.
+        The transcription thread is started upon initialization. A "SERVER_READY" message is sent
+        to the client to indicate that the server is ready.
+
+        Args:
+            websocket (WebSocket): The WebSocket connection for the client.
+            task (str, optional): The task type, e.g., "transcribe". Defaults to "transcribe".
+            device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
+            language (str, optional): The language for transcription. Defaults to None.
+            client_uid (str, optional): A unique identifier for the client. Defaults to None.
+            model (str, optional): The whisper model size. Defaults to 'small.en'
+            initial_prompt (str, optional): Prompt for whisper inference. Defaults to None.
+            single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.
+            send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
+            no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
+            clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
+            same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
+
+        """
+        super().__init__(
+            client_uid,
+            websocket,
+            send_last_n_segments,
+            no_speech_thresh,
+            clip_audio,
+            same_output_threshold,
+            translation_queue
+        )
+        self.cache_path = cache_path
+        self.model_sizes = [
+            "tiny", "tiny.en", "base", "base.en", "small", "small.en",
+            "medium", "medium.en", "large-v2", "large-v3", "distil-small.en",
+            "distil-medium.en", "distil-large-v2", "distil-large-v3",
+            "large-v3-turbo", "turbo"
+        ]
+
+        self.model_size_or_path = model
+        self.language = "en" if self.model_size_or_path.endswith("en") else language
+        self.task = task
+        self.initial_prompt = initial_prompt
+        self.vad_parameters = vad_parameters or {"threshold": 0.5}
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        if device == "cuda":
+            major, _ = torch.cuda.get_device_capability(device)
+            self.compute_type = "float16" if major >= 7 else "float32"
+        else:
+            self.compute_type = "int8"
+
+        if self.model_size_or_path is None:
+            return
+        logging.info(f"Using Device={device} with precision {self.compute_type}")
+    
+        try:
+            if single_model:
+                if ServeClientFasterWhisper.SINGLE_MODEL is None:
+                    self.create_model(device)
+                    ServeClientFasterWhisper.SINGLE_MODEL = self.transcriber
+                else:
+                    self.transcriber = ServeClientFasterWhisper.SINGLE_MODEL
+            else:
+                self.create_model(device)
+        except Exception as e:
+            logging.error(f"Failed to load model: {e}")
+            self.websocket.send(json.dumps({
+                "uid": self.client_uid,
+                "status": "ERROR",
+                "message": f"Failed to load model: {str(self.model_size_or_path)}"
+            }))
+            self.websocket.close()
+            return
+
+        self.use_vad = use_vad
+
+        # threading
+        self.trans_thread = threading.Thread(target=self.speech_to_text)
+        self.trans_thread.start()
+        self.websocket.send(
+            json.dumps(
+                {
+                    "uid": self.client_uid,
+                    "message": self.SERVER_READY,
+                    "backend": "faster_whisper"
+                }
+            )
+        )
+
+    def create_model(self, device):
+        """
+        Instantiates a new model, sets it as the transcriber. If model is a huggingface model_id
+        then it is automatically converted to ctranslate2(faster_whisper) format.
+        """
+        model_ref = self.model_size_or_path
+
+        if model_ref in self.model_sizes:
+            model_to_load = model_ref
+        else:
+            logging.info(f"Model not in model_sizes")
+            if os.path.isdir(model_ref) and ctranslate2.contains_model(model_ref):
+                model_to_load = model_ref
+            else:
+                local_snapshot = snapshot_download(
+                    repo_id = model_ref,
+                    repo_type = "model",
+                )
+                if ctranslate2.contains_model(local_snapshot):
+                    model_to_load = local_snapshot
+                else:
+                    cache_root = os.path.expanduser(os.path.join(self.cache_path, "whisper-ct2-models/"))
+                    os.makedirs(cache_root, exist_ok=True)
+                    safe_name = model_ref.replace("/", "--")
+                    ct2_dir = os.path.join(cache_root, safe_name)
+
+                    if not ctranslate2.contains_model(ct2_dir):
+                        logging.info(f"Converting '{model_ref}' to CTranslate2 @ {ct2_dir}")
+                        ct2_converter = ctranslate2.converters.TransformersConverter(
+                            local_snapshot, 
+                            copy_files=["tokenizer.json", "preprocessor_config.json"]
+                        )
+                        ct2_converter.convert(
+                            output_dir=ct2_dir,
+                            quantization=self.compute_type,
+                            force=False,  # skip if already up-to-date
+                        )
+                    model_to_load = ct2_dir
+
+        logging.info(f"Loading model: {model_to_load}")
+        self.transcriber = WhisperModel(
+            model_to_load,
+            device=device,
+            compute_type=self.compute_type,
+            local_files_only=False,
+        )
+
+    def set_language(self, info):
+        """
+        Updates the language attribute based on the detected language information.
+
+        Args:
+            info (object): An object containing the detected language and its probability. This object
+                        must have at least two attributes: `language`, a string indicating the detected
+                        language, and `language_probability`, a float representing the confidence level
+                        of the language detection.
+        """
+        if info.language_probability > 0.5:
+            self.language = info.language
+            logging.info(f"Detected language {self.language} with probability {info.language_probability}")
+            self.websocket.send(json.dumps(
+                {"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability}))
+
+    def transcribe_audio(self, input_sample):
+        """
+        Transcribes the provided audio sample using the configured transcriber instance.
+
+        If the language has not been set, it updates the session's language based on the transcription
+        information.
+
+        Args:
+            input_sample (np.array): The audio chunk to be transcribed. This should be a NumPy
+                                    array representing the audio data.
+
+        Returns:
+            The transcription result from the transcriber. The exact format of this result
+            depends on the implementation of the `transcriber.transcribe` method but typically
+            includes the transcribed text.
+        """
+        # Batch inference path: submit to central queue and wait
+        if ServeClientFasterWhisper.BATCH_WORKER is not None:
+            from whisper_live.batch_inference import BatchRequest
+            request = BatchRequest(
+                audio=input_sample,
+                language=self.language,
+                task=self.task,
+                initial_prompt=self.initial_prompt,
+                use_vad=self.use_vad,
+                vad_parameters=self.vad_parameters if self.use_vad else None,
+            )
+            ServeClientFasterWhisper.BATCH_WORKER.submit(request)
+            request.future.wait(timeout=30)
+            if request.error:
+                raise request.error
+            if self.language is None and request.info is not None:
+                self.set_language(request.info)
+            return request.result
+
+        # Original lock-based path (backward compatible)
+        if ServeClientFasterWhisper.SINGLE_MODEL:
+            ServeClientFasterWhisper.SINGLE_MODEL_LOCK.acquire()
+        result, info = self.transcriber.transcribe(
+            input_sample,
+            initial_prompt=self.initial_prompt,
+            language=self.language,
+            task=self.task,
+            vad_filter=self.use_vad,
+            vad_parameters=self.vad_parameters if self.use_vad else None)
+        if ServeClientFasterWhisper.SINGLE_MODEL:
+            ServeClientFasterWhisper.SINGLE_MODEL_LOCK.release()
+
+        if self.language is None and info is not None:
+            self.set_language(info)
+        return result
+
+    def handle_transcription_output(self, result, duration):
+        """
+        Handle the transcription output, updating the transcript and sending data to the client.
+
+        Args:
+            result (str): The result from whisper inference i.e. the list of segments.
+            duration (float): Duration of the transcribed audio chunk.
+        """
+        segments = []
+        if len(result):
+            self.t_start = None
+            last_segment = self.update_segments(result, duration)
+            segments = self.prepare_segments(last_segment)
+
+        if len(segments):
+            self.send_transcription_to_client(segments)

whisper_live/backend/openvino_backend.py

@@ -0,0 +1,148 @@
+import json
+import logging
+import threading
+import time
+
+from openvino import Core
+from whisper_live.backend.base import ServeClientBase
+from whisper_live.transcriber.transcriber_openvino import WhisperOpenVINO
+
+
+class ServeClientOpenVINO(ServeClientBase):
+    SINGLE_MODEL = None
+    SINGLE_MODEL_LOCK = threading.Lock()
+
+    def __init__(
+        self,
+        websocket,
+        task="transcribe",
+        device=None,
+        language=None,
+        client_uid=None,
+        model="small.en",
+        initial_prompt=None,
+        vad_parameters=None,
+        use_vad=True,
+        single_model=False,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+    ):
+        """
+        Initialize a ServeClient instance.
+        The Whisper model is initialized based on the client's language and device availability.
+        The transcription thread is started upon initialization. A "SERVER_READY" message is sent
+        to the client to indicate that the server is ready.
+
+        Args:
+            websocket (WebSocket): The WebSocket connection for the client.
+            task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
+            device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
+            language (str, optional): The language for transcription. Defaults to None.
+            client_uid (str, optional): A unique identifier for the client. Defaults to None.
+            model (str, optional): Huggingface model_id for a valid OpenVINO model.
+            initial_prompt (str, optional): Prompt for whisper inference. Defaults to None.
+            single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.
+            send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
+            no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
+            clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
+            same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
+        """
+        super().__init__(
+            client_uid,
+            websocket,
+            send_last_n_segments,
+            no_speech_thresh,
+            clip_audio,
+            same_output_threshold,
+        )
+        self.language = "en" if language is None else language
+        if not self.language.startswith("<|"):
+            self.language = f"<|{self.language}|>"
+
+        self.task = "transcribe" if task is None else task
+
+        self.clip_audio = True
+
+        core = Core()
+        available_devices = core.available_devices
+        if 'GPU' in available_devices:
+            selected_device = 'GPU'
+        else:
+            gpu_devices = [d for d in available_devices if d.startswith('GPU')]
+            selected_device = gpu_devices[0] if gpu_devices else 'CPU'
+        self.device = selected_device
+
+
+        if single_model:
+            if ServeClientOpenVINO.SINGLE_MODEL is None:
+                self.create_model(model)
+                ServeClientOpenVINO.SINGLE_MODEL = self.transcriber
+            else:
+                self.transcriber = ServeClientOpenVINO.SINGLE_MODEL
+        else:
+            self.create_model(model)
+
+        # threading
+        self.trans_thread = threading.Thread(target=self.speech_to_text)
+        self.trans_thread.start()
+
+        self.websocket.send(json.dumps({
+            "uid": self.client_uid,
+            "message": self.SERVER_READY,
+            "backend": "openvino"
+        }))
+        logging.info(f"Using OpenVINO device: {self.device}")
+        logging.info(f"Running OpenVINO backend with language: {self.language} and task: {self.task}")
+
+    def create_model(self, model_id):
+        """
+        Instantiates a new model, sets it as the transcriber.
+        """
+        self.transcriber = WhisperOpenVINO(
+            model_id,
+            device=self.device,
+            language=self.language,
+            task=self.task
+        )
+
+    def transcribe_audio(self, input_sample):
+        """
+        Transcribes the provided audio sample using the configured transcriber instance.
+
+        If the language has not been set, it updates the session's language based on the transcription
+        information.
+
+        Args:
+            input_sample (np.array): The audio chunk to be transcribed. This should be a NumPy
+                                    array representing the audio data.
+
+        Returns:
+            The transcription result from the transcriber. The exact format of this result
+            depends on the implementation of the `transcriber.transcribe` method but typically
+            includes the transcribed text.
+        """
+        if ServeClientOpenVINO.SINGLE_MODEL:
+            ServeClientOpenVINO.SINGLE_MODEL_LOCK.acquire()
+        result = self.transcriber.transcribe(input_sample)
+        if ServeClientOpenVINO.SINGLE_MODEL:
+            ServeClientOpenVINO.SINGLE_MODEL_LOCK.release()
+        return result
+
+    def handle_transcription_output(self, result, duration):
+        """
+        Handle the transcription output, updating the transcript and sending data to the client.
+
+        Args:
+            result (str): The result from whisper inference i.e. the list of segments.
+            duration (float): Duration of the transcribed audio chunk.
+        """
+        segments = []
+        if len(result):
+            self.t_start = None
+            last_segment = self.update_segments(result, duration)
+            segments = self.prepare_segments(last_segment)
+
+        if len(segments):
+            self.send_transcription_to_client(segments)

whisper_live/backend/tokenization_small100.py

@@ -0,0 +1,365 @@
+# Copyright (c) 2022 Idiap Research Institute, http://www.idiap.ch/
+# Written by Alireza Mohammadshahi <alireza.mohammadshahi@idiap.ch>
+# This is a modified version of https://github.com/huggingface/transformers/blob/main/src/transformers/models/m2m_100/tokenization_m2m_100.py 
+# which owns by Fariseq Authors and The HuggingFace Inc. team.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for SMALL100."""
+import json
+import os
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import sentencepiece
+
+from transformers.tokenization_utils import BatchEncoding, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+SPIECE_UNDERLINE = "▁"
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.json",
+    "spm_file": "sentencepiece.bpe.model",
+    "tokenizer_config_file": "tokenizer_config.json",
+}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "alirezamsh/small100": "https://huggingface.co/alirezamsh/small100/resolve/main/vocab.json",
+    },
+    "spm_file": {
+        "alirezamsh/small100": "https://huggingface.co/alirezamsh/small100/resolve/main/sentencepiece.bpe.model",
+    },
+    "tokenizer_config_file": {
+        "alirezamsh/small100": "https://huggingface.co/alirezamsh/small100/resolve/main/tokenizer_config.json",
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "alirezamsh/small100": 1024,
+}
+
+# fmt: off
+FAIRSEQ_LANGUAGE_CODES = {
+    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"]
+}
+# fmt: on
+
+
+class SMALL100Tokenizer(PreTrainedTokenizer):
+    """
+    Construct an SMALL100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        spm_file (`str`):
+            Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
+            contains the vocabulary.
+        tgt_lang (`str`, *optional*):
+            A string representing the target language.
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        sep_token (`str`, *optional*, defaults to `"</s>"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        pad_token (`str`, *optional*, defaults to `"<pad>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        language_codes (`str`, *optional*):
+            What language codes to use. Should be `"m2m100"`.
+        sp_model_kwargs (`dict`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+    Examples:
+    ```python
+    >>> from tokenization_small100 import SMALL100Tokenizer
+    >>> tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100", tgt_lang="ro")
+    >>> src_text = " UN Chief Says There Is No Military Solution in Syria"
+    >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
+    >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
+    >>> model(**model_inputs)  # should work
+    ```"""
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+
+    prefix_tokens: List[int] = []
+    suffix_tokens: List[int] = []
+
+    def __init__(
+        self,
+        vocab_file,
+        spm_file,
+        tgt_lang=None,
+        bos_token="<s>",
+        eos_token="</s>",
+        sep_token="</s>",
+        pad_token="<pad>",
+        unk_token="<unk>",
+        language_codes="m2m100",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        num_madeup_words=8,
+        **kwargs,
+    ) -> None:
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        self.language_codes = language_codes
+        fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes]
+        self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}
+
+        kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", [])
+        kwargs["additional_special_tokens"] += [
+            self.get_lang_token(lang_code)
+            for lang_code in fairseq_language_code
+            if self.get_lang_token(lang_code) not in kwargs["additional_special_tokens"]
+        ]
+
+        self.vocab_file = vocab_file
+        self.encoder = load_json(vocab_file)
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.spm_file = spm_file
+        self.sp_model = load_spm(spm_file, self.sp_model_kwargs)
+
+        self.encoder_size = len(self.encoder)
+
+        self.lang_token_to_id = {
+            self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)
+        }
+        self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
+        self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()}
+
+        self._tgt_lang = tgt_lang if tgt_lang is not None else "en"
+        self.cur_lang_id = self.get_lang_id(self._tgt_lang)
+        self.num_madeup_words = num_madeup_words
+        
+        super().__init__(
+            tgt_lang=tgt_lang,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            sep_token=sep_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            language_codes=language_codes,
+            sp_model_kwargs=self.sp_model_kwargs,
+            num_madeup_words=num_madeup_words,
+            **kwargs,
+        )
+        
+        self.set_lang_special_tokens(self._tgt_lang)
+
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self.encoder) + len(self.lang_token_to_id) + self.num_madeup_words
+
+    @property
+    def tgt_lang(self) -> str:
+        return self._tgt_lang
+
+    @tgt_lang.setter
+    def tgt_lang(self, new_tgt_lang: str) -> None:
+        self._tgt_lang = new_tgt_lang
+        self.set_lang_special_tokens(self._tgt_lang)
+
+    def _tokenize(self, text: str) -> List[str]:
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        if token in self.lang_token_to_id:
+            return self.lang_token_to_id[token]
+        return self.encoder.get(token, self.encoder[self.unk_token])
+
+    def _convert_id_to_token(self, index: int) -> str:
+        """Converts an index (integer) in a token (str) using the decoder."""
+        if index in self.id_to_lang_token:
+            return self.id_to_lang_token[index]
+        return self.decoder.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """Converts a sequence of tokens (strings for sub-words) in a single string."""
+        return self.sp_model.decode(tokens)
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        prefix_ones = [1] * len(self.prefix_tokens)
+        suffix_ones = [1] * len(self.suffix_tokens)
+        if token_ids_1 is None:
+            return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
+        return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. An MBART sequence has the following format, where `X` represents the sequence:
+        - `input_ids` (for encoder) `X [eos, src_lang_code]`
+        - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
+        BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+        separator.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            if self.prefix_tokens is None:
+                return token_ids_0 + self.suffix_tokens
+            else:
+                return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+        # We don't expect to process pairs, but leave the pair logic for API consistency
+        if self.prefix_tokens is None:
+            return token_ids_0 + token_ids_1 + self.suffix_tokens
+        else:
+            return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+    def get_vocab(self) -> Dict:
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def __getstate__(self) -> Dict:
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d: Dict) -> None:
+        self.__dict__ = d
+
+        # for backward compatibility
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+
+        self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs)
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        save_dir = Path(save_directory)
+        if not save_dir.is_dir():
+            raise OSError(f"{save_directory} should be a directory")
+        vocab_save_path = save_dir / (
+            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
+        )
+        spm_save_path = save_dir / (
+            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
+        )
+
+        save_json(self.encoder, vocab_save_path)
+
+        if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file):
+            copyfile(self.spm_file, spm_save_path)
+        elif not os.path.isfile(self.spm_file):
+            with open(spm_save_path, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (str(vocab_save_path), str(spm_save_path))
+
+    def prepare_seq2seq_batch(
+        self,
+        src_texts: List[str],
+        tgt_texts: Optional[List[str]] = None,
+        tgt_lang: str = "ro",
+        **kwargs,
+    ) -> BatchEncoding:
+        self.tgt_lang = tgt_lang
+        self.set_lang_special_tokens(self.tgt_lang)
+        return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+    def _build_translation_inputs(self, raw_inputs, tgt_lang: Optional[str], **extra_kwargs):
+        """Used by translation pipeline, to prepare inputs for the generate function"""
+        if tgt_lang is None:
+            raise ValueError("Translation requires a `tgt_lang` for this model")
+        self.tgt_lang = tgt_lang
+        inputs = self(raw_inputs, add_special_tokens=True, **extra_kwargs)
+        return inputs
+
+    def _switch_to_input_mode(self):
+        self.set_lang_special_tokens(self.tgt_lang)
+
+    def _switch_to_target_mode(self):
+        self.prefix_tokens = None
+        self.suffix_tokens = [self.eos_token_id]        
+
+    def set_lang_special_tokens(self, src_lang: str) -> None:
+        """Reset the special tokens to the tgt lang setting. No prefix and suffix=[eos, tgt_lang_code]."""
+        lang_token = self.get_lang_token(src_lang)
+        self.cur_lang_id = self.lang_token_to_id[lang_token]
+        self.prefix_tokens = [self.cur_lang_id]
+        self.suffix_tokens = [self.eos_token_id]
+
+    def get_lang_token(self, lang: str) -> str:
+        return self.lang_code_to_token[lang]
+
+    def get_lang_id(self, lang: str) -> int:
+        lang_token = self.get_lang_token(lang)
+        return self.lang_token_to_id[lang_token]
+
+
+def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
+    spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
+    spm.Load(str(path))
+    return spm
+
+
+def load_json(path: str) -> Union[Dict, List]:
+    with open(path, "r") as f:
+        return json.load(f)
+
+
+def save_json(data, path: str) -> None:
+    with open(path, "w") as f:
+        json.dump(data, f, indent=2)

whisper_live/backend/translation_backend.py

@@ -0,0 +1,218 @@
+import json
+import logging
+import threading
+import time
+import queue
+from typing import Dict, Any, Optional
+import torch
+import threading
+from transformers import M2M100ForConditionalGeneration
+from whisper_live.backend.tokenization_small100 import SMALL100Tokenizer
+
+from whisper_live.backend.base import ServeClientBase
+
+
+class ServeClientTranslation(ServeClientBase):
+    """
+    Handles translation of completed transcription segments in a separate thread.
+    Reads from a queue populated by the transcription backend and sends translated
+    segments back to the client via WebSocket.
+    """
+    
+    def __init__(
+        self,
+        client_uid,
+        websocket,
+        translation_queue,
+        target_language="fr", 
+        send_last_n_segments=10,
+        model_name="alirezamsh/small100"
+    ):
+        """
+        Initialize the translation client.
+        
+        Args:
+            client_uid (str): Unique identifier for the client
+            websocket: WebSocket connection to the client
+            translation_queue (queue.Queue): Queue containing completed segments to translate
+            target_language (str): Target language code (default: "fr" for French)
+            send_last_n_segments (int): Number of recent translated segments to send
+            model_name (str): Translation model name to use
+        """
+        super().__init__(client_uid, websocket, send_last_n_segments)
+        self.translation_queue = translation_queue
+        self.target_language = target_language
+        self.model_name = model_name
+        self.translated_segments = []
+        self.translation_model = None
+        self.tokenizer = None
+        self.device = None
+        self.model_loaded = False
+        self.load_translation_model()
+        
+    def load_translation_model(self):
+        """Load the translation model and tokenizer."""
+        try:
+            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            logging.info(f"Loading translation model on device: {self.device}")
+            
+            self.translation_model = M2M100ForConditionalGeneration.from_pretrained(
+                self.model_name
+            ).to(self.device)
+            self.tokenizer = SMALL100Tokenizer.from_pretrained(self.model_name)
+            self.tokenizer.tgt_lang = self.target_language
+            
+            self.model_loaded = True
+            logging.info(f"Translation model loaded successfully. Target language: {self.target_language}")
+        except Exception as e:
+            logging.error(f"Failed to load translation model: {e}")
+            self.translation_model = None
+            self.tokenizer = None
+            self.model_loaded = False
+    
+    def translate_text(self, text: str) -> str:
+        """
+        Translate a single text segment.
+        
+        Args:
+            text (str): Text to translate
+            
+        Returns:
+            str: Translated text or original text if translation fails
+        """
+        if not self.model_loaded or not text.strip():
+            return text
+            
+        try:
+            # Encode input and move to device
+            encoded_input = self.tokenizer(text, return_tensors="pt").to(self.device)
+            
+            # Generate translation
+            with torch.no_grad():
+                generated_tokens = self.translation_model.generate(**encoded_input)
+            
+            # Decode output
+            output = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+            return output[0] if output else text
+            
+        except Exception as e:
+            logging.error(f"Translation failed for text '{text}': {e}")
+            return text
+    
+    def process_translation_queue(self):
+        """
+        Process segments from the translation queue.
+        Continuously reads from the queue until None is received (exit signal).
+        """
+        logging.info(f"Starting translation processing for client {self.client_uid}")
+        
+        while not self.exit:
+            try:
+                # Get segment from queue with timeout
+                segment = self.translation_queue.get(timeout=1.0)
+                
+                # Check for exit signal
+                if segment is None:
+                    logging.info(f"Received exit signal for translation client {self.client_uid}")
+                    break
+                    
+                # Only translate completed segments
+                if not segment.get("completed", False):
+                    self.translation_queue.task_done()
+                    continue
+                    
+                # Translate the segment
+                original_text = segment.get("text", "")
+                translated_text = self.translate_text(original_text)
+                
+                # Create translated segment
+                translated_segment = {
+                    "start": segment["start"],
+                    "end": segment["end"],
+                    "text": translated_text,
+                    "completed": segment.get("completed", False),
+                    "target_language": self.target_language
+                }
+                
+                self.translated_segments.append(translated_segment)
+                segments_to_send = self.prepare_translated_segments()
+                self.send_translation_to_client(segments_to_send)
+                
+                self.translation_queue.task_done()
+                
+            except queue.Empty:
+                continue
+            except Exception as e:
+                logging.error(f"Error processing translation queue: {e}")
+                continue
+        
+        logging.info(f"Translation processing ended for client {self.client_uid}")
+    
+    def prepare_translated_segments(self):
+        """
+        Prepare the last n translated segments to send to client.
+        
+        Returns:
+            list: List of recent translated segments
+        """
+        if len(self.translated_segments) >= self.send_last_n_segments:
+            return self.translated_segments[-self.send_last_n_segments:]
+        return self.translated_segments[:]
+    
+    def send_translation_to_client(self, translated_segments):
+        """
+        Send translated segments to the client via WebSocket.
+        
+        Args:
+            translated_segments (list): List of translated segments to send
+        """
+        try:
+            self.websocket.send(
+                json.dumps({
+                    "uid": self.client_uid,
+                    "translated_segments": translated_segments,
+                })
+            )
+        except Exception as e:
+            logging.error(f"[ERROR]: Sending translation data to client: {e}")
+    
+    def speech_to_text(self):
+        """
+        Override parent method to handle translation processing.
+        This method will be called when the translation thread starts.
+        """
+        self.process_translation_queue()
+    
+    def set_target_language(self, language: str):
+        """
+        Change the target language for translation.
+        
+        Args:
+            language (str): New target language code
+        """
+        self.target_language = language
+        if self.tokenizer:
+            self.tokenizer.tgt_lang = language
+            logging.info(f"Target language changed to: {language}")
+    
+    def cleanup(self):
+        """Clean up translation resources."""
+        logging.info(f"Cleaning up translation resources for client {self.client_uid}")
+        self.exit = True
+        
+        try:
+            self.translation_queue.put(None, timeout=1.0)
+        except:
+            pass
+        
+        self.translated_segments.clear()
+        
+        if self.translation_model:
+            del self.translation_model
+            self.translation_model = None
+        if self.tokenizer:
+            del self.tokenizer
+            self.tokenizer = None
+        
+        if self.device and self.device.type == 'cuda':
+            torch.cuda.empty_cache()

whisper_live/backend/trt_backend.py

@@ -0,0 +1,210 @@
+import json
+import logging
+import threading
+import time
+
+from whisper_live.backend.base import ServeClientBase
+from whisper_live.transcriber.transcriber_tensorrt import WhisperTRTLLM
+
+
+class ServeClientTensorRT(ServeClientBase):
+    SINGLE_MODEL = None
+    SINGLE_MODEL_LOCK = threading.Lock()
+
+    def __init__(
+        self,
+        websocket,
+        task="transcribe",
+        multilingual=False,
+        language=None,
+        client_uid=None,
+        model=None,
+        single_model=False,
+        use_py_session=False,
+        max_new_tokens=225,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+    ):
+        """
+        Initialize a ServeClient instance.
+        The Whisper model is initialized based on the client's language and device availability.
+        The transcription thread is started upon initialization. A "SERVER_READY" message is sent
+        to the client to indicate that the server is ready.
+
+        Args:
+            websocket (WebSocket): The WebSocket connection for the client.
+            task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
+            device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
+            multilingual (bool, optional): Whether the client supports multilingual transcription. Defaults to False.
+            language (str, optional): The language for transcription. Defaults to None.
+            client_uid (str, optional): A unique identifier for the client. Defaults to None.
+            single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.
+            use_py_session (bool, optional): Use python session or cpp session. Defaults to Cpp Session.
+            max_new_tokens (int, optional): Max number of tokens to generate.
+            send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
+            no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
+            clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
+            same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
+        """
+        super().__init__(
+            client_uid,
+            websocket,
+            send_last_n_segments,
+            no_speech_thresh,
+            clip_audio,
+            same_output_threshold,
+        )
+
+        self.language = language if multilingual else "en"
+        self.task = task
+        self.eos = False
+        self.max_new_tokens = max_new_tokens
+
+        if single_model:
+            if ServeClientTensorRT.SINGLE_MODEL is None:
+                self.create_model(model, multilingual, use_py_session=use_py_session)
+                ServeClientTensorRT.SINGLE_MODEL = self.transcriber
+            else:
+                self.transcriber = ServeClientTensorRT.SINGLE_MODEL
+        else:
+            self.create_model(model, multilingual, use_py_session=use_py_session)
+
+        # threading
+        self.trans_thread = threading.Thread(target=self.speech_to_text)
+        self.trans_thread.start()
+
+        self.websocket.send(json.dumps({
+            "uid": self.client_uid,
+            "message": self.SERVER_READY,
+            "backend": "tensorrt"
+        }))
+
+    def create_model(self, model, multilingual, warmup=True, use_py_session=False):
+        """
+        Instantiates a new model, sets it as the transcriber and does warmup if desired.
+        """
+        self.transcriber = WhisperTRTLLM(
+            model,
+            assets_dir="assets",
+            device="cuda",
+            is_multilingual=multilingual,
+            language=self.language,
+            task=self.task,
+            use_py_session=use_py_session,
+            max_output_len=self.max_new_tokens,
+        )
+        if warmup:
+            self.warmup()
+
+    def warmup(self, warmup_steps=10):
+        """
+        Warmup TensorRT since first few inferences are slow.
+
+        Args:
+            warmup_steps (int): Number of steps to warm up the model for.
+        """
+        logging.info("[INFO:] Warming up TensorRT engine..")
+        mel, _ = self.transcriber.log_mel_spectrogram("assets/jfk.flac")
+        for i in range(warmup_steps):
+            self.transcriber.transcribe(mel)
+
+    def set_eos(self, eos):
+        """
+        Sets the End of Speech (EOS) flag.
+
+        Args:
+            eos (bool): The value to set for the EOS flag.
+        """
+        self.lock.acquire()
+        self.eos = eos
+        self.lock.release()
+
+    def handle_transcription_output(self, last_segment, duration):
+        """
+        Handle the transcription output, updating the transcript and sending data to the client.
+
+        Args:
+            last_segment (str): The last segment from the whisper output which is considered to be incomplete because
+                                of the possibility of word being truncated.
+            duration (float): Duration of the transcribed audio chunk.
+        """
+        segments = self.prepare_segments({"text": last_segment})
+        self.send_transcription_to_client(segments)
+        if self.eos:
+            self.update_timestamp_offset(last_segment, duration)
+
+    def transcribe_audio(self, input_bytes):
+        """
+        Transcribe the audio chunk and send the results to the client.
+
+        Args:
+            input_bytes (np.array): The audio chunk to transcribe.
+        """
+        if ServeClientTensorRT.SINGLE_MODEL:
+            ServeClientTensorRT.SINGLE_MODEL_LOCK.acquire()
+        logging.info(f"[WhisperTensorRT:] Processing audio with duration: {input_bytes.shape[0] / self.RATE}")
+        mel, duration = self.transcriber.log_mel_spectrogram(input_bytes)
+        last_segment = self.transcriber.transcribe(
+            mel,
+            text_prefix=f"<|startoftranscript|><|{self.language}|><|{self.task}|><|notimestamps|>",
+        )
+        if ServeClientTensorRT.SINGLE_MODEL:
+            ServeClientTensorRT.SINGLE_MODEL_LOCK.release()
+        if last_segment:
+            self.handle_transcription_output(last_segment, duration)
+
+    def update_timestamp_offset(self, last_segment, duration):
+        """
+        Update timestamp offset and transcript.
+
+        Args:
+            last_segment (str): Last transcribed audio from the whisper model.
+            duration (float): Duration of the last audio chunk.
+        """
+        if not len(self.transcript):
+            self.transcript.append({"text": last_segment + " "})
+        elif self.transcript[-1]["text"].strip() != last_segment:
+            self.transcript.append({"text": last_segment + " "})
+        
+        with self.lock:
+            self.timestamp_offset += duration
+
+    def speech_to_text(self):
+        """
+        Process an audio stream in an infinite loop, continuously transcribing the speech.
+
+        This method continuously receives audio frames, performs real-time transcription, and sends
+        transcribed segments to the client via a WebSocket connection.
+
+        If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
+        It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
+        are sent to the client in real-time, and a history of segments is maintained to provide context.
+
+        Raises:
+            Exception: If there is an issue with audio processing or WebSocket communication.
+
+        """
+        while True:
+            if self.exit:
+                logging.info("Exiting speech to text thread")
+                break
+
+            if self.frames_np is None:
+                time.sleep(0.02)    # wait for any audio to arrive
+                continue
+
+            self.clip_audio_if_no_valid_segment()
+
+            input_bytes, duration = self.get_audio_chunk_for_processing()
+            if duration < 0.4:
+                continue
+
+            try:
+                input_sample = input_bytes.copy()
+                logging.info(f"[WhisperTensorRT:] Processing audio with duration: {duration}")
+                self.transcribe_audio(input_sample)
+
+            except Exception as e:
+                logging.error(f"[ERROR]: {e}")

whisper_live/batch_inference.py

@@ -0,0 +1,397 @@
+"""
+Batch inference scheduler for WhisperLive.
+
+Replaces the per-session SINGLE_MODEL_LOCK with a queue-based batch system.
+Multiple sessions submit audio to a central queue; a single dedicated thread
+collects pending requests and runs them as a GPU batch via CTranslate2's
+batched encode() + generate() API.
+
+For batch_size=1, falls back to standard transcriber.transcribe() for
+identical behavior to the non-batched path.
+
+Usage:
+    Enable via ``--batch_inference`` CLI flag. The batch worker is lazily
+    started after the first client connects and the shared model is loaded.
+
+Thread safety:
+    - ``queue.Queue`` is stdlib thread-safe.
+    - Each ``BatchRequest.future`` (``threading.Event``) is written by the
+      batch worker BEFORE ``.set()``, read by the session thread AFTER
+      ``.wait()`` — no data race.
+    - Only the batch worker thread touches the GPU model — zero lock
+      contention between session threads.
+"""
+
+import logging
+import queue
+import threading
+import time
+from dataclasses import dataclass, field
+from math import ceil
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+
+from faster_whisper.audio import pad_or_trim
+from faster_whisper.tokenizer import Tokenizer
+from faster_whisper.vad import (
+    VadOptions,
+    collect_chunks,
+    get_speech_timestamps,
+)
+
+from whisper_live.transcriber.transcriber_faster_whisper import (
+    Segment,
+    TranscriptionInfo,
+    get_compression_ratio,
+    get_suppressed_tokens,
+)
+
+
+@dataclass
+class BatchRequest:
+    """A single inference request submitted by a session thread.
+
+    The session thread creates this, calls ``BatchInferenceWorker.submit()``,
+    then blocks on ``future.wait()``.  The batch worker fills ``result``
+    and/or ``error``, then signals ``future.set()``.
+
+    Attributes:
+        audio: Raw audio samples (float32, 16 kHz mono).
+        language: ISO language code or None for auto-detection.
+        task: ``"transcribe"`` or ``"translate"``.
+        initial_prompt: Optional prompt for Whisper conditioning.
+        use_vad: Whether to apply Voice Activity Detection.
+        vad_parameters: Parameters forwarded to ``VadOptions``.
+        future: Event signaled when the result is ready.
+        result: List of ``Segment`` objects (filled by worker).
+        info: ``TranscriptionInfo`` metadata (filled by worker).
+        error: Exception instance if processing failed.
+    """
+    audio: np.ndarray
+    language: Optional[str] = None
+    task: str = "transcribe"
+    initial_prompt: Optional[str] = None
+    use_vad: bool = True
+    vad_parameters: Optional[Dict] = None
+    # Signaling
+    future: threading.Event = field(default_factory=threading.Event)
+    # Results (filled by batch worker)
+    result: Optional[Any] = None
+    info: Optional[Any] = None
+    error: Optional[Exception] = None
+
+
+class BatchInferenceWorker:
+    """Central batch inference scheduler for the faster_whisper backend.
+
+    Owns a single daemon thread that is the **only** thread touching the GPU
+    model.  Per-session transcription threads submit ``BatchRequest`` objects
+    and block on ``future.wait()`` instead of competing for
+    ``SINGLE_MODEL_LOCK``.
+
+    The worker loop:
+
+    1. Blocks until the first request arrives from the queue.
+    2. Waits up to ``batch_window_ms`` for additional requests (up to
+       ``max_batch_size``).
+    3. Processes the collected batch:
+       - **batch_size == 1**: delegates to ``transcriber.transcribe()`` for
+         identical behavior to the non-batched path.
+       - **batch_size > 1**: runs a custom batched GPU path using
+         CTranslate2's ``encode()`` + ``generate()`` APIs.
+
+    Args:
+        transcriber: The shared ``WhisperModel`` instance.
+        max_batch_size: Maximum number of requests per batch.
+        batch_window_ms: Maximum time (ms) to wait for the batch to fill
+            after the first request arrives.
+    """
+
+    def __init__(
+        self,
+        transcriber,
+        max_batch_size: int = 8,
+        batch_window_ms: int = 50,
+    ):
+        self.transcriber = transcriber
+        self.max_batch_size = max_batch_size
+        self.batch_window_ms = batch_window_ms
+        self._queue: queue.Queue = queue.Queue()
+        self._stop_event = threading.Event()
+        self._thread: Optional[threading.Thread] = None
+
+    def start(self):
+        """Start the background batch worker thread."""
+        self._thread = threading.Thread(target=self._worker_loop, daemon=True)
+        self._thread.start()
+        logging.info(
+            f"[BatchInference] Started (max_batch={self.max_batch_size}, "
+            f"window={self.batch_window_ms}ms)"
+        )
+
+    def stop(self):
+        """Signal the worker to stop and wait for it to finish."""
+        self._stop_event.set()
+        if self._thread:
+            self._thread.join(timeout=5)
+
+    def submit(self, request: BatchRequest):
+        """Submit an inference request to the batch queue.
+
+        Args:
+            request: The ``BatchRequest`` to enqueue.  The caller should
+                then call ``request.future.wait()`` to block until the
+                result is ready.
+        """
+        self._queue.put(request)
+
+    # -------------------------------------------------------------------------
+    # Worker loop
+    # -------------------------------------------------------------------------
+
+    def _worker_loop(self):
+        """Main loop: collect requests into batches and process them."""
+        while not self._stop_event.is_set():
+            batch: List[BatchRequest] = []
+
+            # Block until first request arrives
+            try:
+                first = self._queue.get(timeout=0.5)
+                batch.append(first)
+            except queue.Empty:
+                continue
+
+            # Collect more requests within the batch window
+            deadline = time.monotonic() + (self.batch_window_ms / 1000.0)
+            while len(batch) < self.max_batch_size:
+                remaining = deadline - time.monotonic()
+                if remaining <= 0:
+                    break
+                try:
+                    item = self._queue.get(timeout=remaining)
+                    batch.append(item)
+                except queue.Empty:
+                    break
+
+            # Process the collected batch
+            try:
+                self._process_batch(batch)
+            except Exception as e:
+                logging.error(f"[BatchInference] Batch processing error: {e}")
+                for req in batch:
+                    if not req.future.is_set():
+                        req.error = e
+                        req.future.set()
+
+    # -------------------------------------------------------------------------
+    # Batch processing
+    # -------------------------------------------------------------------------
+
+    def _process_batch(self, batch: List[BatchRequest]):
+        """Dispatch to single or multi-item processing."""
+        if len(batch) == 1:
+            self._process_single(batch[0])
+            return
+
+        logging.info(f"[BatchInference] Processing batch of {len(batch)}")
+        self._process_multi(batch)
+
+    def _process_single(self, req: BatchRequest):
+        """Process a single request using standard ``transcriber.transcribe()``.
+
+        This path is used when only one request is available in the batch
+        window, ensuring identical behavior to the non-batched code path.
+        """
+        try:
+            result, info = self.transcriber.transcribe(
+                req.audio,
+                language=req.language,
+                task=req.task,
+                initial_prompt=req.initial_prompt,
+                vad_filter=req.use_vad,
+                vad_parameters=req.vad_parameters if req.use_vad else None,
+            )
+            # Materialize the generator into a list
+            req.result = list(result) if result is not None else []
+            req.info = info
+        except Exception as e:
+            req.error = e
+        finally:
+            req.future.set()
+
+    def _process_multi(self, batch: List[BatchRequest]):
+        """Batched GPU path: encode + generate for multiple sessions at once.
+
+        Pipeline:
+            1. Per-item CPU preprocessing (VAD filtering + mel feature extraction)
+            2. Batch GPU encode — single ``transcriber.encode()`` call
+            3. Per-item prompt construction (handles different languages/tasks)
+            4. Batch GPU generate — single ``transcriber.model.generate()`` call
+            5. Per-item segment parsing and result dispatch
+        """
+        # Step 1: Per-item CPU preprocessing (VAD + feature extraction)
+        preprocessed = []
+        for req in batch:
+            try:
+                audio = req.audio
+                speech_chunks = None
+
+                if req.use_vad:
+                    vad_params = req.vad_parameters or {}
+                    vad_opts = VadOptions(**vad_params) if isinstance(vad_params, dict) else vad_params
+                    speech_chunks = get_speech_timestamps(audio, vad_opts)
+                    if speech_chunks:
+                        audio_chunks, _ = collect_chunks(audio, speech_chunks)
+                        audio = np.concatenate(audio_chunks, axis=0) if audio_chunks else audio
+
+                if audio.shape[0] == 0:
+                    # No speech detected — return empty result immediately
+                    req.result = []
+                    req.info = self._make_info(req, 0.0, 0.0)
+                    req.future.set()
+                    continue
+
+                duration = audio.shape[0] / self.transcriber.feature_extractor.sampling_rate
+                features = self.transcriber.feature_extractor(audio)
+                features = pad_or_trim(features)  # -> [n_mels, 3000]
+                preprocessed.append((req, features, audio, duration, speech_chunks))
+            except Exception as e:
+                req.error = e
+                req.future.set()
+
+        if not preprocessed:
+            return
+
+        try:
+            # Step 2: Batch GPU encode
+            feature_batch = np.stack([p[1] for p in preprocessed])  # [B, n_mels, 3000]
+            encoder_output = self.transcriber.encode(feature_batch)
+
+            # Step 3: Build per-item prompts (handles different languages/tasks)
+            tokenizers_list = []
+            prompts = []
+            resolved_languages = []
+
+            for i, (req, features, audio, duration, speech_chunks) in enumerate(preprocessed):
+                lang = req.language
+                # If language unknown, detect from encoder output
+                if lang is None:
+                    try:
+                        lang_results = self.transcriber.model.detect_language(encoder_output)
+                        if lang_results and len(lang_results) > i:
+                            detected = lang_results[i]
+                            if detected:
+                                lang = detected[0][0].strip("<|>")
+                    except Exception:
+                        lang = "en"  # fallback
+
+                resolved_languages.append(lang or "en")
+
+                tokenizer = Tokenizer(
+                    self.transcriber.hf_tokenizer,
+                    self.transcriber.model.is_multilingual,
+                    task=req.task,
+                    language=lang or "en",
+                )
+
+                previous_tokens = []
+                if req.initial_prompt:
+                    previous_tokens = tokenizer.encode(" " + req.initial_prompt.strip())
+
+                prompt = self.transcriber.get_prompt(
+                    tokenizer,
+                    previous_tokens=previous_tokens,
+                    without_timestamps=False,
+                )
+                tokenizers_list.append(tokenizer)
+                prompts.append(prompt)
+
+            # Step 4: Batch GPU generate
+            suppress_tokens = get_suppressed_tokens(tokenizers_list[0], [-1])
+
+            results = self.transcriber.model.generate(
+                encoder_output,
+                prompts,
+                beam_size=5,
+                patience=1,
+                length_penalty=1,
+                max_length=self.transcriber.max_length,
+                suppress_blank=True,
+                suppress_tokens=suppress_tokens,
+                return_scores=True,
+                return_no_speech_prob=True,
+                sampling_temperature=0.0,
+                repetition_penalty=1,
+                no_repeat_ngram_size=0,
+            )
+
+            # Step 5: Per-item segment parsing and result dispatch
+            for i, (req, features, audio, duration, speech_chunks) in enumerate(preprocessed):
+                try:
+                    tokenizer = tokenizers_list[i]
+                    gen_result = results[i]
+
+                    tokens = gen_result.sequences_ids[0]
+                    seq_len = len(tokens)
+                    cum_logprob = gen_result.scores[0] * seq_len
+                    avg_logprob = cum_logprob / (seq_len + 1) if seq_len > 0 else 0.0
+
+                    segment_size = int(ceil(duration) * self.transcriber.frames_per_second)
+
+                    subsegments, _, _ = self.transcriber._split_segments_by_timestamps(
+                        tokenizer=tokenizer,
+                        tokens=tokens,
+                        time_offset=0,
+                        segment_size=segment_size,
+                        segment_duration=duration,
+                        seek=0,
+                    )
+
+                    segments = []
+                    for seg_idx, subseg in enumerate(subsegments):
+                        text = tokenizer.decode(subseg["tokens"]).strip()
+                        if not text:
+                            continue
+                        segments.append(Segment(
+                            id=seg_idx,
+                            seek=subseg.get("seek", 0),
+                            start=subseg["start"],
+                            end=subseg["end"],
+                            text=text,
+                            tokens=subseg["tokens"],
+                            avg_logprob=avg_logprob,
+                            compression_ratio=get_compression_ratio(text),
+                            no_speech_prob=gen_result.no_speech_prob,
+                            words=None,
+                            temperature=0.0,
+                        ))
+
+                    req.result = segments
+                    req.info = self._make_info(
+                        req, duration, duration,
+                        language=resolved_languages[i],
+                    )
+                except Exception as e:
+                    req.error = e
+                finally:
+                    req.future.set()
+
+        except Exception as e:
+            logging.error(f"[BatchInference] GPU batch error: {e}")
+            for req, *_ in preprocessed:
+                if not req.future.is_set():
+                    req.error = e
+                    req.future.set()
+
+    def _make_info(self, req, duration, duration_after_vad, language=None):
+        """Build a ``TranscriptionInfo`` for the given request."""
+        return TranscriptionInfo(
+            language=language or req.language or "en",
+            language_probability=1.0,
+            duration=duration,
+            duration_after_vad=duration_after_vad,
+            all_language_probs=None,
+            transcription_options=None,
+            vad_options=None,
+        )

whisper_live/client.py

@@ -30,9 +30,19 @@ class Client:
         model="small",
         srt_file_path="output.srt",
         use_vad=True,
+        use_wss=False,
         log_transcription=True,
-        max_clients=4,
-        max_connection_time=600,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+        transcription_callback=None,
+        enable_translation=False,
+        target_language="fr",
+        translation_callback=None,
+        translation_srt_file_path="output_translated.srt",
+        enable_timestamps=False,
+        display_segments=4,
     ):
         """
         Initializes a Client instance for audio recording and streaming to a server.
@@ -50,8 +60,15 @@ class Client:
             srt_file_path (str, optional): The file path to save the output SRT file. Default is "output.srt".
             use_vad (bool, optional): Whether to enable voice activity detection. Default is True.
             log_transcription (bool, optional): Whether to log transcription output to the console. Default is True.
-            max_clients (int, optional): Maximum number of client connections allowed. Default is 4.
-            max_connection_time (int, optional): Maximum allowed connection time in seconds. Default is 600.
+            send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
+            no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
+            clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
+            same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
+            transcription_callback (callable, optional): A callback function to handle transcription results. Default is None.
+            enable_translation (float, optional): Whether to enable translation from any to any language. Defaults to False.
+            target_language (str, optional): Target language for translation. Defaults to 'fr'.
+            translation_callback (callable, optional): A callback function to handle translation results. Default is None.
+            translation_srt_file_path (str, optional): The file path to save the translated output SRT file. Default is "output_translated.srt".
         """
         self.recording = False
         self.task = "transcribe"
@@ -64,19 +81,32 @@ class Client:
         self.server_error = False
         self.srt_file_path = srt_file_path
         self.use_vad = use_vad
+        self.use_wss = use_wss
         self.last_segment = None
         self.last_received_segment = None
         self.log_transcription = log_transcription
-        self.max_clients = max_clients
-        self.max_connection_time = max_connection_time
+        self.send_last_n_segments = send_last_n_segments
+        self.no_speech_thresh = no_speech_thresh
+        self.clip_audio = clip_audio
+        self.same_output_threshold = same_output_threshold
+        self.transcription_callback = transcription_callback
 
+        # Translation-specific attributes
+        self.enable_translation = enable_translation
+        self.target_language = target_language
+        self.translation_callback = translation_callback
+        self.translation_srt_file_path = translation_srt_file_path
+        self.last_translated_segment = None
         if translate:
             self.task = "translate"
+        self.enable_timestamps = enable_timestamps
+        self.display_segments = display_segments
 
         self.audio_bytes = None
 
         if host is not None and port is not None:
-            socket_url = f"ws://{host}:{port}"
+            socket_protocol = 'wss' if self.use_wss else "ws"
+            socket_url = f"{socket_protocol}://{host}:{port}"
             self.client_socket = websocket.WebSocketApp(
                 socket_url,
                 on_open=lambda ws: self.on_open(ws),
@@ -94,10 +124,11 @@ class Client:
 
         # start websocket client in a thread
         self.ws_thread = threading.Thread(target=self.client_socket.run_forever)
-        self.ws_thread.setDaemon(True)
+        self.ws_thread.daemon = True
         self.ws_thread.start()
 
         self.transcript = []
+        self.translated_transcript = []
         print("[INFO]: * recording")
 
     def handle_status_messages(self, message_data):
@@ -112,28 +143,77 @@ class Client:
         elif status == "WARNING":
             print(f"Message from Server: {message_data['message']}")
 
-    def process_segments(self, segments):
+    def process_segments(self, segments, translated=False):
         """Processes transcript segments."""
         text = []
         for i, seg in enumerate(segments):
             if not text or text[-1] != seg["text"]:
-                text.append(seg["text"])
+                text.append(seg["text"].strip())
                 if i == len(segments) - 1 and not seg.get("completed", False):
                     self.last_segment = seg
-                elif (self.server_backend == "faster_whisper" and seg.get("completed", False) and
-                      (not self.transcript or
-                        float(seg['start']) >= float(self.transcript[-1]['end']))):
-                    self.transcript.append(seg)
+                elif self.server_backend == "faster_whisper" and seg.get("completed", False):
+                    if translated:
+                        if (not self.translated_transcript or float(seg['start']) >= float(self.translated_transcript[-1]['end'])):
+                            self.translated_transcript.append(seg)
+                    else:
+                        if (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"]
+        if not translated:
+            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"]
+
+        # call the transcription callback if provided
+        if translated:
+            if self.translation_callback and callable(self.translation_callback):
+                try:
+                    self.translation_callback(" ".join(text), segments) # string, list
+                except Exception as e:
+                    print(f"[WARN] translation_callback raised: {e}")
+                return
+        else:
+            if self.transcription_callback and callable(self.transcription_callback):
+                try:
+                    self.transcription_callback(" ".join(text), segments) # string, list
+                except Exception as e:
+                    print(f"[WARN] transcription_callback raised: {e}")
+                return
 
         if self.log_transcription:
-            # Truncate to last 3 entries for brevity.
-            text = text[-3:]
-            utils.clear_screen()
-            utils.print_transcript(text)
+            if self.enable_timestamps:
+                original_text_with_timestamps = [
+                    {"start": seg["start"], "end": seg["end"], "text": seg["text"]}
+                    for seg in self.transcript[-self.display_segments:]]
+                if self.last_segment is not None and not any(
+                    data.get("text") == self.last_segment["text"]
+                    for data in original_text_with_timestamps):
+                    original_text_with_timestamps.append({
+                        "start": self.last_segment["start"],
+                        "end": self.last_segment["end"],
+                        "text": self.last_segment["text"]
+                    })
+                utils.clear_screen()
+                utils.print_transcript(original_text_with_timestamps, timestamps=True)
+
+                if self.enable_translation:
+                    print(f"\n\nTRANSLATION to {self.target_language}:")
+                    utils.print_transcript([
+                        {"start": seg["start"], "end": seg["end"], "text": seg["text"]}
+                        for seg in self.translated_transcript[-self.display_segments:]
+                    ], timestamps=True)
+
+            else:
+                original_text = [seg["text"] for seg in self.transcript[-self.display_segments:]]
+                if self.last_segment is not None and self.last_segment["text"] not in original_text:
+                    original_text.append(self.last_segment["text"])
+                utils.clear_screen()
+                utils.print_transcript(original_text)
+
+                if self.enable_translation:
+                    print(f"\n\nTRANSLATION to {self.target_language}:")
+                    utils.print_transcript([seg["text"] for seg in self.translated_transcript[-self.display_segments:]], translated=True)
+
 
     def on_message(self, ws, message):
         """
@@ -179,6 +259,9 @@ class Client:
 
         if "segments" in message.keys():
             self.process_segments(message["segments"])
+        
+        if "translated_segments" in message.keys():
+            self.process_segments(message["translated_segments"], translated=True)
 
     def on_error(self, ws, error):
         print(f"[ERROR] WebSocket Error: {error}")
@@ -210,8 +293,12 @@ class Client:
                     "task": self.task,
                     "model": self.model,
                     "use_vad": self.use_vad,
-                    "max_clients": self.max_clients,
-                    "max_connection_time": self.max_connection_time,
+                    "send_last_n_segments": self.send_last_n_segments,
+                    "no_speech_thresh": self.no_speech_thresh,
+                    "clip_audio": self.clip_audio,
+                    "same_output_threshold": self.same_output_threshold,
+                    "enable_translation": self.enable_translation,
+                    "target_language": self.target_language,
                 }
             )
         )
@@ -271,6 +358,9 @@ class Client:
                 self.transcript.append(self.last_segment)
             utils.create_srt_file(self.transcript, output_path)
 
+        if self.enable_translation:
+            utils.create_srt_file(self.translated_transcript, self.translation_srt_file_path)
+
     def wait_before_disconnect(self):
         """Waits a bit before disconnecting in order to process pending responses."""
         assert self.last_response_received
@@ -390,14 +480,18 @@ class TranscriptionTeeClient:
 
         # 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,
-            )
+            if self.mute_audio_playback:
+                self.stream = None
+            else:
+                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,
+                )
+
             chunk_duration = self.chunk / float(wavfile.getframerate())
             try:
                 while any(client.recording for client in self.clients):
@@ -418,7 +512,8 @@ class TranscriptionTeeClient:
                     client.wait_before_disconnect()
                 self.multicast_packet(Client.END_OF_AUDIO.encode('utf-8'), True)
                 self.write_all_clients_srt()
-                self.stream.close()
+                if self.stream:
+                    self.stream.close()
                 self.close_all_clients()
 
             except KeyboardInterrupt:
@@ -665,7 +760,7 @@ 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
+    Acts as a high-level client for audio transcription tasksoutput_transcription_path using a WebSocket connection. It can be used
     to send audio data for transcription to a server and receive transcribed text segments.
 
     Args:
@@ -679,9 +774,16 @@ class TranscriptionClient(TranscriptionTeeClient):
         output_recording_filename (str, optional): Path to save the output recording WAV file. Default is "./output_recording.wav".
         output_transcription_path (str, optional): File path to save the output transcription (SRT file). Default is "./output.srt".
         log_transcription (bool, optional): Whether to log transcription output to the console. Default is True.
-        max_clients (int, optional): Maximum number of client connections allowed. Default is 4.
-        max_connection_time (int, optional): Maximum allowed connection time in seconds. Default is 600.
         mute_audio_playback (bool, optional): If True, mutes audio playback during file playback. Default is False.
+        send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
+        no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
+        clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
+        same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
+        transcription_callback (callable, optional): A callback function to handle transcription results. Default is None.
+        enable_translation (float, optional): Whether to enable translation from any to any language. Defaults to False.
+        target_language (str, optional): Target language for translation. Defaults to 'fr'.
+        translation_callback (callable, optional): A callback function to handle translation results. Default is None.
+        translation_srt_file_path (str, optional): The file path to save the translated output SRT file. Default is "output_translated.srt".
 
     Attributes:
         client (Client): An instance of the underlying Client class responsible for handling the WebSocket connection.
@@ -701,24 +803,53 @@ class TranscriptionClient(TranscriptionTeeClient):
         translate=False,
         model="small",
         use_vad=True,
+        use_wss=False,
         save_output_recording=False,
         output_recording_filename="./output_recording.wav",
         output_transcription_path="./output.srt",
         log_transcription=True,
-        max_clients=4,
-        max_connection_time=600,
         mute_audio_playback=False,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+        transcription_callback=None,
+        enable_translation=False,
+        target_language="fr",
+        translation_callback=None,
+        translation_srt_file_path="./output_translated.srt",
+        enable_timestamps=False,
+        display_segments=4,
     ):
         self.client = Client(
-            host, port, lang, translate, model, srt_file_path=output_transcription_path,
-            use_vad=use_vad, log_transcription=log_transcription, max_clients=max_clients,
-            max_connection_time=max_connection_time
+            host,
+            port,
+            lang,
+            translate,
+            model,
+            srt_file_path=output_transcription_path,
+            use_vad=use_vad,
+            use_wss=use_wss,
+            log_transcription=log_transcription,
+            send_last_n_segments=send_last_n_segments,
+            no_speech_thresh=no_speech_thresh,
+            clip_audio=clip_audio,
+            same_output_threshold=same_output_threshold,
+            transcription_callback=transcription_callback,
+            enable_translation=enable_translation,
+            target_language=target_language,
+            translation_callback=translation_callback,
+            translation_srt_file_path=translation_srt_file_path,
+            enable_timestamps=enable_timestamps,
+            display_segments=display_segments,
         )
 
         if save_output_recording and not output_recording_filename.endswith(".wav"):
             raise ValueError(f"Please provide a valid `output_recording_filename`: {output_recording_filename}")
         if not output_transcription_path.endswith(".srt"):
             raise ValueError(f"Please provide a valid `output_transcription_path`: {output_transcription_path}. The file extension should be `.srt`.")
+        if not translation_srt_file_path.endswith(".srt"):
+            raise ValueError(f"Please provide a valid `translation_srt_file_path`: {translation_srt_file_path}. The file extension should be `.srt`.")
         TranscriptionTeeClient.__init__(
             self,
             [self.client],

whisper_live/transcriber/transcriber_faster_whisper.py

@@ -27,7 +27,6 @@ from faster_whisper.vad import (
     VadOptions,
     collect_chunks,
     get_speech_timestamps,
-    merge_segments,
 )
 
 
@@ -407,8 +406,7 @@ class BatchedInferencePipeline:
                         **vad_parameters, max_speech_duration_s=chunk_length
                     )
 
-                active_segments = get_speech_timestamps(audio, vad_parameters)
-                clip_timestamps = merge_segments(active_segments, vad_parameters)
+                clip_timestamps = get_speech_timestamps(audio, vad_parameters)
             # run the audio if it is less than 30 sec even without clip_timestamps
             elif duration < chunk_length:
                 clip_timestamps = [{"start": 0, "end": audio.shape[0]}]

whisper_live/transcriber/transcriber_openvino.py

@@ -0,0 +1,23 @@
+import librosa
+import os
+
+import openvino_genai as ov_genai
+import huggingface_hub as hf_hub
+
+
+class WhisperOpenVINO(object):
+    def __init__(self, model_id="OpenVINO/whisper-tiny-fp16-ov", device="CPU", language="en", task="transcribe"):
+        model_path = model_id.split('/')[-1]
+        cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "openvino_whisper_models")
+        os.makedirs(cache_dir, exist_ok=True)
+        model_path = os.path.join(cache_dir, model_path)
+        if not os.path.exists(model_path):
+            hf_hub.snapshot_download(model_id, local_dir=model_path)
+        self.model = ov_genai.WhisperPipeline(str(model_path), device=device)
+        self.language = language
+        self.task = task
+
+    def transcribe(self, input_audio):
+        outputs = self.model.generate(input_audio, return_timestamps=True, language=self.language, task=self.task)
+        outputs = [seg for seg in outputs.chunks]
+        return outputs

whisper_live/transcriber/transcriber_tensorrt.py

@@ -9,7 +9,12 @@ 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, load_audio_wav_format, pad_or_trim, load_audio)
+from whisper_live.transcriber.tensorrt_utils import (
+    mel_filters, 
+    load_audio_wav_format, 
+    pad_or_trim, 
+    load_audio
+)
 
 import tensorrt_llm
 import tensorrt_llm.logger as logger
@@ -18,7 +23,8 @@ from tensorrt_llm._utils import (str_dtype_to_torch, str_dtype_to_trt,
 from tensorrt_llm.bindings import GptJsonConfig, KVCacheType
 from tensorrt_llm.runtime import PYTHON_BINDINGS, ModelConfig, SamplingConfig
 from tensorrt_llm.runtime.session import Session, TensorInfo
-
+if PYTHON_BINDINGS:
+    from tensorrt_llm.runtime import ModelRunnerCpp
 
 SAMPLE_RATE = 16000
 N_FFT = 400
@@ -250,8 +256,17 @@ class WhisperDecoding:
 
 class WhisperTRTLLM(object):
 
-    def __init__(self, engine_dir, assets_dir=None, device=None, is_multilingual=False,
-                 language="en", task="transcribe"):
+    def __init__(self, 
+                 engine_dir,
+                 assets_dir=None,
+                 device=None,
+                 is_multilingual=False,
+                 language="en",
+                 task="transcribe",
+                 use_py_session=False,
+                 num_beams=1,
+                 debug_mode=False,
+                 max_output_len=96):
         world_size = 1
         runtime_rank = tensorrt_llm.mpi_rank()
         runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
@@ -263,13 +278,6 @@ class WhisperTRTLLM(object):
         self.num_languages = encoder_config['num_languages']
         is_multilingual = (decoder_config['vocab_size'] >= 51865)
 
-        self.encoder = WhisperEncoding(engine_dir)
-        self.decoder = WhisperDecoding(engine_dir,
-                                        runtime_mapping,
-                                        debug_mode=False)
-        self.n_mels = self.encoder.n_mels
-        # self.tokenizer = get_tokenizer(num_languages=self.encoder.num_languages,
-        #                                tokenizer_dir=assets_dir)
         self.device = device
         self.tokenizer = get_tokenizer(
             is_multilingual,
@@ -277,7 +285,28 @@ class WhisperTRTLLM(object):
             language=language,
             task=task,
         )
-        self.filters = mel_filters(self.device, self.encoder.n_mels, assets_dir)
+
+        if use_py_session:
+            self.encoder = WhisperEncoding(engine_dir)
+            self.decoder = WhisperDecoding(engine_dir,
+                                        runtime_mapping,
+                                        debug_mode=False)
+        else:
+            json_config = GptJsonConfig.parse_file(engine_dir / 'decoder' /
+                                                   'config.json')
+            assert json_config.model_config.supports_inflight_batching
+            runner_kwargs = dict(engine_dir=engine_dir,
+                                 is_enc_dec=True,
+                                 max_batch_size=1,
+                                 max_input_len=3000,
+                                 max_output_len=max_output_len,
+                                 max_beam_width=num_beams,
+                                 debug_mode=debug_mode,
+                                 kv_cache_free_gpu_memory_fraction=0.9,
+                                 cross_kv_cache_fraction=0.5)
+            self.model_runner_cpp = ModelRunnerCpp.from_dir(**runner_kwargs)
+        self.filters = mel_filters(self.device, self.n_mels, assets_dir)
+        self.use_py_session = use_py_session
 
     def log_mel_spectrogram(
         self,
@@ -350,16 +379,38 @@ class WhisperTRTLLM(object):
         prompt_id = torch.tensor(prompt_id)
         batch_size = mel.shape[0]
         decoder_input_ids = prompt_id.repeat(batch_size, 1)
-
-        encoder_output, encoder_output_lengths = self.encoder.get_audio_features(mel, mel_input_lengths)
-        encoder_max_input_length = torch.max(encoder_output_lengths).item()
-        output_ids = self.decoder.generate(decoder_input_ids,
-                                           encoder_output,
-                                           encoder_max_input_length,
-                                           encoder_output_lengths,
-                                           self.tokenizer.eot,
-                                           max_new_tokens=max_new_tokens,
-                                           num_beams=num_beams)
+        if self.use_py_session:
+            encoder_output, encoder_output_lengths = self.encoder.get_audio_features(mel, mel_input_lengths)
+            encoder_max_input_length = torch.max(encoder_output_lengths).item()
+            output_ids = self.decoder.generate(decoder_input_ids,
+                                            encoder_output,
+                                            encoder_max_input_length,
+                                            encoder_output_lengths,
+                                            self.tokenizer.eot,
+                                            max_new_tokens=max_new_tokens,
+                                            num_beams=num_beams)
+        else:
+            with torch.no_grad():
+                if isinstance(mel, list):
+                    mel = [
+                        m.transpose(1, 2).type(
+                            str_dtype_to_torch("float16")).squeeze(0)
+                        for m in mel
+                    ]
+                else:
+                    mel = mel.transpose(1, 2)
+                outputs = self.model_runner_cpp.generate(
+                    batch_input_ids=decoder_input_ids,
+                    encoder_input_features=mel,
+                    encoder_output_lengths=mel_input_lengths // 2,
+                    max_new_tokens=max_new_tokens,
+                    end_id=self.tokenizer.eot,
+                    pad_id=self.tokenizer.eot,
+                    num_beams=num_beams,
+                    output_sequence_lengths=True,
+                    return_dict=True)
+                torch.cuda.synchronize()
+                output_ids = outputs['output_ids'].cpu().numpy().tolist()
         texts = []
         for i in range(len(output_ids)):
             text = self.tokenizer.decode(output_ids[i][0]).strip()
@@ -374,7 +425,8 @@ class WhisperTRTLLM(object):
             batch_size=1,
             num_beams=1,
             padding_strategy="max",
-            ):
+            max_new_tokens=96,
+        ):
         mel = mel.type(str_dtype_to_torch(dtype))
         mel = mel.unsqueeze(0)
         # repeat the mel spectrogram to match the batch size
@@ -388,7 +440,13 @@ class WhisperTRTLLM(object):
                                              dtype=torch.int32,
                                              device=mel.device)
 
-        predictions = self.process_batch(mel, features_input_lengths, text_prefix, num_beams)
+        predictions = self.process_batch(
+            mel,
+            features_input_lengths,
+            text_prefix,
+            num_beams,
+            max_new_tokens=max_new_tokens
+        )
         prediction = predictions[0]
 
         # remove all special tokens in the prediction

whisper_live/utils.py

@@ -11,11 +11,16 @@ def clear_screen():
     os.system("cls" if os.name == "nt" else "clear")
 
 
-def print_transcript(text):
+def print_transcript(text, translated=False, timestamps=False):
     """Prints formatted transcript text."""
-    wrapper = textwrap.TextWrapper(width=60)
-    for line in wrapper.wrap(text="".join(text)):
-        print(line)
+    if timestamps:
+        for t in text:
+            print(f'[{t["start"]} -> {t["end"]}] {t["text"]}')
+    else:
+        wrapper = textwrap.TextWrapper(width=60)
+        text=" ".join(text) if translated else "".join(text)
+        for line in wrapper.wrap(text=text):
+            print(line)
 
 
 def format_time(s):