bump to 0.2.12

fixes #251
forwarded_allow_ips in core
2026-03-07 22:33:36 +00:00 · 2025-10-06 19:59:05 +02:00 · 2025-10-06 19:53:27 +02:00 · 2025-10-04 23:04:00 +02:00 · 2025-10-04 23:04:00 +02:00 · 2025-10-04 23:04:00 +02:00
33 changed files with 942 additions and 1690 deletions
--- a/README.md
+++ b/README.md
@@ -54,7 +54,15 @@ pip install whisperlivekit
 > - See [tokenizer.py](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py) for the list of all available languages.
 > - For HTTPS requirements, see the **Parameters** section for SSL configuration options.
- 
+#### Use it to capture audio from web pages.
 Go to `chrome-extension` for instructions.
 <p align="center">
 <img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/chrome-extension/demo-extension.png" alt="WhisperLiveKit Demo" width="600">
 </p>
 #### Optional Dependencies
@@ -132,6 +140,7 @@ async def websocket_endpoint(websocket: WebSocket):
 | Parameter | Description | Default |
 |-----------|-------------|---------|
 | `--model` | Whisper model size. List and recommandations [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/available_models.md) | `small` |
 | `--model-dir` | Directory containing Whisper model.bin and other files. Overrides `--model`. | `None` |
 | `--language` | List [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py). If you use `auto`, the model attempts to detect the language automatically, but it tends to bias towards English. | `auto` |
 | `--target-language` | If sets, activates translation using NLLB. Ex: `fr`. [118 languages available](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/translation/mapping_languages.py). If you want to translate to english, you should rather use `--task translate`, since Whisper can do it directly. | `None` |
 | `--task` | Set to `translate` to translate *only* to english, using Whisper translation. | `transcribe` |
@@ -144,6 +153,7 @@ async def websocket_endpoint(websocket: WebSocket):
 | `--port` | Server port | `8000` |
 | `--ssl-certfile` | Path to the SSL certificate file (for HTTPS support) | `None` |
 | `--ssl-keyfile` | Path to the SSL private key file (for HTTPS support) | `None` |
 | `--forwarded-allow-ips` | Ip or Ips allowed to reverse proxy the whisperlivekit-server. Supported types are  IP Addresses (e.g. 127.0.0.1), IP Networks (e.g. 10.100.0.0/16), or Literals (e.g. /path/to/socket.sock) | `None` |
 | `--pcm-input` | raw PCM (s16le) data is expected as input and FFmpeg will be bypassed. Frontend will use AudioWorklet instead of MediaRecorder | `False` |
 | Translation options | Description | Default |
@@ -161,6 +171,7 @@ async def websocket_endpoint(websocket: WebSocket):
 | SimulStreaming backend options | Description | Default |
 |-----------|-------------|---------|
 | `--disable-fast-encoder` | Disable Faster Whisper or MLX Whisper backends for the encoder (if installed). Inference can be slower but helpful when GPU memory is limited | `False` |
 | `--custom-alignment-heads` | Use your own alignment heads, useful when `--model-dir` is used | `None` |
 | `--frame-threshold` | AlignAtt frame threshold (lower = faster, higher = more accurate) | `25` |
 | `--beams` | Number of beams for beam search (1 = greedy decoding) | `1` |
 | `--decoder` | Force decoder type (`beam` or `greedy`) | `auto` |
--- a/architecture.png
+++ b/architecture.png
--- a/chrome-extension/README.md
+++ b/chrome-extension/README.md
@@ -1,11 +1,13 @@
-## WhisperLiveKit Chrome Extension v0.1.0
+## WhisperLiveKit Chrome Extension v0.1.1
-Capture the audio of your current tab, transcribe or translate it using WhisperliveKit. **Still unstable**
+Capture the audio of your current tab, transcribe diarize and translate it using WhisperliveKit, in Chrome and other Chromium-based browsers.
 > Currently, only the tab audio is captured; your microphone audio is not recorded.
 <img src="https://raw.githubusercontent.com/QuentinFuxa/WhisperLiveKit/refs/heads/main/chrome-extension/demo-extension.png" alt="WhisperLiveKit Demo" width="730">
 ## Running this extension
-1. Clone this repository.
+1. Run `python sync_extension.py` to copy frontend files to the `chrome-extension` directory.
-2. Load this directory in Chrome as an unpacked extension.
+2. Load the `chrome-extension` directory in Chrome as an unpacked extension.
 ## Devs:
--- a/chrome-extension/demo-extension.png
+++ b/chrome-extension/demo-extension.png
--- a/chrome-extension/live_transcription.js
+++ b/chrome-extension/live_transcription.js
@@ -1,669 +0,0 @@
 /* Theme, WebSocket, recording, rendering logic extracted from inline script and adapted for segmented theme control and WS caption */
 let isRecording = false;
 let websocket = null;
 let recorder = null;
 let chunkDuration = 100;
 let websocketUrl = "ws://localhost:8000/asr";
 let userClosing = false;
 let wakeLock = null;
 let startTime = null;
 let timerInterval = null;
 let audioContext = null;
 let analyser = null;
 let microphone = null;
 let waveCanvas = document.getElementById("waveCanvas");
 let waveCtx = waveCanvas.getContext("2d");
 let animationFrame = null;
 let waitingForStop = false;
 let lastReceivedData = null;
 let lastSignature = null;
 let availableMicrophones = [];
 let selectedMicrophoneId = null;
 waveCanvas.width = 60 * (window.devicePixelRatio || 1);
 waveCanvas.height = 30 * (window.devicePixelRatio || 1);
 waveCtx.scale(window.devicePixelRatio || 1, window.devicePixelRatio || 1);
 const statusText = document.getElementById("status");
 const recordButton = document.getElementById("recordButton");
 const chunkSelector = document.getElementById("chunkSelector");
 const websocketInput = document.getElementById("websocketInput");
 const websocketDefaultSpan = document.getElementById("wsDefaultUrl");
 const linesTranscriptDiv = document.getElementById("linesTranscript");
 const timerElement = document.querySelector(".timer");
 const themeRadios = document.querySelectorAll('input[name="theme"]');
 const microphoneSelect = document.getElementById("microphoneSelect");
 const settingsToggle = document.getElementById("settingsToggle");
 const settingsDiv = document.querySelector(".settings");
 chrome.runtime.onInstalled.addListener((details) => {
    if (details.reason.search(/install/g) === -1) {
        return
    }
    chrome.tabs.create({
        url: chrome.runtime.getURL("welcome.html"),
        active: true
    })
 })
 function getWaveStroke() {
  const styles = getComputedStyle(document.documentElement);
  const v = styles.getPropertyValue("--wave-stroke").trim();
  return v || "#000";
 }
 let waveStroke = getWaveStroke();
 function updateWaveStroke() {
  waveStroke = getWaveStroke();
 }
 function applyTheme(pref) {
  if (pref === "light") {
    document.documentElement.setAttribute("data-theme", "light");
  } else if (pref === "dark") {
    document.documentElement.setAttribute("data-theme", "dark");
  } else {
    document.documentElement.removeAttribute("data-theme");
  }
  updateWaveStroke();
 }
 // Persisted theme preference
 const savedThemePref = localStorage.getItem("themePreference") || "system";
 applyTheme(savedThemePref);
 if (themeRadios.length) {
  themeRadios.forEach((r) => {
    r.checked = r.value === savedThemePref;
    r.addEventListener("change", () => {
      if (r.checked) {
        localStorage.setItem("themePreference", r.value);
        applyTheme(r.value);
      }
    });
  });
 }
 // React to OS theme changes when in "system" mode
 const darkMq = window.matchMedia && window.matchMedia("(prefers-color-scheme: dark)");
 const handleOsThemeChange = () => {
  const pref = localStorage.getItem("themePreference") || "system";
  if (pref === "system") updateWaveStroke();
 };
 if (darkMq && darkMq.addEventListener) {
  darkMq.addEventListener("change", handleOsThemeChange);
 } else if (darkMq && darkMq.addListener) {
  // deprecated, but included for Safari compatibility
  darkMq.addListener(handleOsThemeChange);
 }
 async function enumerateMicrophones() {
  try {
      const micPermission = await navigator.permissions.query({
    name: "microphone",
  });
    const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
    stream.getTracks().forEach(track => track.stop());
    const devices = await navigator.mediaDevices.enumerateDevices();
    availableMicrophones = devices.filter(device => device.kind === 'audioinput');
    populateMicrophoneSelect();
    console.log(`Found ${availableMicrophones.length} microphone(s)`);
  } catch (error) {
    console.error('Error enumerating microphones:', error);
    statusText.textContent = "Error accessing microphones. Please grant permission.";
  }
 }
 function populateMicrophoneSelect() {
  if (!microphoneSelect) return;
  microphoneSelect.innerHTML = '<option value="">Default Microphone</option>';
  availableMicrophones.forEach((device, index) => {
    const option = document.createElement('option');
    option.value = device.deviceId;
    option.textContent = device.label || `Microphone ${index + 1}`;
    microphoneSelect.appendChild(option);
  });
  const savedMicId = localStorage.getItem('selectedMicrophone');
  if (savedMicId && availableMicrophones.some(mic => mic.deviceId === savedMicId)) {
    microphoneSelect.value = savedMicId;
    selectedMicrophoneId = savedMicId;
  }
 }
 function handleMicrophoneChange() {
  selectedMicrophoneId = microphoneSelect.value || null;
  localStorage.setItem('selectedMicrophone', selectedMicrophoneId || '');
  const selectedDevice = availableMicrophones.find(mic => mic.deviceId === selectedMicrophoneId);
  const deviceName = selectedDevice ? selectedDevice.label : 'Default Microphone';
  console.log(`Selected microphone: ${deviceName}`);
  statusText.textContent = `Microphone changed to: ${deviceName}`;
  if (isRecording) {
    statusText.textContent = "Switching microphone... Please wait.";
    stopRecording().then(() => {
      setTimeout(() => {
        toggleRecording();
      }, 1000);
    });
  }
 }
 // Helpers
 function fmt1(x) {
  const n = Number(x);
  return Number.isFinite(n) ? n.toFixed(1) : x;
 }
 // Default WebSocket URL computation
 const host = window.location.hostname || "localhost";
 const port = window.location.port;
 const protocol = window.location.protocol === "https:" ? "wss" : "ws";
 const defaultWebSocketUrl = websocketUrl;
 // Populate default caption and input
 if (websocketDefaultSpan) websocketDefaultSpan.textContent = defaultWebSocketUrl;
 websocketInput.value = defaultWebSocketUrl;
 websocketUrl = defaultWebSocketUrl;
 // Optional chunk selector (guard for presence)
 if (chunkSelector) {
  chunkSelector.addEventListener("change", () => {
    chunkDuration = parseInt(chunkSelector.value);
  });
 }
 // WebSocket input change handling
 websocketInput.addEventListener("change", () => {
  const urlValue = websocketInput.value.trim();
  if (!urlValue.startsWith("ws://") && !urlValue.startsWith("wss://")) {
    statusText.textContent = "Invalid WebSocket URL (must start with ws:// or wss://)";
    return;
  }
  websocketUrl = urlValue;
  statusText.textContent = "WebSocket URL updated. Ready to connect.";
 });
 function setupWebSocket() {
  return new Promise((resolve, reject) => {
    try {
      websocket = new WebSocket(websocketUrl);
    } catch (error) {
      statusText.textContent = "Invalid WebSocket URL. Please check and try again.";
      reject(error);
      return;
    }
    websocket.onopen = () => {
      statusText.textContent = "Connected to server.";
      resolve();
    };
    websocket.onclose = () => {
      if (userClosing) {
        if (waitingForStop) {
          statusText.textContent = "Processing finalized or connection closed.";
          if (lastReceivedData) {
            renderLinesWithBuffer(
              lastReceivedData.lines || [],
              lastReceivedData.buffer_diarization || "",
              lastReceivedData.buffer_transcription || "",
              0,
              0,
              true
            );
          }
        }
      } else {
        statusText.textContent = "Disconnected from the WebSocket server. (Check logs if model is loading.)";
        if (isRecording) {
          stopRecording();
        }
      }
      isRecording = false;
      waitingForStop = false;
      userClosing = false;
      lastReceivedData = null;
      websocket = null;
      updateUI();
    };
    websocket.onerror = () => {
      statusText.textContent = "Error connecting to WebSocket.";
      reject(new Error("Error connecting to WebSocket"));
    };
    websocket.onmessage = (event) => {
      const data = JSON.parse(event.data);
      if (data.type === "ready_to_stop") {
        console.log("Ready to stop received, finalizing display and closing WebSocket.");
        waitingForStop = false;
        if (lastReceivedData) {
          renderLinesWithBuffer(
            lastReceivedData.lines || [],
            lastReceivedData.buffer_diarization || "",
            lastReceivedData.buffer_transcription || "",
            0,
            0,
            true
          );
        }
        statusText.textContent = "Finished processing audio! Ready to record again.";
        recordButton.disabled = false;
        if (websocket) {
          websocket.close();
        }
        return;
      }
      lastReceivedData = data;
      const {
        lines = [],
        buffer_transcription = "",
        buffer_diarization = "",
        remaining_time_transcription = 0,
        remaining_time_diarization = 0,
        status = "active_transcription",
      } = data;
      renderLinesWithBuffer(
        lines,
        buffer_diarization,
        buffer_transcription,
        remaining_time_diarization,
        remaining_time_transcription,
        false,
        status
      );
    };
  });
 }
 function renderLinesWithBuffer(
  lines,
  buffer_diarization,
  buffer_transcription,
  remaining_time_diarization,
  remaining_time_transcription,
  isFinalizing = false,
  current_status = "active_transcription"
 ) {
  if (current_status === "no_audio_detected") {
    linesTranscriptDiv.innerHTML =
      "<p style='text-align: center; color: var(--muted); margin-top: 20px;'><em>No audio detected...</em></p>";
    return;
  }
  const showLoading = !isFinalizing && (lines || []).some((it) => it.speaker == 0);
  const showTransLag = !isFinalizing && remaining_time_transcription > 0;
  const showDiaLag = !isFinalizing && !!buffer_diarization && remaining_time_diarization > 0;
  const signature = JSON.stringify({
    lines: (lines || []).map((it) => ({ speaker: it.speaker, text: it.text, start: it.start, end: it.end })),
    buffer_transcription: buffer_transcription || "",
    buffer_diarization: buffer_diarization || "",
    status: current_status,
    showLoading,
    showTransLag,
    showDiaLag,
    isFinalizing: !!isFinalizing,
  });
  if (lastSignature === signature) {
    const t = document.querySelector(".lag-transcription-value");
    if (t) t.textContent = fmt1(remaining_time_transcription);
    const d = document.querySelector(".lag-diarization-value");
    if (d) d.textContent = fmt1(remaining_time_diarization);
    const ld = document.querySelector(".loading-diarization-value");
    if (ld) ld.textContent = fmt1(remaining_time_diarization);
    return;
  }
  lastSignature = signature;
  const linesHtml = (lines || [])
    .map((item, idx) => {
      let timeInfo = "";
      if (item.start !== undefined && item.end !== undefined) {
        timeInfo = ` ${item.start} - ${item.end}`;
      }
      let speakerLabel = "";
      if (item.speaker === -2) {
        speakerLabel = `<span class="silence">Silence<span id='timeInfo'>${timeInfo}</span></span>`;
      } else if (item.speaker == 0 && !isFinalizing) {
        speakerLabel = `<span class='loading'><span class="spinner"></span><span id='timeInfo'><span class="loading-diarization-value">${fmt1(
          remaining_time_diarization
        )}</span> second(s) of audio are undergoing diarization</span></span>`;
      } else if (item.speaker !== 0) {
        speakerLabel = `<span id="speaker">Speaker ${item.speaker}<span id='timeInfo'>${timeInfo}</span></span>`;
      }
      let currentLineText = item.text || "";
      if (idx === lines.length - 1) {
        if (!isFinalizing && item.speaker !== -2) {
          if (remaining_time_transcription > 0) {
            speakerLabel += `<span class="label_transcription"><span class="spinner"></span>Lag <span id='timeInfo'><span class="lag-transcription-value">${fmt1(
              remaining_time_transcription
            )}</span>s</span></span>`;
          }
          if (buffer_diarization && remaining_time_diarization > 0) {
            speakerLabel += `<span class="label_diarization"><span class="spinner"></span>Lag<span id='timeInfo'><span class="lag-diarization-value">${fmt1(
              remaining_time_diarization
            )}</span>s</span></span>`;
          }
        }
        if (buffer_diarization) {
          if (isFinalizing) {
            currentLineText +=
              (currentLineText.length > 0 && buffer_diarization.trim().length > 0 ? " " : "") + buffer_diarization.trim();
          } else {
            currentLineText += `<span class="buffer_diarization">${buffer_diarization}</span>`;
          }
        }
        if (buffer_transcription) {
          if (isFinalizing) {
            currentLineText +=
              (currentLineText.length > 0 && buffer_transcription.trim().length > 0 ? " " : "") +
              buffer_transcription.trim();
          } else {
            currentLineText += `<span class="buffer_transcription">${buffer_transcription}</span>`;
          }
        }
      }
      return currentLineText.trim().length > 0 || speakerLabel.length > 0
        ? `<p>${speakerLabel}<br/><div class='textcontent'>${currentLineText}</div></p>`
        : `<p>${speakerLabel}<br/></p>`;
    })
    .join("");
  linesTranscriptDiv.innerHTML = linesHtml;
  window.scrollTo({ top: document.body.scrollHeight, behavior: "smooth" });
 }
 function updateTimer() {
  if (!startTime) return;
  const elapsed = Math.floor((Date.now() - startTime) / 1000);
  const minutes = Math.floor(elapsed / 60).toString().padStart(2, "0");
  const seconds = (elapsed % 60).toString().padStart(2, "0");
  timerElement.textContent = `${minutes}:${seconds}`;
 }
 function drawWaveform() {
  if (!analyser) return;
  const bufferLength = analyser.frequencyBinCount;
  const dataArray = new Uint8Array(bufferLength);
  analyser.getByteTimeDomainData(dataArray);
  waveCtx.clearRect(
    0,
    0,
    waveCanvas.width / (window.devicePixelRatio || 1),
    waveCanvas.height / (window.devicePixelRatio || 1)
  );
  waveCtx.lineWidth = 1;
  waveCtx.strokeStyle = waveStroke;
  waveCtx.beginPath();
  const sliceWidth = (waveCanvas.width / (window.devicePixelRatio || 1)) / bufferLength;
  let x = 0;
  for (let i = 0; i < bufferLength; i++) {
    const v = dataArray[i] / 128.0;
    const y = (v * (waveCanvas.height / (window.devicePixelRatio || 1))) / 2;
    if (i === 0) {
      waveCtx.moveTo(x, y);
    } else {
      waveCtx.lineTo(x, y);
    }
    x += sliceWidth;
  }
  waveCtx.lineTo(
    waveCanvas.width / (window.devicePixelRatio || 1),
    (waveCanvas.height / (window.devicePixelRatio || 1)) / 2
  );
  waveCtx.stroke();
  animationFrame = requestAnimationFrame(drawWaveform);
 }
 async function startRecording() {
  try {
    try {
      wakeLock = await navigator.wakeLock.request("screen");
    } catch (err) {
      console.log("Error acquiring wake lock.");
    }
    let stream;
    try {
      // Try tab capture first
      stream = await new Promise((resolve, reject) => {
        chrome.tabCapture.capture({audio: true}, (s) => {
          if (s) {
            resolve(s);
          } else {
            reject(new Error('Tab capture failed or not available'));
          }
        });
      });
      statusText.textContent = "Using tab audio capture.";
    } catch (tabError) {
      console.log('Tab capture not available, falling back to microphone', tabError);
      // Fallback to microphone
      const audioConstraints = selectedMicrophoneId
        ? { audio: { deviceId: { exact: selectedMicrophoneId } } }
        : { audio: true };
      stream = await navigator.mediaDevices.getUserMedia(audioConstraints);
      statusText.textContent = "Using microphone audio.";
    }
    audioContext = new (window.AudioContext || window.webkitAudioContext)();
    analyser = audioContext.createAnalyser();
    analyser.fftSize = 256;
    microphone = audioContext.createMediaStreamSource(stream);
    microphone.connect(analyser);
    recorder = new MediaRecorder(stream, { mimeType: "audio/webm" });
    recorder.ondataavailable = (e) => {
      if (websocket && websocket.readyState === WebSocket.OPEN) {
        websocket.send(e.data);
      }
    };
    recorder.start(chunkDuration);
    startTime = Date.now();
    timerInterval = setInterval(updateTimer, 1000);
    drawWaveform();
    isRecording = true;
    updateUI();
  } catch (err) {
    if (window.location.hostname === "0.0.0.0") {
      statusText.textContent =
        "Error accessing audio input. Browsers may block audio access on 0.0.0.0. Try using localhost:8000 instead.";
    } else {
      statusText.textContent = "Error accessing audio input. Please check permissions.";
    }
    console.error(err);
  }
 }
 async function stopRecording() {
  if (wakeLock) {
    try {
      await wakeLock.release();
    } catch (e) {
      // ignore
    }
    wakeLock = null;
  }
  userClosing = true;
  waitingForStop = true;
  if (websocket && websocket.readyState === WebSocket.OPEN) {
    const emptyBlob = new Blob([], { type: "audio/webm" });
    websocket.send(emptyBlob);
    statusText.textContent = "Recording stopped. Processing final audio...";
  }
  if (recorder) {
    recorder.stop();
    recorder = null;
  }
  if (microphone) {
    microphone.disconnect();
    microphone = null;
  }
  if (analyser) {
    analyser = null;
  }
  if (audioContext && audioContext.state !== "closed") {
    try {
      await audioContext.close();
    } catch (e) {
      console.warn("Could not close audio context:", e);
    }
    audioContext = null;
  }
  if (animationFrame) {
    cancelAnimationFrame(animationFrame);
    animationFrame = null;
  }
  if (timerInterval) {
    clearInterval(timerInterval);
    timerInterval = null;
  }
  timerElement.textContent = "00:00";
  startTime = null;
  isRecording = false;
  updateUI();
 }
 async function toggleRecording() {
  if (!isRecording) {
    if (waitingForStop) {
      console.log("Waiting for stop, early return");
      return;
    }
    console.log("Connecting to WebSocket");
    try {
      if (websocket && websocket.readyState === WebSocket.OPEN) {
        await startRecording();
      } else {
        await setupWebSocket();
        await startRecording();
      }
    } catch (err) {
      statusText.textContent = "Could not connect to WebSocket or access mic. Aborted.";
      console.error(err);
    }
  } else {
    console.log("Stopping recording");
    stopRecording();
  }
 }
 function updateUI() {
  recordButton.classList.toggle("recording", isRecording);
  recordButton.disabled = waitingForStop;
  if (waitingForStop) {
    if (statusText.textContent !== "Recording stopped. Processing final audio...") {
      statusText.textContent = "Please wait for processing to complete...";
    }
  } else if (isRecording) {
    statusText.textContent = "Recording...";
  } else {
    if (
      statusText.textContent !== "Finished processing audio! Ready to record again." &&
      statusText.textContent !== "Processing finalized or connection closed."
    ) {
      statusText.textContent = "Click to start transcription";
    }
  }
  if (!waitingForStop) {
    recordButton.disabled = false;
  }
 }
 recordButton.addEventListener("click", toggleRecording);
 if (microphoneSelect) {
  microphoneSelect.addEventListener("change", handleMicrophoneChange);
 }
 // Settings toggle functionality
 settingsToggle.addEventListener("click", () => {
  settingsDiv.classList.toggle("visible");
  settingsToggle.classList.toggle("active");
 });
 document.addEventListener('DOMContentLoaded', async () => {
  try {
    await enumerateMicrophones();
  } catch (error) {
    console.log("Could not enumerate microphones on load:", error);
  }
 });
 navigator.mediaDevices.addEventListener('devicechange', async () => {
  console.log('Device change detected, re-enumerating microphones');
  try {
    await enumerateMicrophones();
  } catch (error) {
    console.log("Error re-enumerating microphones:", error);
  }
 });
 async function run() {
  const micPermission = await navigator.permissions.query({
    name: "microphone",
  });
  document.getElementById(
    "audioPermission"
  ).innerText = `MICROPHONE: ${micPermission.state}`;
  if (micPermission.state !== "granted") {
    chrome.tabs.create({ url: "welcome.html" });
  }
  const intervalId = setInterval(async () => {
    const micPermission = await navigator.permissions.query({
      name: "microphone",
    });
    if (micPermission.state === "granted") {
      document.getElementById(
        "audioPermission"
      ).innerText = `MICROPHONE: ${micPermission.state}`;
      clearInterval(intervalId);
    }
  }, 100);
 }
 void run();
--- a/chrome-extension/manifest.json
+++ b/chrome-extension/manifest.json
@@ -3,9 +3,6 @@
    "name": "WhisperLiveKit Tab Capture",
    "version": "1.0",
    "description": "Capture and transcribe audio from browser tabs using WhisperLiveKit.",
    "background": {
        "service_worker": "background.js"
    },
    "icons": {
        "16": "icons/icon16.png",
        "32": "icons/icon32.png",
@@ -14,7 +11,7 @@
    },
    "action": {
        "default_title": "WhisperLiveKit Tab Capture",
-        "default_popup": "popup.html"
+        "default_popup": "live_transcription.html"
    },
    "permissions": [
        "scripting",
@@ -22,16 +19,5 @@
        "offscreen",
        "activeTab",
        "storage"
    ],
    "web_accessible_resources": [
        {
            "resources": [
                "requestPermissions.html",
                "requestPermissions.js"
            ],
            "matches": [
                "<all_urls>"
            ]
        }
    ]
 }
--- a/chrome-extension/popup.html
+++ b/chrome-extension/popup.html
@@ -1,78 +0,0 @@
 <!DOCTYPE html>
 <html lang="en">
 <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>WhisperLiveKit</title>
    <link rel="stylesheet" href="/web/live_transcription.css" />
 </head>
 <body>
    <div class="settings-container">
        <button id="recordButton">
            <div class="shape-container">
                <div class="shape"></div>
            </div>
            <div class="recording-info">
                <div class="wave-container">
                    <canvas id="waveCanvas"></canvas>
                </div>
                <div class="timer">00:00</div>
            </div>
        </button>
        <button id="settingsToggle" class="settings-toggle" title="Show/hide settings">
            <img src="/web/src/settings.svg" alt="Settings" />
        </button>
        <div class="settings">
            <div class="field">
                <label for="websocketInput">Websocket URL</label>
                <input id="websocketInput" type="text" placeholder="ws://host:port/asr" />
            </div>
            <div class="field">
                <label id="microphoneSelectLabel" for="microphoneSelect">Select Microphone</label>
                <select id="microphoneSelect">
                    <option value="">Default Microphone</option>
                </select>
                        <div id="audioPermission"></div>
            </div>
            <div class="theme-selector-container">
                <div class="segmented" role="radiogroup" aria-label="Theme selector">
                    <input type="radio" id="theme-system" name="theme" value="system" />
                    <label for="theme-system" title="System">
                        <img src="/web/src/system_mode.svg" alt="" />
                        <!-- <span>System</span> -->
                    </label>
                    <input type="radio" id="theme-light" name="theme" value="light" />
                    <label for="theme-light" title="Light">
                        <img src="/web/src/light_mode.svg" alt="" />
                        <!-- <span>Light</span> -->
                    </label>
                    <input type="radio" id="theme-dark" name="theme" value="dark" />
                    <label for="theme-dark" title="Dark">
                        <img src="/web/src/dark_mode.svg" alt="" />
                        <!-- <span>Dark</span> -->
                    </label>
                </div>
            </div>
        </div>
    </div>
    <p id="status"></p>
    <div id="linesTranscript"></div>
    <script src="live_transcription.js"></script>
 </body>
 </html>
--- a/chrome-extension/web/live_transcription.css
+++ b/chrome-extension/web/live_transcription.css
@@ -1,539 +0,0 @@
 :root {
  --bg: #ffffff;
  --text: #111111;
  --muted: #666666;
  --border: #e5e5e5;
  --chip-bg: rgba(0, 0, 0, 0.04);
  --chip-text: #000000;
  --spinner-border: #8d8d8d5c;
  --spinner-top: #b0b0b0;
  --silence-bg: #f3f3f3;
  --loading-bg: rgba(255, 77, 77, 0.06);
  --button-bg: #ffffff;
  --button-border: #e9e9e9;
  --wave-stroke: #000000;
  --label-dia-text: #868686;
  --label-trans-text: #111111;
 }
@media (prefers-color-scheme: dark) {
  :root:not([data-theme="light"]) {
    --bg: #0b0b0b;
    --text: #e6e6e6;
    --muted: #9aa0a6;
    --border: #333333;
    --chip-bg: rgba(255, 255, 255, 0.08);
    --chip-text: #e6e6e6;
    --spinner-border: #555555;
    --spinner-top: #dddddd;
    --silence-bg: #1a1a1a;
    --loading-bg: rgba(255, 77, 77, 0.12);
    --button-bg: #111111;
    --button-border: #333333;
    --wave-stroke: #e6e6e6;
    --label-dia-text: #b3b3b3;
    --label-trans-text: #ffffff;
  }
 }
 :root[data-theme="dark"] {
  --bg: #0b0b0b;
  --text: #e6e6e6;
  --muted: #9aa0a6;
  --border: #333333;
  --chip-bg: rgba(255, 255, 255, 0.08);
  --chip-text: #e6e6e6;
  --spinner-border: #555555;
  --spinner-top: #dddddd;
  --silence-bg: #1a1a1a;
  --loading-bg: rgba(255, 77, 77, 0.12);
  --button-bg: #111111;
  --button-border: #333333;
  --wave-stroke: #e6e6e6;
  --label-dia-text: #b3b3b3;
  --label-trans-text: #ffffff;
 }
 :root[data-theme="light"] {
  --bg: #ffffff;
  --text: #111111;
  --muted: #666666;
  --border: #e5e5e5;
  --chip-bg: rgba(0, 0, 0, 0.04);
  --chip-text: #000000;
  --spinner-border: #8d8d8d5c;
  --spinner-top: #b0b0b0;
  --silence-bg: #f3f3f3;
  --loading-bg: rgba(255, 77, 77, 0.06);
  --button-bg: #ffffff;
  --button-border: #e9e9e9;
  --wave-stroke: #000000;
  --label-dia-text: #868686;
  --label-trans-text: #111111;
 }
 body {
  font-family: ui-sans-serif, system-ui, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
  margin: 20px;
  text-align: center;
  background-color: var(--bg);
  color: var(--text);
 }
 .settings-toggle {
  margin-top: 4px;
  width: 40px;
  height: 40px;
  border: none;
  border-radius: 50%;
  background-color: var(--button-bg);
  cursor: pointer;
  transition: all 0.3s ease;
  /* border: 1px solid var(--button-border); */
  display: flex;
  align-items: center;
  justify-content: center;
  position: relative;
 }
 .settings-toggle:hover {
  background-color: var(--chip-bg);
 }
 .settings-toggle img {
  width: 24px;
  height: 24px;
  opacity: 0.7;
  transition: opacity 0.2s ease, transform 0.3s ease;
 }
 .settings-toggle:hover img {
  opacity: 1;
 }
 .settings-toggle.active img {
  transform: rotate(80deg);
 }
 /* Record button */
 #recordButton {
  width: 50px;
  height: 50px;
  border: none;
  border-radius: 50%;
  background-color: var(--button-bg);
  cursor: pointer;
  transition: all 0.3s ease;
  border: 1px solid var(--button-border);
  display: flex;
  align-items: center;
  justify-content: center;
  position: relative;
 }
 #recordButton.recording {
  width: 180px;
  border-radius: 40px;
  justify-content: flex-start;
  padding-left: 20px;
 }
 #recordButton:active {
  transform: scale(0.95);
 }
 .shape-container {
  width: 25px;
  height: 25px;
  display: flex;
  align-items: center;
  justify-content: center;
  flex-shrink: 0;
 }
 .shape {
  width: 25px;
  height: 25px;
  background-color: rgb(209, 61, 53);
  border-radius: 50%;
  transition: all 0.3s ease;
 }
 #recordButton:disabled .shape {
  background-color: #6e6d6d;
 }
 #recordButton.recording .shape {
  border-radius: 5px;
  width: 25px;
  height: 25px;
 }
 /* Recording elements */
 .recording-info {
  display: none;
  align-items: center;
  margin-left: 15px;
  flex-grow: 1;
 }
 #recordButton.recording .recording-info {
  display: flex;
 }
 .wave-container {
  width: 60px;
  height: 30px;
  position: relative;
  display: flex;
  align-items: center;
  justify-content: center;
 }
 #waveCanvas {
  width: 100%;
  height: 100%;
 }
 .timer {
  font-size: 14px;
  font-weight: 500;
  color: var(--text);
  margin-left: 10px;
 }
 #status {
  margin-top: 20px;
  font-size: 16px;
  color: var(--text);
 }
 /* Settings */
 .settings-container {
  display: flex;
  justify-content: center;
  align-items: flex-start;
  gap: 15px;
  margin-top: 20px;
  flex-wrap: wrap;
 }
 .settings {
  display: none;
  flex-wrap: wrap;
  align-items: flex-start;
  gap: 12px;
  transition: opacity 0.3s ease;
 }
 .settings.visible {
  display: flex;
 }
 .field {
  display: flex;
  flex-direction: column;
  align-items: flex-start;
  gap: 3px;
 }
 #chunkSelector,
 #websocketInput,
 #themeSelector,
 #microphoneSelect {
  font-size: 16px;
  padding: 5px 8px;
  border-radius: 8px;
  border: 1px solid var(--border);
  background-color: var(--button-bg);
  color: var(--text);
  max-height: 30px;
 }
 #microphoneSelect {
  width: 100%;
  max-width: 190px;
  min-width: 120px;
 }
 #chunkSelector:focus,
 #websocketInput:focus,
 #themeSelector:focus,
 #microphoneSelect:focus {
  outline: none;
  border-color: #007bff;
  box-shadow: 0 0 0 3px rgba(0, 123, 255, 0.15);
 }
 label {
  font-size: 13px;
  color: var(--muted);
 }
 .ws-default {
  font-size: 12px;
  color: var(--muted);
 }
 /* Segmented pill control for Theme */
 .segmented {
  display: inline-flex;
  align-items: stretch;
  border: 1px solid var(--button-border);
  background-color: var(--button-bg);
  border-radius: 999px;
  overflow: hidden;
 }
 .segmented input[type="radio"] {
  position: absolute;
  opacity: 0;
  pointer-events: none;
 }
 .theme-selector-container {
  display: flex;
  align-items: center;
  margin-top: 17px;
 }
 .segmented label {
  display: inline-flex;
  align-items: center;
  gap: 6px;
  padding: 6px 12px;
  font-size: 14px;
  color: var(--muted);
  cursor: pointer;
  user-select: none;
  transition: background-color 0.2s ease, color 0.2s ease;
 }
 .segmented label span {
  display: none;
 }
 .segmented label:hover span {
  display: inline;
 }
 .segmented label:hover {
  background-color: var(--chip-bg);
 }
 .segmented img {
  width: 16px;
  height: 16px;
 }
 .segmented input[type="radio"]:checked + label {
  background-color: var(--chip-bg);
  color: var(--text);
 }
 .segmented input[type="radio"]:focus-visible + label,
 .segmented input[type="radio"]:focus + label {
  outline: 2px solid #007bff;
  outline-offset: 2px;
  border-radius: 999px;
 }
 /* Transcript area */
 #linesTranscript {
  margin: 20px auto;
  max-width: 700px;
  text-align: left;
  font-size: 16px;
 }
 #linesTranscript p {
  margin: 0px 0;
 }
 #linesTranscript strong {
  color: var(--text);
 }
 #speaker {
  border: 1px solid var(--border);
  border-radius: 100px;
  padding: 2px 10px;
  font-size: 14px;
  margin-bottom: 0px;
 }
 .label_diarization {
  background-color: var(--chip-bg);
  border-radius: 8px 8px 8px 8px;
  padding: 2px 10px;
  margin-left: 10px;
  display: inline-block;
  white-space: nowrap;
  font-size: 14px;
  margin-bottom: 0px;
  color: var(--label-dia-text);
 }
 .label_transcription {
  background-color: var(--chip-bg);
  border-radius: 8px 8px 8px 8px;
  padding: 2px 10px;
  display: inline-block;
  white-space: nowrap;
  margin-left: 10px;
  font-size: 14px;
  margin-bottom: 0px;
  color: var(--label-trans-text);
 }
 #timeInfo {
  color: var(--muted);
  margin-left: 10px;
 }
 .textcontent {
  font-size: 16px;
  padding-left: 10px;
  margin-bottom: 10px;
  margin-top: 1px;
  padding-top: 5px;
  border-radius: 0px 0px 0px 10px;
 }
 .buffer_diarization {
  color: var(--label-dia-text);
  margin-left: 4px;
 }
 .buffer_transcription {
  color: #7474748c;
  margin-left: 4px;
 }
 .spinner {
  display: inline-block;
  width: 8px;
  height: 8px;
  border: 2px solid var(--spinner-border);
  border-top: 2px solid var(--spinner-top);
  border-radius: 50%;
  animation: spin 0.7s linear infinite;
  vertical-align: middle;
  margin-bottom: 2px;
  margin-right: 5px;
 }
@keyframes spin {
  to {
    transform: rotate(360deg);
  }
 }
 .silence {
  color: var(--muted);
  background-color: var(--silence-bg);
  font-size: 13px;
  border-radius: 30px;
  padding: 2px 10px;
 }
 .loading {
  color: var(--muted);
  background-color: var(--loading-bg);
  border-radius: 8px 8px 8px 0px;
  padding: 2px 10px;
  font-size: 14px;
  margin-bottom: 0px;
 }
 /* for smaller screens */
 /* @media (max-width: 450px) {
  .settings-container {
    flex-direction: column;
    gap: 10px;
    align-items: center;
  }
  .settings {
    justify-content: center;
    gap: 8px;
    width: 100%;
  }
  .field {
    align-items: center;
    width: 100%;
  }
  #websocketInput,
  #microphoneSelect {
    min-width: 200px;
    max-width: 100%;
  }
  .theme-selector-container {
    margin-top: 10px;
  }
 } */
 /* @media (max-width: 768px) and (min-width: 451px) {
  .settings-container {
    gap: 10px;
  }
  .settings {
    gap: 8px;
  }
  #websocketInput,
  #microphoneSelect {
    min-width: 150px;
    max-width: 300px;
  }
 } */
 /* @media (max-width: 480px) {
  body {
    margin: 10px;
  }
  .settings-toggle {
    width: 35px;
    height: 35px;
  }
  .settings-toggle img {
    width: 20px;
    height: 20px;
  }
  .settings {
    flex-direction: column;
    align-items: center;
    gap: 6px;
  }
  #websocketInput,
  #microphoneSelect {
    max-width: 400px;
  }
  .segmented label {
    padding: 4px 8px;
    font-size: 12px;
  }
  .segmented img {
    width: 14px;
    height: 14px;
  }
 } */
 html
 {
    width: 400px;  /* max: 800px */
    height: 600px; /* max: 600px */
    border-radius: 10px;
 }
--- a/chrome-extension/web/src/dark_mode.svg
+++ b/chrome-extension/web/src/dark_mode.svg
@@ -1 +0,0 @@
 <svg xmlns="http://www.w3.org/2000/svg" height="24px" viewBox="0 -960 960 960" width="24px" fill="#5f6368"><path d="M480-120q-151 0-255.5-104.5T120-480q0-138 90-239.5T440-838q13-2 23 3.5t16 14.5q6 9 6.5 21t-7.5 23q-17 26-25.5 55t-8.5 61q0 90 63 153t153 63q31 0 61.5-9t54.5-25q11-7 22.5-6.5T819-479q10 5 15.5 15t3.5 24q-14 138-117.5 229T480-120Zm0-80q88 0 158-48.5T740-375q-20 5-40 8t-40 3q-123 0-209.5-86.5T364-660q0-20 3-40t8-40q-78 32-126.5 102T200-480q0 116 82 198t198 82Zm-10-270Z"/></svg>
--- a/chrome-extension/web/src/light_mode.svg
+++ b/chrome-extension/web/src/light_mode.svg
@@ -1 +0,0 @@
 <svg xmlns="http://www.w3.org/2000/svg" height="24px" viewBox="0 -960 960 960" width="24px" fill="#5f6368"><path d="M480-360q50 0 85-35t35-85q0-50-35-85t-85-35q-50 0-85 35t-35 85q0 50 35 85t85 35Zm0 80q-83 0-141.5-58.5T280-480q0-83 58.5-141.5T480-680q83 0 141.5 58.5T680-480q0 83-58.5 141.5T480-280ZM80-440q-17 0-28.5-11.5T40-480q0-17 11.5-28.5T80-520h80q17 0 28.5 11.5T200-480q0 17-11.5 28.5T160-440H80Zm720 0q-17 0-28.5-11.5T760-480q0-17 11.5-28.5T800-520h80q17 0 28.5 11.5T920-480q0 17-11.5 28.5T880-440h-80ZM480-760q-17 0-28.5-11.5T440-800v-80q0-17 11.5-28.5T480-920q17 0 28.5 11.5T520-880v80q0 17-11.5 28.5T480-760Zm0 720q-17 0-28.5-11.5T440-80v-80q0-17 11.5-28.5T480-200q17 0 28.5 11.5T520-160v80q0 17-11.5 28.5T480-40ZM226-678l-43-42q-12-11-11.5-28t11.5-29q12-12 29-12t28 12l42 43q11 12 11 28t-11 28q-11 12-27.5 11.5T226-678Zm494 495-42-43q-11-12-11-28.5t11-27.5q11-12 27.5-11.5T734-282l43 42q12 11 11.5 28T777-183q-12 12-29 12t-28-12Zm-42-495q-12-11-11.5-27.5T678-734l42-43q11-12 28-11.5t29 11.5q12 12 12 29t-12 28l-43 42q-12 11-28 11t-28-11ZM183-183q-12-12-12-29t12-28l43-42q12-11 28.5-11t27.5 11q12 11 11.5 27.5T282-226l-42 43q-11 12-28 11.5T183-183Zm297-297Z"/></svg>
--- a/chrome-extension/web/src/settings.svg
+++ b/chrome-extension/web/src/settings.svg
@@ -1 +0,0 @@
 <svg xmlns="http://www.w3.org/2000/svg" height="24px" viewBox="0 -960 960 960" width="24px" fill="#5f6368"><path d="M433-80q-27 0-46.5-18T363-142l-9-66q-13-5-24.5-12T307-235l-62 26q-25 11-50 2t-39-32l-47-82q-14-23-8-49t27-43l53-40q-1-7-1-13.5v-27q0-6.5 1-13.5l-53-40q-21-17-27-43t8-49l47-82q14-23 39-32t50 2l62 26q11-8 23-15t24-12l9-66q4-26 23.5-44t46.5-18h94q27 0 46.5 18t23.5 44l9 66q13 5 24.5 12t22.5 15l62-26q25-11 50-2t39 32l47 82q14 23 8 49t-27 43l-53 40q1 7 1 13.5v27q0 6.5-2 13.5l53 40q21 17 27 43t-8 49l-48 82q-14 23-39 32t-50-2l-60-26q-11 8-23 15t-24 12l-9 66q-4 26-23.5 44T527-80h-94Zm7-80h79l14-106q31-8 57.5-23.5T639-327l99 41 39-68-86-65q5-14 7-29.5t2-31.5q0-16-2-31.5t-7-29.5l86-65-39-68-99 42q-22-23-48.5-38.5T533-694l-13-106h-79l-14 106q-31 8-57.5 23.5T321-633l-99-41-39 68 86 64q-5 15-7 30t-2 32q0 16 2 31t7 30l-86 65 39 68 99-42q22 23 48.5 38.5T427-266l13 106Zm42-180q58 0 99-41t41-99q0-58-41-99t-99-41q-59 0-99.5 41T342-480q0 58 40.5 99t99.5 41Zm-2-140Z"/></svg>
--- a/chrome-extension/web/src/system_mode.svg
+++ b/chrome-extension/web/src/system_mode.svg
@@ -1 +0,0 @@
 <svg xmlns="http://www.w3.org/2000/svg" height="24px" viewBox="0 -960 960 960" width="24px" fill="#5f6368"><path d="M396-396q-32-32-58.5-67T289-537q-5 14-6.5 28.5T281-480q0 83 58 141t141 58q14 0 28.5-2t28.5-6q-39-22-74-48.5T396-396Zm85 196q-56 0-107-21t-91-61q-40-40-61-91t-21-107q0-51 17-97.5t50-84.5q13-14 32-9.5t27 24.5q21 55 52.5 104t73.5 91q42 42 91 73.5T648-326q20 8 24.5 27t-9.5 32q-38 33-84.5 50T481-200Zm223-192q-16-5-23-20.5t-4-32.5q9-48-6-94.5T621-621q-35-35-80.5-49.5T448-677q-17 3-32-4t-21-23q-6-16 1.5-31t23.5-19q69-15 138 4.5T679-678q51 51 71 120t5 138q-4 17-19 25t-32 3ZM480-840q-17 0-28.5-11.5T440-880v-40q0-17 11.5-28.5T480-960q17 0 28.5 11.5T520-920v40q0 17-11.5 28.5T480-840Zm0 840q-17 0-28.5-11.5T440-40v-40q0-17 11.5-28.5T480-120q17 0 28.5 11.5T520-80v40q0 17-11.5 28.5T480 0Zm255-734q-12-12-12-28.5t12-28.5l28-28q11-11 27.5-11t28.5 11q12 12 12 28.5T819-762l-28 28q-12 12-28 12t-28-12ZM141-141q-12-12-12-28.5t12-28.5l28-28q12-12 28-12t28 12q12 12 12 28.5T225-169l-28 28q-11 11-27.5 11T141-141Zm739-299q-17 0-28.5-11.5T840-480q0-17 11.5-28.5T880-520h40q17 0 28.5 11.5T960-480q0 17-11.5 28.5T920-440h-40Zm-840 0q-17 0-28.5-11.5T0-480q0-17 11.5-28.5T40-520h40q17 0 28.5 11.5T120-480q0 17-11.5 28.5T80-440H40Zm779 299q-12 12-28.5 12T762-141l-28-28q-12-12-12-28t12-28q12-12 28.5-12t28.5 12l28 28q11 11 11 27.5T819-141ZM226-735q-12 12-28.5 12T169-735l-28-28q-11-11-11-27.5t11-28.5q12-12 28.5-12t28.5 12l28 28q12 12 12 28t-12 28Zm170 339Z"/></svg>
--- a/chrome-extension/welcome.html
+++ b/chrome-extension/welcome.html
@@ -1,12 +0,0 @@
 <!DOCTYPE html>
 <html>
  <head>
    <title>Welcome</title>
    <script src="welcome.js"></script>
  </head>
  <body>
    This page exists to workaround an issue with Chrome that blocks permission
    requests from chrome extensions
    <!-- <button id="requestMicrophone">Request Microphone</button> -->
  </body>
 </html>
--- a/docs/API.md
+++ b/docs/API.md
@@ -0,0 +1,264 @@
 # WhisperLiveKit WebSocket API Documentation
 > !! **Note**: The new API structure described in this document is currently under deployment. 
 This documentation is intended for devs who want to build custom frontends.
 WLK provides real-time speech transcription, speaker diarization, and translation through a WebSocket API. The server sends incremental updates as audio is processed, allowing clients to display live transcription results with minimal latency.
 ---
 ## Legacy API (Current)
 ### Message Structure
 The current API sends complete state snapshots on each update (several time per second)
 ```typescript
 {
  "type": str,
  "status": str,
  "lines": [
    {
      "speaker": int,
      "text": str,
      "start": float,
      "end": float,
      "translation": str | null,
      "detected_language": str
    }
  ],
  "buffer_transcription": str,
  "buffer_diarization": str,
  "remaining_time_transcription": float,
  "remaining_time_diarization": float
 }
 ```
 ---
 ## New API (Under Development)
 ### Philosophy
 Principles:
 - **Incremental Updates**: Only updates and new segments are sent
 - **Ephemeral Buffers**: Temporary, unvalidated data displayed in real-time but overwritten on next update, at speaker level
 ## Message Format
 ```typescript
 {
  "type": "transcript_update",
  "status": "active_transcription" | "no_audio_detected",
  "segments": [
    {
      "id": number,
      "speaker": number,
      "text": string,
      "start_speaker": float,
      "start": float,
      "end": float,
      "language": string | null,
      "translation": string,
      "words": [
        {
          "text": string,
          "start": float,
          "end": float,
          "validated": {
            "text": boolean,
            "speaker": boolean,
          }
        }
      ],
      "buffer": {
        "transcription": string,
        "diarization": string,
        "translation": string
      }
    }
  ],
  "metadata": {
    "remaining_time_transcription": float,
    "remaining_time_diarization": float
  }
 }
 ```
 ### Other Message Types
 #### Config Message (sent on connection)
 ```json
 {
  "type": "config",
  "useAudioWorklet": true / false
 }
 ```
 #### Ready to Stop Message (sent after processing complete)
 ```json
 {
  "type": "ready_to_stop"
 }
 ```
 ---
 ## Field Descriptions
 ### Segment Fields
 | Field | Type | Description |
 |-------|------|-------------|
 | `id` | `number` | Unique identifier for this segment. Used by clients to update specific segments efficiently. |
 | `speaker` | `number` | Speaker ID (1, 2, 3...). Special value `-2` indicates silence. |
 | `text` | `string` | Validated transcription text for this update. Should be **appended** to the segment's text on the client side.  |
 | `start_speaker` | `float` | Timestamp (seconds) when this speaker segment began. |
 | `start` | `float` | Timestamp (seconds) of the first word in this update. |
 | `end` | `float` | Timestamp (seconds) of the last word in this update. |
 | `language` | `string \| null` | ISO language code (e.g., "en", "fr"). `null` until language is detected. |
 | `translation` | `string` | Validated translation text for this update. Should be **appended** to the segment's translation on the client side. |
 | `words` | `Array` | Array of word-level objects with timing and validation information. |
 | `buffer` | `Object` | Per-segment temporary buffers, see below |
 ### Word Object
 | Field | Type | Description |
 |-------|------|-------------|
 | `text` | `string` | The word text. |
 | `start` | `number` | Start timestamp (seconds) of this word. |
 | `end` | `number` | End timestamp (seconds) of this word. |
 | `validated.text` | `boolean` | Whether the transcription text has been validated. if false, word is also in buffer: transcription |
 | `validated.speaker` | `boolean` | Whether the speaker assignment has been validated. if false, word is also in buffer: diarization |
 | `validated.language` | `boolean` | Whether the language detection has been validated. if false, word is also in buffer: translation |
 ### Buffer Object (Per-Segment)
 Buffers are **ephemeral**. They should be displayed to the user but not stored permanently in the frontend. Each update may contain a completely different buffer value, and previous buffer is likely to be in the next validated text.
 | Field | Type | Description |
 |-------|------|-------------|
 | `transcription` | `string` | Pending transcription text. Displayed immediately but **overwritten** on next update. |
 | `diarization` | `string` | Pending diarization text (text waiting for speaker assignment). Displayed immediately but **overwritten** on next update. |
 | `translation` | `string` | Pending translation text. Displayed immediately but **overwritten** on next update. |
 ### Metadata Fields
 | Field | Type | Description |
 |-------|------|-------------|
 | `remaining_time_transcription` | `float` | Seconds of audio waiting for transcription processing. |
 | `remaining_time_diarization` | `float` | Seconds of audio waiting for speaker diarization. |
 ### Status Values
 | Status | Description |
 |--------|-------------|
 | `active_transcription` | Normal operation, transcription is active. |
 | `no_audio_detected` | No audio has been detected yet. |
 ---
 ## Update Behavior
 ### Incremental Updates
 The API sends **only changed or new segments**. Clients should:
 1. Maintain a local map of segments by ID
 2. When receiving an update, merge/update segments by ID
 3. Render only the changed segments
 ### Language Detection
 When language is detected for a segment:
 ```jsonc
 // Update 1: No language yet
 {
  "segments": [
    {"id": 1, "speaker": 1, "text": "May see", "language": null}
  ]
 }
 // Update 2: Same segment ID, language now detected
 {
  "segments": [
    {"id": 1, "speaker": 1, "text": "Merci", "language": "fr"}
  ]
 }
 ```
 **Client behavior**: **Replace** the existing segment with the same ID.
 ### Buffer Behavior
 Buffers are **per-segment** to handle multi-speaker scenarios correctly.
 #### Example: Translation with diarization and translation
 ```jsonc
 // Update 1
 {
  "segments": [
    {
      "id": 1,
      "speaker": 1,
      "text": "Hello world, how are",
      "translation": "",
      "buffer": {
        "transcription": "",
        "diarization": " you on",
        "translation": "Bonjour le monde"
      }
    }
  ]
 }
 // ==== Frontend ====
 // <SPEAKER>1</SPEAKER>
 // <TRANSCRIPTION>Hello world, how are <DIARIZATION BUFFER> you on</DIARIZATION BUFFER></TRANSCRIPTION>
 // <TRANSLATION><TRANSLATION BUFFER>Bonjour le monde</TRANSLATION BUFFER></TRANSLATION>
 // Update 2
 {
  "segments": [
    {
      "id": 1,
      "speaker": 1,
      "text": " you on this",
      "translation": "Bonjour tout le monde",
      "buffer": {
        "transcription": "",
        "diarization": " beautiful day",
        "translation": ",comment"
      }
    },
  ]
 }
 // ==== Frontend ====
 // <SPEAKER>1</SPEAKER>
 // <TRANSCRIPTION>Hello world, how are you on this<DIARIZATION BUFFER>  beautiful day</DIARIZATION BUFFER></TRANSCRIPTION>
 // <TRANSLATION>Bonjour tout le monde<TRANSLATION BUFFER>, comment</TRANSLATION BUFFER><TRANSLATION>
 ```
 ### Silence Segments
 Silence is represented with the speaker id = `-2`:
 ```jsonc
 {
  "id": 5,
  "speaker": -2,
  "text": "",
  "start": 10.5,
  "end": 12.3
 }
 ```
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "whisperlivekit"
-version = "0.2.11"
+version = "0.2.12"
 description = "Real-time speech-to-text with speaker diarization using Whisper"
 readme = "README.md"
 authors = [
@@ -50,7 +50,7 @@ Homepage = "https://github.com/QuentinFuxa/WhisperLiveKit"
 whisperlivekit-server = "whisperlivekit.basic_server:main"
 [tool.setuptools]
-packages = ["whisperlivekit", "whisperlivekit.diarization", "whisperlivekit.simul_whisper", "whisperlivekit.simul_whisper.whisper", "whisperlivekit.simul_whisper.whisper.assets", "whisperlivekit.simul_whisper.whisper.normalizers", "whisperlivekit.web", "whisperlivekit.whisper_streaming_custom"]
+packages = ["whisperlivekit", "whisperlivekit.diarization", "whisperlivekit.simul_whisper", "whisperlivekit.simul_whisper.whisper", "whisperlivekit.simul_whisper.whisper.assets", "whisperlivekit.simul_whisper.whisper.normalizers", "whisperlivekit.web", "whisperlivekit.whisper_streaming_custom", "whisperlivekit.translation"]
 [tool.setuptools.package-data]
 whisperlivekit = ["web/*.html", "web/*.css", "web/*.js", "web/src/*.svg"]
--- a/sync_extension.py
+++ b/sync_extension.py
@@ -0,0 +1,38 @@
 import shutil
 import os
 from pathlib import Path
 def sync_extension_files():
    """Copy core files from web directory to Chrome extension directory."""
    web_dir = Path("whisperlivekit/web")
    extension_dir = Path("chrome-extension")
    files_to_sync = [
        "live_transcription.html", "live_transcription.js", "live_transcription.css"
    ]
    svg_files = [
        "system_mode.svg",
        "light_mode.svg", 
        "dark_mode.svg",
        "settings.svg"
    ]
    for file in files_to_sync:
        src_path = web_dir / file
        dest_path = extension_dir / file
        dest_path.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy2(src_path, dest_path)
    for svg_file in svg_files:
        src_path = web_dir / "src" / svg_file
        dest_path = extension_dir / "web" / "src" / svg_file
        dest_path.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy2(src_path, dest_path)
 if __name__ == "__main__":
    sync_extension_files()
--- a/whisperlivekit/audio_processor.py
+++ b/whisperlivekit/audio_processor.py
@@ -9,13 +9,25 @@ from whisperlivekit.core import TranscriptionEngine, online_factory, online_diar
 from whisperlivekit.silero_vad_iterator import FixedVADIterator
 from whisperlivekit.results_formater import format_output
 from whisperlivekit.ffmpeg_manager import FFmpegManager, FFmpegState
-# Set up logging once
+
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 SENTINEL = object() # unique sentinel object for end of stream marker
 def cut_at(cumulative_pcm, cut_sec):
    cumulative_len = 0
    cut_sample = int(cut_sec * 16000)
    for ind, pcm_array in enumerate(cumulative_pcm):
        if (cumulative_len + len(pcm_array)) >= cut_sample:
            cut_chunk = cut_sample - cumulative_len
            before = np.concatenate(cumulative_pcm[:ind] + [cumulative_pcm[ind][:cut_chunk]])
            after = [cumulative_pcm[ind][cut_chunk:]] + cumulative_pcm[ind+1:]
            return before, after
        cumulative_len += len(pcm_array)
    return np.concatenate(cumulative_pcm), []
 async def get_all_from_queue(queue):
    items = []
@@ -50,29 +62,32 @@ class AudioProcessor:
        self.bytes_per_sec = self.samples_per_sec * self.bytes_per_sample
        self.max_bytes_per_sec = 32000 * 5  # 5 seconds of audio at 32 kHz
        self.is_pcm_input = self.args.pcm_input
        self.debug = False
        # State management
        self.is_stopping = False
        self.silence = False
        self.silence_duration = 0.0
        self.tokens = []
        self.last_validated_token = 0
        self.translated_segments = []
        self.buffer_transcription = Transcript()
        self.end_buffer = 0
        self.end_attributed_speaker = 0
        self.lock = asyncio.Lock()
-        self.beg_loop = None #to deal with a potential little lag at the websocket initialization, this is now set in process_audio
+        self.beg_loop = 0.0 #to deal with a potential little lag at the websocket initialization, this is now set in process_audio
        self.sep = " "  # Default separator
        self.last_response_content = FrontData()
        self.last_detected_speaker = None
        self.speaker_languages = {}
        self.cumulative_pcm_len = 0
        self.diarization_before_transcription = False
        if self.diarization_before_transcription:
            self.cumulative_pcm = []
            self.last_start = 0.0
            self.last_end = 0.0
        # Models and processing
        self.asr = models.asr
        self.tokenizer = models.tokenizer
        self.vac_model = models.vac_model
        if self.args.vac:
            self.vac = FixedVADIterator(models.vac_model)
@@ -100,17 +115,21 @@ class AudioProcessor:
        self.transcription_task = None
        self.diarization_task = None
        self.translation_task = None
        self.watchdog_task = None
        self.all_tasks_for_cleanup = []
        self.online_translation = None
        self.transcription = None
        self.translation = None
        self.diarization = None
        if self.args.transcription:
-            self.online = online_factory(self.args, models.asr, models.tokenizer)        
+            self.transcription = online_factory(self.args, models.asr)        
-            self.sep = self.online.asr.sep   
+            self.sep = self.transcription.asr.sep   
        if self.args.diarization:
            self.diarization = online_diarization_factory(self.args, models.diarization_model)
        if models.translation_model:
-            self.online_translation = online_translation_factory(self.args, models.translation_model)
+            self.translation = online_translation_factory(self.args, models.translation_model)
    def convert_pcm_to_float(self, pcm_buffer):
        """Convert PCM buffer in s16le format to normalized NumPy array."""
@@ -119,7 +138,7 @@ class AudioProcessor:
    async def add_dummy_token(self):
        """Placeholder token when no transcription is available."""
        async with self.lock:
-            current_time = time() - self.beg_loop if self.beg_loop else 0
+            current_time = time() - self.beg_loop
            self.tokens.append(ASRToken(
                start=current_time, end=current_time + 1,
                text=".", speaker=-1, is_dummy=True
@@ -142,6 +161,7 @@ class AudioProcessor:
            return State(
                tokens=self.tokens.copy(),
                last_validated_token=self.last_validated_token,
                translated_segments=self.translated_segments.copy(),
                buffer_transcription=self.buffer_transcription,
                end_buffer=self.end_buffer,
@@ -204,9 +224,9 @@ class AudioProcessor:
        logger.info("FFmpeg stdout processing finished. Signaling downstream processors if needed.")
        if not self.diarization_before_transcription and self.transcription_queue:
            await self.transcription_queue.put(SENTINEL)
-        if self.args.diarization and self.diarization_queue:
+        if self.diarization:
            await self.diarization_queue.put(SENTINEL)
-        if self.online_translation:
+        if self.translation:
            await self.translation_queue.put(SENTINEL)
    async def transcription_processor(self):
@@ -221,7 +241,7 @@ class AudioProcessor:
                    self.transcription_queue.task_done()
                    break
-                asr_internal_buffer_duration_s = len(getattr(self.online, 'audio_buffer', [])) / self.online.SAMPLING_RATE
+                asr_internal_buffer_duration_s = len(getattr(self.transcription, 'audio_buffer', [])) / self.transcription.SAMPLING_RATE
                transcription_lag_s = max(0.0, time() - self.beg_loop - self.end_buffer)
                asr_processing_logs = f"internal_buffer={asr_internal_buffer_duration_s:.2f}s | lag={transcription_lag_s:.2f}s |"
                if type(item) is Silence:
@@ -230,10 +250,10 @@ class AudioProcessor:
                        asr_processing_logs += f" | last_end = {self.tokens[-1].end} |"
                    logger.info(asr_processing_logs)
                    cumulative_pcm_duration_stream_time += item.duration
-                    self.online.insert_silence(item.duration, self.tokens[-1].end if self.tokens else 0)
+                    self.transcription.insert_silence(item.duration, self.tokens[-1].end if self.tokens else 0)
                    continue
                elif isinstance(item, ChangeSpeaker):
-                    self.online.new_speaker(item)
+                    self.transcription.new_speaker(item)
                elif isinstance(item, np.ndarray):
                    pcm_array = item
@@ -243,10 +263,10 @@ class AudioProcessor:
                cumulative_pcm_duration_stream_time += duration_this_chunk
                stream_time_end_of_current_pcm = cumulative_pcm_duration_stream_time
-                self.online.insert_audio_chunk(pcm_array, stream_time_end_of_current_pcm)
+                self.transcription.insert_audio_chunk(pcm_array, stream_time_end_of_current_pcm)
-                new_tokens, current_audio_processed_upto = await asyncio.to_thread(self.online.process_iter)
+                new_tokens, current_audio_processed_upto = await asyncio.to_thread(self.transcription.process_iter)
-                _buffer_transcript = self.online.get_buffer()
+                _buffer_transcript = self.transcription.get_buffer()
                buffer_text = _buffer_transcript.text
                if new_tokens:
@@ -293,7 +313,9 @@ class AudioProcessor:
    async def diarization_processor(self, diarization_obj):
        """Process audio chunks for speaker diarization."""
-        self.current_speaker = 0
+        if self.diarization_before_transcription:
            self.current_speaker = 0
            await self.transcription_queue.put(ChangeSpeaker(speaker=self.current_speaker, start=0.0))
        while True:
            try:
                item = await self.diarization_queue.get()
@@ -309,26 +331,39 @@ class AudioProcessor:
                else:
                    raise Exception('item should be pcm_array') 
                # Process diarization
                await diarization_obj.diarize(pcm_array)
                segments = diarization_obj.get_segments()
                if self.diarization_before_transcription:
-                    if segments and segments[-1].speaker != self.current_speaker:
+                    segments = diarization_obj.get_segments()
-                        self.current_speaker = segments[-1].speaker
+                    self.cumulative_pcm.append(pcm_array)
-                        cut_at = int(segments[-1].start*16000 - (self.cumulative_pcm_len))
+                    if segments:
-                        await self.transcription_queue.put(pcm_array[cut_at:])
+                        last_segment = segments[-1]                    
-                        await self.transcription_queue.put(ChangeSpeaker(speaker=self.current_speaker, start=cut_at))                        
+                        if last_segment.speaker != self.current_speaker:
-                        await self.transcription_queue.put(pcm_array[:cut_at])
+                            cut_sec = last_segment.start - self.last_end
-                    else:
+                            to_transcript, self.cumulative_pcm = cut_at(self.cumulative_pcm, cut_sec)
-                        await self.transcription_queue.put(pcm_array)
+                            await self.transcription_queue.put(to_transcript)
-                else:           
+                            
                            self.current_speaker = last_segment.speaker
                            await self.transcription_queue.put(ChangeSpeaker(speaker=self.current_speaker, start=last_segment.start))
                            cut_sec = last_segment.end - last_segment.start
                            to_transcript, self.cumulative_pcm = cut_at(self.cumulative_pcm, cut_sec)
                            await self.transcription_queue.put(to_transcript)                            
                            self.last_start = last_segment.start
                            self.last_end = last_segment.end
                        else:
                            cut_sec = last_segment.end - self.last_end
                            to_transcript, self.cumulative_pcm = cut_at(self.cumulative_pcm, cut_sec)
                            await self.transcription_queue.put(to_transcript)
                            self.last_end = last_segment.end
                elif not self.diarization_before_transcription:           
                    async with self.lock:
                        self.tokens = diarization_obj.assign_speakers_to_tokens(
                            self.tokens,
                            use_punctuation_split=self.args.punctuation_split
                        )
                self.cumulative_pcm_len += len(pcm_array)            
                if len(self.tokens) > 0:
                    self.end_attributed_speaker = max(self.tokens[-1].end, self.end_attributed_speaker)
                self.diarization_queue.task_done()
@@ -352,7 +387,7 @@ class AudioProcessor:
                    self.translation_queue.task_done()
                    break
                elif type(item) is Silence:
-                    self.online_translation.insert_silence(item.duration)
+                    self.translation.insert_silence(item.duration)
                    continue
                # get all the available tokens for translation. The more words, the more precise
@@ -364,10 +399,14 @@ class AudioProcessor:
                    if additional_token is SENTINEL:
                        sentinel_found = True
                        break
-                    tokens_to_process.append(additional_token)                
+                    elif type(additional_token) is Silence:
                        self.translation.insert_silence(additional_token.duration)
                        continue
                    else:
                        tokens_to_process.append(additional_token)                
                if tokens_to_process:
-                    self.online_translation.insert_tokens(tokens_to_process)
+                    self.translation.insert_tokens(tokens_to_process)
-                    self.translated_segments = await asyncio.to_thread(self.online_translation.process)
+                    self.translated_segments = await asyncio.to_thread(self.translation.process)
                self.translation_queue.task_done()
                for _ in additional_tokens:
                    self.translation_queue.task_done()
@@ -390,35 +429,23 @@ class AudioProcessor:
        """Format processing results for output."""
        while True:
            try:
                # If FFmpeg error occurred, notify front-end
                if self._ffmpeg_error:
-                    yield FrontData(
+                    yield FrontData(status="error", error=f"FFmpeg error: {self._ffmpeg_error}")
                        status="error",
                        error=f"FFmpeg error: {self._ffmpeg_error}"
                    )
                    self._ffmpeg_error = None
                    await asyncio.sleep(1)
                    continue
                # Get current state
                state = await self.get_current_state()
                # Add dummy tokens if needed
                if (not state.tokens or state.tokens[-1].is_dummy) and not self.args.transcription and self.args.diarization:
                    await self.add_dummy_token()
                    sleep(0.5)
                    state = await self.get_current_state()
-                # Format output
+                
-                lines, undiarized_text, end_w_silence = format_output(
+                lines, undiarized_text = format_output(
                    state,
                    self.silence,
-                    current_time = time() - self.beg_loop if self.beg_loop else None,
+                    current_time = time() - self.beg_loop,
                    args = self.args,
                    debug = self.debug,
                    sep=self.sep
                )
-                if end_w_silence:
+                if lines and lines[-1].speaker == -2:
                    buffer_transcription = Transcript()
                else:
                    buffer_transcription = state.buffer_transcription
@@ -445,7 +472,7 @@ class AudioProcessor:
                    status=response_status,
                    lines=lines,
                    buffer_transcription=buffer_transcription.text.strip(),
-                    buffer_diarization=buffer_diarization.strip(),
+                    buffer_diarization=buffer_diarization,
                    remaining_time_transcription=state.remaining_time_transcription,
                    remaining_time_diarization=state.remaining_time_diarization if self.args.diarization else 0
                )
@@ -494,17 +521,17 @@ class AudioProcessor:
            self.all_tasks_for_cleanup.append(self.ffmpeg_reader_task)
            processing_tasks_for_watchdog.append(self.ffmpeg_reader_task)
-        if self.args.transcription and self.online:
+        if self.transcription:
            self.transcription_task = asyncio.create_task(self.transcription_processor())
            self.all_tasks_for_cleanup.append(self.transcription_task)
            processing_tasks_for_watchdog.append(self.transcription_task)
-        if self.args.diarization and self.diarization:
+        if self.diarization:
            self.diarization_task = asyncio.create_task(self.diarization_processor(self.diarization))
            self.all_tasks_for_cleanup.append(self.diarization_task)
            processing_tasks_for_watchdog.append(self.diarization_task)
-        if self.online_translation:
+        if self.translation:
            self.translation_task = asyncio.create_task(self.translation_processor())
            self.all_tasks_for_cleanup.append(self.translation_task)
            processing_tasks_for_watchdog.append(self.translation_task)
@@ -555,7 +582,7 @@ class AudioProcessor:
                logger.info("FFmpeg manager stopped.")
            except Exception as e:
                logger.warning(f"Error stopping FFmpeg manager: {e}")
-        if self.args.diarization and hasattr(self, 'dianization') and hasattr(self.diarization, 'close'):
+        if self.diarization:
            self.diarization.close()
        logger.info("AudioProcessor cleanup complete.")
@@ -608,9 +635,13 @@ class AudioProcessor:
                f"Consider using a smaller model."
            )
-        # Process audio chunk
+        chunk_size = min(len(self.pcm_buffer), self.max_bytes_per_sec)
-        pcm_array = self.convert_pcm_to_float(self.pcm_buffer[:self.max_bytes_per_sec])
+        aligned_chunk_size = (chunk_size // self.bytes_per_sample) * self.bytes_per_sample
-        self.pcm_buffer = self.pcm_buffer[self.max_bytes_per_sec:]
+        
        if aligned_chunk_size == 0:
            return
        pcm_array = self.convert_pcm_to_float(self.pcm_buffer[:aligned_chunk_size])
        self.pcm_buffer = self.pcm_buffer[aligned_chunk_size:]
        res = None
        end_of_audio = False
--- a/whisperlivekit/basic_server.py
+++ b/whisperlivekit/basic_server.py
@@ -118,6 +118,8 @@ def main():
    if ssl_kwargs:
        uvicorn_kwargs = {**uvicorn_kwargs, **ssl_kwargs}
    if args.forwarded_allow_ips:
        uvicorn_kwargs = { **uvicorn_kwargs, "forwarded_allow_ips" : args.forwarded_allow_ips }
    uvicorn.run(**uvicorn_kwargs)
--- a/whisperlivekit/core.py
+++ b/whisperlivekit/core.py
@@ -4,10 +4,15 @@ try:
 except ImportError:
    from .whisper_streaming_custom.whisper_online import backend_factory
    from .whisper_streaming_custom.online_asr import OnlineASRProcessor
 from whisperlivekit.warmup import warmup_asr
 from argparse import Namespace
 import sys
 def update_with_kwargs(_dict, kwargs):
    _dict.update({
        k: v for k, v in kwargs.items() if k in _dict
    })
    return _dict
 class TranscriptionEngine:
    _instance = None
    _initialized = False
@@ -21,76 +26,48 @@ class TranscriptionEngine:
        if TranscriptionEngine._initialized:
            return
-        defaults = {
+        global_params = {
            "host": "localhost",
            "port": 8000,
            "warmup_file": None,
            "diarization": False,
            "punctuation_split": False,
            "min_chunk_size": 0.5,
            "model": "tiny",
            "model_cache_dir": None,
            "model_dir": None,
            "lan": "auto",
            "task": "transcribe",
            "target_language": "",
            "backend": "faster-whisper",
            "vac": True,
            "vac_chunk_size": 0.04,
            "log_level": "DEBUG",
            "ssl_certfile": None,
            "ssl_keyfile": None,
            "forwarded_allow_ips": None,
            "transcription": True,
            "vad": True,
            "pcm_input": False,
            # whisperstreaming params:
            "buffer_trimming": "segment",
            "confidence_validation": False,
            "buffer_trimming_sec": 15,
            # simulstreaming params:
            "disable_fast_encoder": False,
            "frame_threshold": 25,
            "beams": 1,
            "decoder_type": None,
            "audio_max_len": 20.0,
            "audio_min_len": 0.0,
            "cif_ckpt_path": None,
            "never_fire": False,
            "init_prompt": None,
            "static_init_prompt": None,
            "max_context_tokens": None,
            "model_path": './base.pt',
            "diarization_backend": "sortformer",
            # diarization params:
            "disable_punctuation_split" : False,
-            "segmentation_model": "pyannote/segmentation-3.0",
+            "diarization_backend": "sortformer",
            "embedding_model": "pyannote/embedding",  
            # translation params:
            "nllb_backend": "ctranslate2",
            "nllb_size": "600M"
        }
        global_params = update_with_kwargs(global_params, kwargs)
-        config_dict = {**defaults, **kwargs}
+        transcription_common_params = {
            "backend": "simulstreaming",
            "warmup_file": None,
            "min_chunk_size": 0.5,
            "model_size": "tiny",
            "model_cache_dir": None,
            "model_dir": None,
            "lan": "auto",
            "task": "transcribe",
        }
        transcription_common_params = update_with_kwargs(transcription_common_params, kwargs)                                            
        if transcription_common_params['model_size'].endswith(".en"):
            transcription_common_params["lan"] = "en"
        if 'no_transcription' in kwargs:
-            config_dict['transcription'] = not kwargs['no_transcription']
+            global_params['transcription'] = not global_params['no_transcription']
        if 'no_vad' in kwargs:
-            config_dict['vad'] = not kwargs['no_vad']
+            global_params['vad'] = not kwargs['no_vad']
        if 'no_vac' in kwargs:
-            config_dict['vac'] = not kwargs['no_vac']
+            global_params['vac'] = not kwargs['no_vac']
        config_dict.pop('no_transcription', None)
        config_dict.pop('no_vad', None)
-        if 'language' in kwargs:
+        self.args = Namespace(**{**global_params, **transcription_common_params})
            config_dict['lan'] = kwargs['language']
        config_dict.pop('language', None) 
        self.args = Namespace(**config_dict)
        self.asr = None
        self.tokenizer = None
@@ -104,44 +81,57 @@ class TranscriptionEngine:
        if self.args.transcription:
            if self.args.backend == "simulstreaming": 
                from whisperlivekit.simul_whisper import SimulStreamingASR
                self.tokenizer = None
                simulstreaming_kwargs = {}
                for attr in ['frame_threshold', 'beams', 'decoder_type', 'audio_max_len', 'audio_min_len', 
                            'cif_ckpt_path', 'never_fire', 'init_prompt', 'static_init_prompt', 
                            'max_context_tokens', 'model_path', 'warmup_file', 'preload_model_count', 'disable_fast_encoder']:
                    if hasattr(self.args, attr):
                        simulstreaming_kwargs[attr] = getattr(self.args, attr)
                # Add segment_length from min_chunk_size
                simulstreaming_kwargs['segment_length'] = getattr(self.args, 'min_chunk_size', 0.5)
                simulstreaming_kwargs['task'] = self.args.task
-                size = self.args.model
+                simulstreaming_params = {
                    "disable_fast_encoder": False,
                    "custom_alignment_heads": None,
                    "frame_threshold": 25,
                    "beams": 1,
                    "decoder_type": None,
                    "audio_max_len": 20.0,
                    "audio_min_len": 0.0,
                    "cif_ckpt_path": None,
                    "never_fire": False,
                    "init_prompt": None,
                    "static_init_prompt": None,
                    "max_context_tokens": None,
                    "model_path": './base.pt',
                    "preload_model_count": 1,
                }
                simulstreaming_params = update_with_kwargs(simulstreaming_params, kwargs)
                self.tokenizer = None        
                self.asr = SimulStreamingASR(
-                    modelsize=size,
+                    **transcription_common_params, **simulstreaming_params
                    lan=self.args.lan,
                    cache_dir=getattr(self.args, 'model_cache_dir', None),
                    model_dir=getattr(self.args, 'model_dir', None),
                    **simulstreaming_kwargs
                )
            else:
-                self.asr, self.tokenizer = backend_factory(self.args)
+                
-                warmup_asr(self.asr, self.args.warmup_file) #for simulstreaming, warmup should be done in the online class not here
+                whisperstreaming_params = {
                    "buffer_trimming": "segment",
                    "confidence_validation": False,
                    "buffer_trimming_sec": 15,
                }
                whisperstreaming_params = update_with_kwargs(whisperstreaming_params, kwargs)
                self.asr = backend_factory(
                    **transcription_common_params, **whisperstreaming_params
                )
        if self.args.diarization:
            if self.args.diarization_backend == "diart":
                from whisperlivekit.diarization.diart_backend import DiartDiarization
                diart_params = {
                    "segmentation_model": "pyannote/segmentation-3.0",
                    "embedding_model": "pyannote/embedding",
                }
                diart_params = update_with_kwargs(diart_params, kwargs)
                self.diarization_model = DiartDiarization(
                    block_duration=self.args.min_chunk_size,
-                    segmentation_model_name=self.args.segmentation_model,
+                    **diart_params
                    embedding_model_name=self.args.embedding_model
                )
            elif self.args.diarization_backend == "sortformer":
                from whisperlivekit.diarization.sortformer_backend import SortformerDiarization
                self.diarization_model = SortformerDiarization()
            else:
                raise ValueError(f"Unknown diarization backend: {self.args.diarization_backend}")
        self.translation_model = None
        if self.args.target_language:
@@ -149,26 +139,21 @@ class TranscriptionEngine:
                raise Exception('Translation cannot be set with language auto when transcription backend is not simulstreaming')
            else:
                from whisperlivekit.translation.translation import load_model
-                self.translation_model = load_model([self.args.lan], backend=self.args.nllb_backend, model_size=self.args.nllb_size) #in the future we want to handle different languages for different speakers
+                translation_params = { 
                    "nllb_backend": "ctranslate2",
                    "nllb_size": "600M"
                }
                translation_params = update_with_kwargs(translation_params, kwargs)
                self.translation_model = load_model([self.args.lan], **translation_params) #in the future we want to handle different languages for different speakers
        TranscriptionEngine._initialized = True
-
+def online_factory(args, asr):
 def online_factory(args, asr, tokenizer, logfile=sys.stderr):
    if args.backend == "simulstreaming":    
        from whisperlivekit.simul_whisper import SimulStreamingOnlineProcessor
-        online = SimulStreamingOnlineProcessor(
+        online = SimulStreamingOnlineProcessor(asr)
            asr,
            logfile=logfile,
        )
    else:
-        online = OnlineASRProcessor(
+        online = OnlineASRProcessor(asr)
            asr,
            tokenizer,
            logfile=logfile,
            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
            confidence_validation = args.confidence_validation
        )
    return online
--- a/whisperlivekit/parse_args.py
+++ b/whisperlivekit/parse_args.py
@@ -89,6 +89,7 @@ def parse_args():
        "--model",
        type=str,
        default="small",
        dest='model_size',
        help="Name size of the Whisper model to use (default: tiny). Suggested values: tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large-v3,large,large-v3-turbo. The model is automatically downloaded from the model hub if not present in model cache dir.",
    )
@@ -109,6 +110,7 @@ def parse_args():
        "--language",
        type=str,
        default="auto",
        dest='lan',
        help="Source language code, e.g. en,de,cs, or 'auto' for language detection.",
    )
    parser.add_argument(
@@ -173,6 +175,7 @@ def parse_args():
    )
    parser.add_argument("--ssl-certfile", type=str, help="Path to the SSL certificate file.", default=None)
    parser.add_argument("--ssl-keyfile", type=str, help="Path to the SSL private key file.", default=None)
    parser.add_argument("--forwarded-allow-ips", type=str, help="Allowed ips for reverse proxying.", default=None)
    parser.add_argument(
        "--pcm-input",
        action="store_true",
@@ -189,6 +192,13 @@ def parse_args():
        dest="disable_fast_encoder",
        help="Disable Faster Whisper or MLX Whisper backends for encoding (if installed). Slower but helpful when GPU memory is limited",
    )
    simulstreaming_group.add_argument(
        "--custom-alignment-heads",
        type=str,
        default=None,
        help="Use your own alignment heads, useful when `--model-dir` is used",
    )
    simulstreaming_group.add_argument(
        "--frame-threshold",
--- a/whisperlivekit/remove_silences.py
+++ b/whisperlivekit/remove_silences.py
@@ -78,16 +78,8 @@ def no_token_to_silence(tokens):
    return new_tokens
 def ends_with_silence(tokens, current_time, vac_detected_silence):
    end_w_silence = False
    if not tokens:
        return [], end_w_silence
    last_token = tokens[-1]
-    if tokens and current_time and (
+    if  vac_detected_silence or (current_time - last_token.end >= END_SILENCE_DURATION):
        current_time - last_token.end >= END_SILENCE_DURATION 
        or
        (current_time - last_token.end >= 3 and vac_detected_silence)
        ):
        end_w_silence = True
        if last_token.speaker == -2:
            last_token.end = current_time
        else:
@@ -99,12 +91,14 @@ def ends_with_silence(tokens, current_time, vac_detected_silence):
                    probability=0.95
                )
            )
-    return tokens, end_w_silence
+    return tokens
 def handle_silences(tokens, current_time, vac_detected_silence):
    if not tokens:
        return []
    tokens = blank_to_silence(tokens) #useful for simulstreaming backend which tends to generate [BLANK_AUDIO] text
    tokens = no_token_to_silence(tokens)
-    tokens, end_w_silence = ends_with_silence(tokens, current_time, vac_detected_silence)
+    tokens = ends_with_silence(tokens, current_time, vac_detected_silence)
-    return tokens, end_w_silence
+    return tokens
--- a/whisperlivekit/results_formater.py
+++ b/whisperlivekit/results_formater.py
@@ -7,6 +7,8 @@ logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 CHECK_AROUND = 4
 DEBUG = False
 def is_punctuation(token):
    if token.is_punctuation():
@@ -30,99 +32,96 @@ def next_speaker_change(i, tokens, speaker):
 def new_line(
    token,
    speaker,
    debug_info = ""
 ):
    return Line(
-        speaker = speaker,
+        speaker = token.corrected_speaker,
-        text = token.text + debug_info,
+        text = token.text + (f"[{format_time(token.start)} : {format_time(token.end)}]" if DEBUG else ""),
        start = token.start,
        end = token.end,
        detected_language=token.detected_language
    )
-def append_token_to_last_line(lines, sep, token, debug_info):
+def append_token_to_last_line(lines, sep, token):
-    if token.text:
+    if not lines:
-        lines[-1].text += sep + token.text + debug_info
+        lines.append(new_line(token))
-        lines[-1].end = token.end
+    else:
-    if not lines[-1].detected_language and token.detected_language:
+        if token.text:
-        lines[-1].detected_language = token.detected_language
+            lines[-1].text += sep + token.text + (f"[{format_time(token.start)} : {format_time(token.end)}]" if DEBUG else "")
-         
+            lines[-1].end = token.end
        if not lines[-1].detected_language and token.detected_language:
            lines[-1].detected_language = token.detected_language
-def format_output(state, silence, current_time, args, debug, sep):
+def format_output(state, silence, current_time, args, sep):
    diarization = args.diarization
    disable_punctuation_split = args.disable_punctuation_split
    tokens = state.tokens
    translated_segments = state.translated_segments # Here we will attribute the speakers only based on the timestamps of the segments
-    end_attributed_speaker = state.end_attributed_speaker
+    last_validated_token = state.last_validated_token
-    previous_speaker = -1
+    previous_speaker = 1
    lines = []
    undiarized_text = []
-    tokens, end_w_silence = handle_silences(tokens, current_time, silence)
+    tokens = handle_silences(tokens, current_time, silence)
    last_punctuation = None
-    for i, token in enumerate(tokens):
+    for i, token in enumerate(tokens[last_validated_token:]):
-        speaker = token.speaker
+        speaker = int(token.speaker)
-        if not diarization and speaker == -1: #Speaker -1 means no attributed by diarization. In the frontend, it should appear under 'Speaker 1'
+        token.corrected_speaker = speaker
-            speaker = 1
+        if not diarization:
-        if diarization and not tokens[-1].speaker == -2:
+            if speaker == -1: #Speaker -1 means no attributed by diarization. In the frontend, it should appear under 'Speaker 1'
-            if (speaker in [-1, 0]) and token.end >= end_attributed_speaker:
+                token.corrected_speaker = 1
-                undiarized_text.append(token.text)
+                token.validated_speaker = True
                continue
            elif (speaker in [-1, 0]) and token.end < end_attributed_speaker:
                speaker = previous_speaker
        debug_info = ""
        if debug:
            debug_info = f"[{format_time(token.start)} : {format_time(token.end)}]"
        if not lines:
            lines.append(new_line(token, speaker, debug_info = ""))
            continue
        else:
-            previous_speaker = lines[-1].speaker
+            # if token.end > end_attributed_speaker and token.speaker != -2:
-        
+            #     if tokens[-1].speaker == -2:  #if it finishes by a silence, we want to append the undiarized text to the last speaker.
-        if is_punctuation(token):
+            #         token.corrected_speaker = previous_speaker
-            last_punctuation = i
+            #     else:
-            
+            #         undiarized_text.append(token.text)
-        
+            #         continue
-        if last_punctuation == i-1:
+            # else:
-            if speaker != previous_speaker:
+                if is_punctuation(token):
-                # perfect, diarization perfectly aligned
+                    last_punctuation = i  
-                lines.append(new_line(token, speaker, debug_info = ""))
+                
-                last_punctuation, next_punctuation = None, None
+                if last_punctuation == i-1:
-                continue
+                    if token.speaker != previous_speaker:
-            
+                        token.validated_speaker = True
-            speaker_change_pos, new_speaker = next_speaker_change(i, tokens, speaker)
+                        # perfect, diarization perfectly aligned
-            if speaker_change_pos:
+                        last_punctuation = None
-                # Corrects delay:
+                    else:
-                # That was the idea. Okay haha |SPLIT SPEAKER| that's a good one 
+                        speaker_change_pos, new_speaker = next_speaker_change(i, tokens, speaker)
-                # should become:
+                        if speaker_change_pos:
-                # That was the idea. |SPLIT SPEAKER| Okay haha that's a good one 
+                            # Corrects delay:
-                lines.append(new_line(token, new_speaker, debug_info = ""))
+                            # That was the idea. <Okay> haha |SPLIT SPEAKER| that's a good one 
-            else:
+                            # should become:
-                # No speaker change to come
+                            # That was the idea. |SPLIT SPEAKER| <Okay> haha that's a good one 
-                append_token_to_last_line(lines, sep, token, debug_info)
+                            token.corrected_speaker = new_speaker
-            continue
+                            token.validated_speaker = True
-        
+                elif speaker != previous_speaker:
                    if not (speaker == -2 or previous_speaker == -2):
                        if next_punctuation_change(i, tokens):
                            # Corrects advance:
                            # Are you |SPLIT SPEAKER| <okay>? yeah, sure. Absolutely 
                            # should become:
                            # Are you <okay>? |SPLIT SPEAKER| yeah, sure. Absolutely 
                            token.corrected_speaker = previous_speaker
                            token.validated_speaker = True
                        else: #Problematic, except if the language has no punctuation. We append to previous line, except if disable_punctuation_split is set to True.
                            if not disable_punctuation_split:
                                token.corrected_speaker = previous_speaker
                                token.validated_speaker = False
        if token.validated_speaker:
            state.last_validated_token = i
        previous_speaker = token.corrected_speaker  
-        if speaker != previous_speaker:
+    previous_speaker = 1
-            if speaker == -2 or previous_speaker == -2: #silences can happen anytime
+    
-                lines.append(new_line(token, speaker, debug_info = ""))
+    lines = []
-                continue
+    for token in tokens:
-            elif next_punctuation_change(i, tokens):
+        if int(token.corrected_speaker) != int(previous_speaker):
-                # Corrects advance:
+            lines.append(new_line(token))
-                # Are you |SPLIT SPEAKER| okay? yeah, sure. Absolutely 
+        else:
-                # should become:
+            append_token_to_last_line(lines, sep, token)
-                # Are you okay? |SPLIT SPEAKER| yeah, sure. Absolutely 
+
-                append_token_to_last_line(lines, sep, token, debug_info)
+        previous_speaker = token.corrected_speaker        
                continue
            else: #we create a new speaker, but that's no ideal. We are not sure about the split. We prefer to append to previous line
                if disable_punctuation_split:
                    lines.append(new_line(token, speaker, debug_info = ""))
                    continue
                pass
        append_token_to_last_line(lines, sep, token, debug_info)
    if lines and translated_segments:
        unassigned_translated_segments = []
@@ -158,4 +157,4 @@ def format_output(state, silence, current_time, args, debug, sep):
    if state.buffer_transcription and lines:
        lines[-1].end = max(state.buffer_transcription.end, lines[-1].end)
-    return lines, undiarized_text, end_w_silence
+    return lines, undiarized_text
--- a/whisperlivekit/simul_whisper/backend.py
+++ b/whisperlivekit/simul_whisper/backend.py
@@ -22,11 +22,9 @@ try:
    HAS_MLX_WHISPER = True
 except ImportError:
    if platform.system() == "Darwin" and platform.machine() == "arm64":
-        print(f"""
+        print(f"""{"="*50}
-            {"="*50}
+MLX Whisper not found but you are on Apple Silicon. Consider installing mlx-whisper for better performance: pip install mlx-whisper
-            MLX Whisper not found but you are on Apple Silicon. Consider installing mlx-whisper for better performance: pip install mlx-whisper
+{"="*50}""")
            {"="*50}
            """)
    HAS_MLX_WHISPER = False
 if HAS_MLX_WHISPER:
    HAS_FASTER_WHISPER = False
@@ -47,7 +45,6 @@ class SimulStreamingOnlineProcessor:
        self,
        asr,
        logfile=sys.stderr,
        warmup_file=None
    ):        
        self.asr = asr
        self.logfile = logfile
@@ -111,7 +108,7 @@ class SimulStreamingOnlineProcessor:
        """
        try:
            timestamped_words = self.model.infer(is_last=is_last)
-            if timestamped_words and timestamped_words[0].detected_language == None:
+            if self.model.cfg.language == "auto" and timestamped_words and timestamped_words[0].detected_language == None:
                self.buffer.extend(timestamped_words)
                return [], self.end
@@ -146,31 +143,20 @@ class SimulStreamingASR():
    """SimulStreaming backend with AlignAtt policy."""
    sep = ""
-    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr, **kwargs):
+    def __init__(self, logfile=sys.stderr, **kwargs):
        self.logfile = logfile
        self.transcribe_kargs = {}
        self.original_language = lan
-        self.model_path = kwargs.get('model_path', './large-v3.pt')
+        for key, value in kwargs.items():
-        self.frame_threshold = kwargs.get('frame_threshold', 25)
+            setattr(self, key, value)
-        self.audio_max_len = kwargs.get('audio_max_len', 20.0)
+
-        self.audio_min_len = kwargs.get('audio_min_len', 0.0)
+        if self.decoder_type is None:
-        self.segment_length = kwargs.get('segment_length', 0.5)
+            self.decoder_type = 'greedy' if self.beams == 1 else 'beam'
-        self.beams = kwargs.get('beams', 1)
+
        self.decoder_type = kwargs.get('decoder_type', 'greedy' if self.beams == 1 else 'beam')
        self.task = kwargs.get('task', 'transcribe')
        self.cif_ckpt_path = kwargs.get('cif_ckpt_path', None)
        self.never_fire = kwargs.get('never_fire', False)
        self.init_prompt = kwargs.get('init_prompt', None)
        self.static_init_prompt = kwargs.get('static_init_prompt', None)
        self.max_context_tokens = kwargs.get('max_context_tokens', None)
        self.warmup_file = kwargs.get('warmup_file', None)
        self.preload_model_count = kwargs.get('preload_model_count', 1)
        self.disable_fast_encoder = kwargs.get('disable_fast_encoder', False)
        self.fast_encoder = False
-        if model_dir is not None:
+        if self.model_dir is not None:
-            self.model_path = model_dir
+            self.model_path = self.model_dir
-        elif modelsize is not None:
+        elif self.model_size is not None:
            model_mapping = {
                'tiny': './tiny.pt',
                'base': './base.pt',
@@ -185,13 +171,13 @@ class SimulStreamingASR():
                'large-v3': './large-v3.pt',
                'large': './large-v3.pt'
            }
-            self.model_path = model_mapping.get(modelsize, f'./{modelsize}.pt')
+            self.model_path = model_mapping.get(self.model_size, f'./{self.model_size}.pt')
        self.cfg = AlignAttConfig(
                model_path=self.model_path,
-                segment_length=self.segment_length,
+                segment_length=self.min_chunk_size,
                frame_threshold=self.frame_threshold,
-                language=self.original_language,
+                language=self.lan,
                audio_max_len=self.audio_max_len,
                audio_min_len=self.audio_min_len,
                cif_ckpt_path=self.cif_ckpt_path,
@@ -210,8 +196,12 @@ class SimulStreamingASR():
        else:
            self.tokenizer = None
-        self.model_name = os.path.basename(self.cfg.model_path).replace(".pt", "")
+        if self.model_dir:
-        self.model_path = os.path.dirname(os.path.abspath(self.cfg.model_path))
+            self.model_name = self.model_dir
            self.model_path = None
        else:
            self.model_name = os.path.basename(self.cfg.model_path).replace(".pt", "")
            self.model_path = os.path.dirname(os.path.abspath(self.cfg.model_path))
        self.mlx_encoder, self.fw_encoder = None, None
        if not self.disable_fast_encoder:
@@ -233,7 +223,12 @@ class SimulStreamingASR():
    def load_model(self):
-        whisper_model = load_model(name=self.model_name, download_root=self.model_path, decoder_only=self.fast_encoder)
+        whisper_model = load_model(
            name=self.model_name,
            download_root=self.model_path,
            decoder_only=self.fast_encoder,
            custom_alignment_heads=self.custom_alignment_heads
            )
        warmup_audio = load_file(self.warmup_file)
        if warmup_audio is not None:
            warmup_audio = torch.from_numpy(warmup_audio).float()
@@ -249,7 +244,7 @@ class SimulStreamingASR():
            else:
                # For standard encoder, use the original transcribe warmup
                warmup_audio = load_file(self.warmup_file)
-                whisper_model.transcribe(warmup_audio, language=self.original_language if self.original_language != 'auto' else None)
+                whisper_model.transcribe(warmup_audio, language=self.lan if self.lan != 'auto' else None)
        return whisper_model
    def get_new_model_instance(self):
--- a/whisperlivekit/simul_whisper/whisper/init.py
+++ b/whisperlivekit/simul_whisper/whisper/init.py
@@ -105,7 +105,8 @@ def load_model(
    device: Optional[Union[str, torch.device]] = None,
    download_root: str = None,
    in_memory: bool = False,
-    decoder_only=False
+    decoder_only=False,
    custom_alignment_heads=None
 ) -> Whisper:
    """
    Load a Whisper ASR model
@@ -135,15 +136,17 @@ def load_model(
        download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper")
    if name in _MODELS:
-        checkpoint_file = _download(_MODELS[name], download_root, in_memory)
+        checkpoint_file = _download(_MODELS[name], download_root, in_memory)        
        alignment_heads = _ALIGNMENT_HEADS[name]
    elif os.path.isfile(name):
        checkpoint_file = open(name, "rb").read() if in_memory else name
        alignment_heads = None
    else:
        raise RuntimeError(
            f"Model {name} not found; available models = {available_models()}"
        )
    alignment_heads = _ALIGNMENT_HEADS.get(name, None)
    if custom_alignment_heads:
        alignment_heads = custom_alignment_heads.encode()
    with (
        io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")
--- a/whisperlivekit/timed_objects.py
+++ b/whisperlivekit/timed_objects.py
@@ -43,6 +43,12 @@ class TimedText:
@dataclass()
 class ASRToken(TimedText):
    corrected_speaker: Optional[int] = -1
    validated_speaker: bool = False
    validated_text: bool = False
    validated_language: bool = False
    def with_offset(self, offset: float) -> "ASRToken":
        """Return a new token with the time offset added."""
        return ASRToken(self.start + offset, self.end + offset, self.text, self.speaker, self.probability, detected_language=self.detected_language)
@@ -126,7 +132,7 @@ class Line(TimedText):
    def to_dict(self):
        _dict = {
-            'speaker': int(self.speaker),
+            'speaker': int(self.speaker) if self.speaker != -1 else 1,
            'text': self.text,
            'start': format_time(self.start),
            'end': format_time(self.end),
@@ -151,7 +157,7 @@ class FrontData():
    def to_dict(self):
        _dict = {
            'status': self.status,
-            'lines': [line.to_dict() for line in self.lines],
+            'lines': [line.to_dict() for line in self.lines if (line.text or line.speaker == -2)],
            'buffer_transcription': self.buffer_transcription,
            'buffer_diarization': self.buffer_diarization,
            'remaining_time_transcription': self.remaining_time_transcription,
@@ -169,6 +175,7 @@ class ChangeSpeaker:
@dataclass  
 class State():
    tokens: list
    last_validated_token: int
    translated_segments: list
    buffer_transcription: str
    end_buffer: float
--- a/whisperlivekit/translation/translation.py
+++ b/whisperlivekit/translation/translation.py
@@ -21,27 +21,27 @@ class TranslationModel():
    device: str
    tokenizer: dict = field(default_factory=dict)
    backend_type: str = 'ctranslate2'
-    model_size: str = '600M'
+    nllb_size: str = '600M'
    def get_tokenizer(self, input_lang):
        if not self.tokenizer.get(input_lang, False):
            self.tokenizer[input_lang] = transformers.AutoTokenizer.from_pretrained(
-                f"facebook/nllb-200-distilled-{self.model_size}",
+                f"facebook/nllb-200-distilled-{self.nllb_size}",
                src_lang=input_lang,
                clean_up_tokenization_spaces=True
            )
        return self.tokenizer[input_lang]
-def load_model(src_langs, backend='ctranslate2', model_size='600M'):
+def load_model(src_langs, nllb_backend='ctranslate2', nllb_size='600M'):
    device = "cuda" if torch.cuda.is_available() else "cpu"
-    MODEL = f'nllb-200-distilled-{model_size}-ctranslate2'
+    MODEL = f'nllb-200-distilled-{nllb_size}-ctranslate2'
-    if backend=='ctranslate2':
+    if nllb_backend=='ctranslate2':
        MODEL_GUY = 'entai2965'
        huggingface_hub.snapshot_download(MODEL_GUY + '/' + MODEL,local_dir=MODEL)
        translator = ctranslate2.Translator(MODEL,device=device)
-    elif backend=='transformers':
+    elif nllb_backend=='transformers':
-        translator = transformers.AutoModelForSeq2SeqLM.from_pretrained(f"facebook/nllb-200-distilled-{model_size}")
+        translator = transformers.AutoModelForSeq2SeqLM.from_pretrained(f"facebook/nllb-200-distilled-{nllb_size}")
    tokenizer = dict()
    for src_lang in src_langs:
        if src_lang != 'auto':
@@ -50,9 +50,9 @@ def load_model(src_langs, backend='ctranslate2', model_size='600M'):
    translation_model = TranslationModel(
        translator=translator,
        tokenizer=tokenizer,
-        backend_type=backend,
+        backend_type=nllb_backend,
        device = device,
-        model_size = model_size
+        nllb_size = nllb_size
    )
    for src_lang in src_langs:
        if src_lang != 'auto':
@@ -157,7 +157,7 @@ if __name__ == '__main__':
    test = test_string.split(' ')
    step = len(test) // 3
-    shared_model = load_model([input_lang], backend='ctranslate2')
+    shared_model = load_model([input_lang], nllb_backend='ctranslate2')
    online_translation = OnlineTranslation(shared_model, input_languages=[input_lang], output_languages=[output_lang])
    beg_inference = time.time()    
--- a/whisperlivekit/web/live_transcription.css
+++ b/whisperlivekit/web/live_transcription.css
@@ -72,6 +72,12 @@
  --label-trans-text: #111111;
 }
 html.is-extension
 {
    width: 350px;
    height: 500px;
 }
 body {
  font-family: ui-sans-serif, system-ui, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
  margin: 0;
@@ -191,6 +197,14 @@ body {
  justify-content: center;
  align-items: center;
  gap: 15px;
  position: relative;
  flex-wrap: wrap;
 }
 .buttons-container {
  display: flex;
  align-items: center;
  gap: 15px;
 }
 .settings {
@@ -200,6 +214,66 @@ body {
  gap: 12px;
 }
 .settings-toggle {
  width: 40px;
  height: 40px;
  border: none;
  border-radius: 50%;
  background-color: var(--button-bg);
  border: 1px solid var(--button-border);
  cursor: pointer;
  display: none;
  align-items: center;
  justify-content: center;
  transition: all 0.2s ease;
 }
 .settings-toggle:hover {
  background-color: var(--chip-bg);
 }
 .settings-toggle.active {
  background-color: var(--chip-bg);
 }
 .settings-toggle img {
  width: 20px;
  height: 20px;
 }
@media (max-width: 10000px) {
  .settings-toggle {
    display: flex;
  }
  .settings {
    display: none;
    background: var(--bg);
    border: 1px solid var(--border);
    border-radius: 18px;
    padding: 12px;
  }
  .settings.visible {
    display: flex;
  }
 }
@media (max-width: 600px) {
  .settings-container {
    flex-direction: column;
    align-items: center;
    gap: 10px;
  }
  .buttons-container {
    display: flex;
    justify-content: center;
    align-items: center;
    gap: 15px;
  }
 }
 .field {
  display: flex;
  flex-direction: column;
@@ -409,7 +483,6 @@ label {
 .buffer_diarization {
  color: var(--label-dia-text);
  margin-left: 4px;
 }
 .buffer_transcription {
@@ -454,7 +527,7 @@ label {
 }
 /* for smaller screens */
-@media (max-width: 768px) {
+@media (max-width: 200px) {
  .header-container {
    padding: 15px;
  }
@@ -464,6 +537,10 @@ label {
    gap: 10px;
  }
  .buttons-container {
    gap: 10px;
  }
  .settings {
    justify-content: center;
    gap: 8px;
@@ -522,8 +599,6 @@ label {
 .label_language {
  background-color: var(--chip-bg);
  margin-bottom: 0px;
  margin-top: 5px;
  height: 18.5px;
  border-radius: 100px;
  padding: 2px 8px;
  margin-left: 10px;
--- a/whisperlivekit/web/live_transcription.html
+++ b/whisperlivekit/web/live_transcription.html
@@ -5,23 +5,29 @@
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>WhisperLiveKit</title>
-    <link rel="stylesheet" href="/web/live_transcription.css" />
+    <link rel="stylesheet" href="live_transcription.css" />
 </head>
 <body>
    <div class="header-container">
        <div class="settings-container">
-            <button id="recordButton">
+            <div class="buttons-container">
-                <div class="shape-container">
+                <button id="recordButton">
-                    <div class="shape"></div>
+                    <div class="shape-container">
-                </div>
+                        <div class="shape"></div>
                <div class="recording-info">
                    <div class="wave-container">
                        <canvas id="waveCanvas"></canvas>
                    </div>
-                    <div class="timer">00:00</div>
+                    <div class="recording-info">
-                </div>
+                        <div class="wave-container">
-            </button>
+                            <canvas id="waveCanvas"></canvas>
                        </div>
                        <div class="timer">00:00</div>
                    </div>
                </button>
                <button id="settingsToggle" class="settings-toggle" title="Show/hide settings">
                    <img src="web/src/settings.svg" alt="Settings" />
                </button>
            </div>
            <div class="settings">
                <div class="field">
@@ -67,7 +73,7 @@
        <div id="linesTranscript"></div>
    </div>
-    <script src="/web/live_transcription.js"></script>
+    <script src="live_transcription.js"></script>
 </body>
-</html>
+</html>
--- a/whisperlivekit/web/live_transcription.js
+++ b/whisperlivekit/web/live_transcription.js
@@ -1,4 +1,8 @@
-/* Theme, WebSocket, recording, rendering logic extracted from inline script and adapted for segmented theme control and WS caption */
+const isExtension = typeof chrome !== 'undefined' && chrome.runtime && chrome.runtime.getURL;
 if (isExtension) {
  document.documentElement.classList.add('is-extension');
 }
 const isWebContext = !isExtension;
 let isRecording = false;
 let websocket = null;
@@ -25,6 +29,8 @@ let selectedMicrophoneId = null;
 let serverUseAudioWorklet = null;
 let configReadyResolve;
 const configReady = new Promise((r) => (configReadyResolve = r));
 let outputAudioContext = null;
 let audioSource = null;
 waveCanvas.width = 60 * (window.devicePixelRatio || 1);
 waveCanvas.height = 30 * (window.devicePixelRatio || 1);
@@ -40,6 +46,21 @@ const timerElement = document.querySelector(".timer");
 const themeRadios = document.querySelectorAll('input[name="theme"]');
 const microphoneSelect = document.getElementById("microphoneSelect");
 const settingsToggle = document.getElementById("settingsToggle");
 const settingsDiv = document.querySelector(".settings");
 // if (isExtension) {
 //   chrome.runtime.onInstalled.addListener((details) => {
 //     if (details.reason.search(/install/g) === -1) {
 //       return;
 //     }
 //     chrome.tabs.create({
 //       url: chrome.runtime.getURL("welcome.html"),
 //       active: true
 //     });
 //   });
 // }
 const translationIcon = `<svg xmlns="http://www.w3.org/2000/svg" height="12px" viewBox="0 -960 960 960" width="12px" fill="#5f6368"><path d="m603-202-34 97q-4 11-14 18t-22 7q-20 0-32.5-16.5T496-133l152-402q5-11 15-18t22-7h30q12 0 22 7t15 18l152 403q8 19-4 35.5T868-80q-13 0-22.5-7T831-106l-34-96H603ZM362-401 188-228q-11 11-27.5 11.5T132-228q-11-11-11-28t11-28l174-174q-35-35-63.5-80T190-640h84q20 39 40 68t48 58q33-33 68.5-92.5T484-720H80q-17 0-28.5-11.5T40-760q0-17 11.5-28.5T80-800h240v-40q0-17 11.5-28.5T360-880q17 0 28.5 11.5T400-840v40h240q17 0 28.5 11.5T680-760q0 17-11.5 28.5T640-720h-76q-21 72-63 148t-83 116l96 98-30 82-122-125Zm266 129h144l-72-204-72 204Z"/></svg>`
 const silenceIcon = `<svg xmlns="http://www.w3.org/2000/svg" style="vertical-align: text-bottom;" height="14px" viewBox="0 -960 960 960" width="14px" fill="#5f6368"><path d="M514-556 320-752q9-3 19-5.5t21-2.5q66 0 113 47t47 113q0 11-1.5 22t-4.5 22ZM40-200v-32q0-33 17-62t47-44q51-26 115-44t141-18q26 0 49.5 2.5T456-392l-56-54q-9 3-19 4.5t-21 1.5q-66 0-113-47t-47-113q0-11 1.5-21t4.5-19L84-764q-11-11-11-28t11-28q12-12 28.5-12t27.5 12l675 685q11 11 11.5 27.5T816-80q-11 13-28 12.5T759-80L641-200h39q0 33-23.5 56.5T600-120H120q-33 0-56.5-23.5T40-200Zm80 0h480v-32q0-14-4.5-19.5T580-266q-36-18-92.5-36T360-320q-71 0-127.5 18T140-266q-9 5-14.5 14t-5.5 20v32Zm240 0Zm560-400q0 69-24.5 131.5T829-355q-12 14-30 15t-32-13q-13-13-12-31t12-33q30-38 46.5-85t16.5-98q0-51-16.5-97T767-781q-12-15-12.5-33t12.5-32q13-14 31.5-13.5T829-845q42 51 66.5 113.5T920-600Zm-182 0q0 32-10 61.5T700-484q-11 15-29.5 15.5T638-482q-13-13-13.5-31.5T633-549q6-11 9.5-24t3.5-27q0-14-3.5-27t-9.5-25q-9-17-8.5-35t13.5-31q14-14 32.5-13.5T700-716q18 25 28 54.5t10 61.5Z"/></svg>`;
 const languageIcon = `<svg xmlns="http://www.w3.org/2000/svg" height="12" viewBox="0 -960 960 960" width="12" fill="#5f6368"><path d="M480-80q-82 0-155-31.5t-127.5-86Q143-252 111.5-325T80-480q0-83 31.5-155.5t86-127Q252-817 325-848.5T480-880q83 0 155.5 31.5t127 86q54.5 54.5 86 127T880-480q0 82-31.5 155t-86 127.5q-54.5 54.5-127 86T480-80Zm0-82q26-36 45-75t31-83H404q12 44 31 83t45 75Zm-104-16q-18-33-31.5-68.5T322-320H204q29 50 72.5 87t99.5 55Zm208 0q56-18 99.5-55t72.5-87H638q-9 38-22.5 73.5T584-178ZM170-400h136q-3-20-4.5-39.5T300-480q0-21 1.5-40.5T306-560H170q-5 20-7.5 39.5T160-480q0 21 2.5 40.5T170-400Zm216 0h188q3-20 4.5-39.5T580-480q0-21-1.5-40.5T574-560H386q-3 20-4.5 39.5T380-480q0 21 1.5 40.5T386-400Zm268 0h136q5-20 7.5-39.5T800-480q0-21-2.5-40.5T790-560H654q3 20 4.5 39.5T660-480q0 21-1.5 40.5T654-400Zm-16-240h118q-29-50-72.5-87T584-782q18 33 31.5 68.5T638-640Zm-234 0h152q-12-44-31-83t-45-75q-26 36-45 75t-31 83Zm-200 0h118q9-38 22.5-73.5T376-782q-56 18-99.5 55T204-640Z"/></svg>`
@@ -156,10 +177,16 @@ function fmt1(x) {
  return Number.isFinite(n) ? n.toFixed(1) : x;
 }
-// Default WebSocket URL computation
+let host, port, protocol;
-const host = window.location.hostname || "localhost";
+port = 8000;
-const port = window.location.port;
+if (isExtension) {
-const protocol = window.location.protocol === "https:" ? "wss" : "ws";
+    host = "localhost";
    protocol = "ws";
 } else {
    host = window.location.hostname || "localhost";
    port = window.location.port;
    protocol = window.location.protocol === "https:" ? "wss" : "ws";
 }
 const defaultWebSocketUrl = `${protocol}://${host}${port ? ":" + port : ""}/asr`;
 // Populate default caption and input
@@ -390,10 +417,13 @@ function renderLinesWithBuffer(
      }
      if (item.translation) {
-        currentLineText += `<div class="label_translation">
+        currentLineText += `
-          ${translationIcon}
+            <div>
-          <span>${item.translation}</span>
+                <div class="label_translation">
-        </div>`;
+                    ${translationIcon}
                    <span>${item.translation}</span>
                </div>
            </div>`;
      }
      return currentLineText.trim().length > 0 || speakerLabel.length > 0
@@ -468,11 +498,44 @@ async function startRecording() {
      console.log("Error acquiring wake lock.");
    }
-    const audioConstraints = selectedMicrophoneId 
+    let stream;
-      ? { audio: { deviceId: { exact: selectedMicrophoneId } } }
+    
-      : { audio: true };
+    // chromium extension. in the future, both chrome page audio and mic will be used
-
+    if (isExtension) {
-    const stream = await navigator.mediaDevices.getUserMedia(audioConstraints);
+      try {
        stream = await new Promise((resolve, reject) => {
          chrome.tabCapture.capture({audio: true}, (s) => {
            if (s) {
              resolve(s);
            } else {
              reject(new Error('Tab capture failed or not available'));
            }
          });
        });
        try {
          outputAudioContext = new (window.AudioContext || window.webkitAudioContext)();
          audioSource = outputAudioContext.createMediaStreamSource(stream);
          audioSource.connect(outputAudioContext.destination);
        } catch (audioError) {
          console.warn('could not preserve system audio:', audioError);
        }
        statusText.textContent = "Using tab audio capture.";
      } catch (tabError) {
        console.log('Tab capture not available, falling back to microphone', tabError);
        const audioConstraints = selectedMicrophoneId
          ? { audio: { deviceId: { exact: selectedMicrophoneId } } }
          : { audio: true };
        stream = await navigator.mediaDevices.getUserMedia(audioConstraints);
        statusText.textContent = "Using microphone audio.";
      }
    } else if (isWebContext) {
      const audioConstraints = selectedMicrophoneId 
        ? { audio: { deviceId: { exact: selectedMicrophoneId } } }
        : { audio: true };
      stream = await navigator.mediaDevices.getUserMedia(audioConstraints);
    }
    audioContext = new (window.AudioContext || window.webkitAudioContext)();
    analyser = audioContext.createAnalyser();
@@ -606,6 +669,16 @@ async function stopRecording() {
    audioContext = null;
  }
  if (audioSource) {
    audioSource.disconnect();
    audioSource = null;
  }
  if (outputAudioContext && outputAudioContext.state !== "closed") {
    outputAudioContext.close()
    outputAudioContext = null;
  }
  if (animationFrame) {
    cancelAnimationFrame(animationFrame);
    animationFrame = null;
@@ -657,7 +730,7 @@ function updateUI() {
      statusText.textContent = "Please wait for processing to complete...";
    }
  } else if (isRecording) {
-    statusText.textContent = "Recording...";
+    statusText.textContent = "";
  } else {
    if (
      statusText.textContent !== "Finished processing audio! Ready to record again." &&
@@ -691,3 +764,40 @@ navigator.mediaDevices.addEventListener('devicechange', async () => {
    console.log("Error re-enumerating microphones:", error);
  }
 });
 settingsToggle.addEventListener("click", () => {
 settingsDiv.classList.toggle("visible");
 settingsToggle.classList.toggle("active");
 });
 if (isExtension) {
  async function checkAndRequestPermissions() {
    const micPermission = await navigator.permissions.query({
      name: "microphone",
    });
    const permissionDisplay = document.getElementById("audioPermission");
    if (permissionDisplay) {
      permissionDisplay.innerText = `MICROPHONE: ${micPermission.state}`;
    }
    // if (micPermission.state !== "granted") {
    //   chrome.tabs.create({ url: "welcome.html" });
    // }
    const intervalId = setInterval(async () => {
      const micPermission = await navigator.permissions.query({
        name: "microphone",
      });
      if (micPermission.state === "granted") {
        if (permissionDisplay) {
          permissionDisplay.innerText = `MICROPHONE: ${micPermission.state}`;
        }
        clearInterval(intervalId);
      }
    }, 100);
  }
  void checkAndRequestPermissions();
 }
--- a/whisperlivekit/web/web_interface.py
+++ b/whisperlivekit/web/web_interface.py
@@ -23,6 +23,24 @@ def get_inline_ui_html():
        with resources.files('whisperlivekit.web').joinpath('live_transcription.js').open('r', encoding='utf-8') as f:
            js_content = f.read()
        with resources.files('whisperlivekit.web').joinpath('pcm_worklet.js').open('r', encoding='utf-8') as f:
            worklet_code = f.read()
        with resources.files('whisperlivekit.web').joinpath('recorder_worker.js').open('r', encoding='utf-8') as f:
            worker_code = f.read()
        js_content = js_content.replace(
            'await audioContext.audioWorklet.addModule("/web/pcm_worklet.js");',
            'const workletBlob = new Blob([`' + worklet_code + '`], { type: "application/javascript" });\n' +
            'const workletUrl = URL.createObjectURL(workletBlob);\n' +
            'await audioContext.audioWorklet.addModule(workletUrl);'
        )
        js_content = js_content.replace(
            'recorderWorker = new Worker("/web/recorder_worker.js");',
            'const workerBlob = new Blob([`' + worker_code + '`], { type: "application/javascript" });\n' +
            'const workerUrl = URL.createObjectURL(workerBlob);\n' +
            'recorderWorker = new Worker(workerUrl);'
        )
        # SVG files
        with resources.files('whisperlivekit.web').joinpath('src', 'system_mode.svg').open('r', encoding='utf-8') as f:
            system_svg = f.read()
@@ -33,15 +51,18 @@ def get_inline_ui_html():
        with resources.files('whisperlivekit.web').joinpath('src', 'dark_mode.svg').open('r', encoding='utf-8') as f:
            dark_svg = f.read()
            dark_data_uri = f"data:image/svg+xml;base64,{base64.b64encode(dark_svg.encode('utf-8')).decode('utf-8')}"
-        
+        with resources.files('whisperlivekit.web').joinpath('src', 'settings.svg').open('r', encoding='utf-8') as f:
            settings = f.read()
            settings_uri = f"data:image/svg+xml;base64,{base64.b64encode(settings.encode('utf-8')).decode('utf-8')}"
        # Replace external references
        html_content = html_content.replace(
-            '<link rel="stylesheet" href="/web/live_transcription.css" />',
+            '<link rel="stylesheet" href="live_transcription.css" />',
            f'<style>\n{css_content}\n</style>'
        )
        html_content = html_content.replace(
-            '<script src="/web/live_transcription.js"></script>',
+            '<script src="live_transcription.js"></script>',
            f'<script>\n{js_content}\n</script>'
        )
@@ -61,6 +82,11 @@ def get_inline_ui_html():
            f'<img src="{dark_data_uri}" alt="" />'
        )
        html_content = html_content.replace(
            '<img src="web/src/settings.svg" alt="Settings" />',
            f'<img src="{settings_uri}" alt="" />'
        )
        return html_content
    except Exception as e:
--- a/whisperlivekit/whisper_streaming_custom/backends.py
+++ b/whisperlivekit/whisper_streaming_custom/backends.py
@@ -11,14 +11,14 @@ class ASRBase:
    sep = " "  # join transcribe words with this character (" " for whisper_timestamped,
              # "" for faster-whisper because it emits the spaces when needed)
-    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr):
+    def __init__(self, lan, model_size=None, cache_dir=None, model_dir=None, logfile=sys.stderr):
        self.logfile = logfile
        self.transcribe_kargs = {}
        if lan == "auto":
            self.original_language = None
        else:
            self.original_language = lan
-        self.model = self.load_model(modelsize, cache_dir, model_dir)
+        self.model = self.load_model(model_size, cache_dir, model_dir)
    def with_offset(self, offset: float) -> ASRToken:
        # This method is kept for compatibility (typically you will use ASRToken.with_offset)
@@ -27,7 +27,7 @@ class ASRBase:
    def __repr__(self):
        return f"ASRToken(start={self.start:.2f}, end={self.end:.2f}, text={self.text!r})"
-    def load_model(self, modelsize, cache_dir, model_dir):
+    def load_model(self, model_size, cache_dir, model_dir):
        raise NotImplementedError("must be implemented in the child class")
    def transcribe(self, audio, init_prompt=""):
@@ -41,7 +41,7 @@ class WhisperTimestampedASR(ASRBase):
    """Uses whisper_timestamped as the backend."""
    sep = " "
-    def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
+    def load_model(self, model_size=None, cache_dir=None, model_dir=None):
        import whisper
        import whisper_timestamped
        from whisper_timestamped import transcribe_timestamped
@@ -49,7 +49,7 @@ class WhisperTimestampedASR(ASRBase):
        self.transcribe_timestamped = transcribe_timestamped
        if model_dir is not None:
            logger.debug("ignoring model_dir, not implemented")
-        return whisper.load_model(modelsize, download_root=cache_dir)
+        return whisper.load_model(model_size, download_root=cache_dir)
    def transcribe(self, audio, init_prompt=""):
        result = self.transcribe_timestamped(
@@ -88,17 +88,17 @@ class FasterWhisperASR(ASRBase):
    """Uses faster-whisper as the backend."""
    sep = ""
-    def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
+    def load_model(self, model_size=None, cache_dir=None, model_dir=None):
        from faster_whisper import WhisperModel
        if model_dir is not None:
            logger.debug(f"Loading whisper model from model_dir {model_dir}. "
-                         f"modelsize and cache_dir parameters are not used.")
+                         f"model_size and cache_dir parameters are not used.")
            model_size_or_path = model_dir
-        elif modelsize is not None:
+        elif model_size is not None:
-            model_size_or_path = modelsize
+            model_size_or_path = model_size
        else:
-            raise ValueError("Either modelsize or model_dir must be set")
+            raise ValueError("Either model_size or model_dir must be set")
        device = "auto" # Allow CTranslate2 to decide available device
        compute_type = "auto" # Allow CTranslate2 to decide faster compute type
@@ -149,18 +149,18 @@ class MLXWhisper(ASRBase):
    """
    sep = ""
-    def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
+    def load_model(self, model_size=None, cache_dir=None, model_dir=None):
        from mlx_whisper.transcribe import ModelHolder, transcribe
        import mlx.core as mx
        if model_dir is not None:
-            logger.debug(f"Loading whisper model from model_dir {model_dir}. modelsize parameter is not used.")
+            logger.debug(f"Loading whisper model from model_dir {model_dir}. model_size parameter is not used.")
            model_size_or_path = model_dir
-        elif modelsize is not None:
+        elif model_size is not None:
-            model_size_or_path = self.translate_model_name(modelsize)
+            model_size_or_path = self.translate_model_name(model_size)
-            logger.debug(f"Loading whisper model {modelsize}. You use mlx whisper, so {model_size_or_path} will be used.")
+            logger.debug(f"Loading whisper model {model_size}. You use mlx whisper, so {model_size_or_path} will be used.")
        else:
-            raise ValueError("Either modelsize or model_dir must be set")
+            raise ValueError("Either model_size or model_dir must be set")
        self.model_size_or_path = model_size_or_path
        dtype = mx.float16
--- a/whisperlivekit/whisper_streaming_custom/online_asr.py
+++ b/whisperlivekit/whisper_streaming_custom/online_asr.py
@@ -106,9 +106,6 @@ class OnlineASRProcessor:
    def __init__(
        self,
        asr,
        tokenize_method: Optional[callable] = None,
        buffer_trimming: Tuple[str, float] = ("segment", 15),
        confidence_validation = False,
        logfile=sys.stderr,
    ):
        """
@@ -119,13 +116,14 @@ class OnlineASRProcessor:
        buffer_trimming: A tuple (option, seconds), where option is either "sentence" or "segment".
        """
        self.asr = asr
-        self.tokenize = tokenize_method
+        self.tokenize = asr.tokenizer
        self.logfile = logfile
-        self.confidence_validation = confidence_validation
+        self.confidence_validation = asr.confidence_validation
        self.global_time_offset = 0.0
        self.init()
-        self.buffer_trimming_way, self.buffer_trimming_sec = buffer_trimming
+        self.buffer_trimming_way = asr.buffer_trimming
        self.buffer_trimming_sec = asr.buffer_trimming_sec
        if self.buffer_trimming_way not in ["sentence", "segment"]:
            raise ValueError("buffer_trimming must be either 'sentence' or 'segment'")
--- a/whisperlivekit/whisper_streaming_custom/whisper_online.py
+++ b/whisperlivekit/whisper_streaming_custom/whisper_online.py
@@ -6,6 +6,7 @@ from functools import lru_cache
 import time
 import logging
 from .backends import FasterWhisperASR, MLXWhisper, WhisperTimestampedASR, OpenaiApiASR
 from whisperlivekit.warmup import warmup_asr
 logger = logging.getLogger(__name__)
@@ -63,11 +64,23 @@ def create_tokenizer(lan):
    return WtPtok()
-def backend_factory(args):
+def backend_factory(
-    backend = args.backend
+            backend,
            lan,
            model_size,
            model_cache_dir,
            model_dir,
            task,
            buffer_trimming,
            buffer_trimming_sec,
            confidence_validation,
            warmup_file=None,
            min_chunk_size=None,
        ):
    backend = backend
    if backend == "openai-api":
        logger.debug("Using OpenAI API.")
-        asr = OpenaiApiASR(lan=args.lan)        
+        asr = OpenaiApiASR(lan=lan)        
    else:
        if backend == "faster-whisper":
            asr_cls = FasterWhisperASR
@@ -77,34 +90,33 @@ def backend_factory(args):
            asr_cls = WhisperTimestampedASR
        # Only for FasterWhisperASR and WhisperTimestampedASR
-        size = args.model
+
        t = time.time()
-        logger.info(f"Loading Whisper {size} model for language {args.lan}...")
+        logger.info(f"Loading Whisper {model_size} model for language {lan}...")
        asr = asr_cls(
-            modelsize=size,
+            model_size=model_size,
-            lan=args.lan,
+            lan=lan,
-            cache_dir=getattr(args, 'model_cache_dir', None),
+            cache_dir=model_cache_dir,
-            model_dir=getattr(args, 'model_dir', None),
+            model_dir=model_dir,
        )
        e = time.time()
        logger.info(f"done. It took {round(e-t,2)} seconds.")
-    # Apply common configurations
+    if task == "translate":
    if getattr(args, "vad", False):  # Checks if VAD argument is present and True
        logger.info("Setting VAD filter")
        asr.use_vad()
    language = args.lan
    if args.task == "translate":
        if backend != "simulstreaming":
            asr.set_translate_task()
        tgt_language = "en"  # Whisper translates into English
    else:
-        tgt_language = language  # Whisper transcribes in this language
+        tgt_language = lan  # Whisper transcribes in this language
    # Create the tokenizer
-    if args.buffer_trimming == "sentence":
+    if buffer_trimming == "sentence":
        tokenizer = create_tokenizer(tgt_language)
    else:
        tokenizer = None
-    return asr, tokenizer
+    
    warmup_asr(asr, warmup_file)
    asr.confidence_validation = confidence_validation
    asr.tokenizer = tokenizer
    asr.buffer_trimming = buffer_trimming
    asr.buffer_trimming_sec = buffer_trimming_sec
    return asr
Author	SHA1	Message	Date
Quentin Fuxa	12a69205ed	bump to 0.2.12	2025-10-06 19:59:05 +02:00
Quentin Fuxa	1f684cdd97	fixes #251	2025-10-06 19:53:27 +02:00
Quentin Fuxa	290470dd60	forwarded_allow_ips in core	2025-10-04 23:04:00 +02:00
Quentin Fuxa	425ac7b51d	forwarded_allow_ips in core	2025-10-04 23:04:00 +02:00
Quentin Fuxa	0382cfbeba	forwarded_allow_ips in core	2025-10-04 23:04:00 +02:00
Quentin Fuxa	9b1e061b32	forwarded_allow_ips in core	2025-10-04 23:04:00 +02:00
Quentin Fuxa	b4abc158b9	Merge pull request #249 from Damrod/add-ip-forwarding-support fix wss for reverse proxying	2025-10-06 10:20:05 +02:00
Alvaro Ollero	5832d7433d	update documentation	2025-10-04 23:18:10 +02:00
Alvaro Ollero	3736458503	Uvicorn exposes a configuration option to enable reverse proxying from a trusted ip. This PR exposes it downstreams to end clients	2025-10-04 22:21:06 +02:00
Quentin Fuxa	374618e050	token speakers are only reattributed for token coming after last_validated_token	2025-10-04 09:52:00 +02:00
Quentin Fuxa	543972ef38	fixes #248	2025-10-04 09:52:00 +02:00
Quentin Fuxa	971f8473eb	update api doc	2025-10-05 11:09:47 +02:00
Quentin Fuxa	8434ef5efc	update api	2025-10-05 11:09:12 +02:00
Quentin Fuxa	73f36cc0ef	v0 doc new api	2025-10-02 23:04:00 +02:00
Quentin Fuxa	a7db39d999	solves incorrect spacing in buffer diarization	2025-10-02 23:04:00 +02:00
Quentin Fuxa	a153e11fe0	update when self.diarization_before_transcription	2025-09-28 11:04:00 +02:00
Quentin Fuxa	ca6f9246cc	force language = en for .en models	2025-09-28 11:04:00 +02:00
Quentin Fuxa	d080d675a8	cutom alignment heads parameter for custom models	2025-09-27 11:04:00 +02:00
Quentin Fuxa	40bff38933	Merge pull request #239 from msghik/feature/fine-tuned-model-support feat: Allow loading fine-tuned models in simulstreaming	2025-09-29 10:08:26 +02:00
Quentin Fuxa	2fe3ca0188	connect source to output destination when used as chrome extension to keep audio playing	2025-09-27 13:59:44 +02:00
Quentin Fuxa	545ea15c9a	ensure buffer size to be a multiple of the element size	2025-09-27 13:58:32 +02:00
Quentin Fuxa	8cbaeecc75	cutom alignment heads parameter for custom models	2025-09-27 11:04:00 +02:00
google-labs-jules[bot]	70e854b346	feat: Allow loading fine-tuned models in simulstreaming This change modifies the `simulstreaming` backend to support loading fine-tuned Whisper models via the `--model_dir` argument. The `SimulStreamingASR` class has been updated to: - Use the `model_dir` path directly to load the model, which is the correct procedure for fine-tuned `.pt` files. - Automatically disable the `faster-whisper` and `mlx-whisper` fast encoders when `model_dir` is used, as they are not compatible with standard fine-tuned models. The call site in `core.py` already passed the `model_dir` argument, so no changes were needed there. This change makes the `simulstreaming` backend more flexible and allows users to leverage their own custom models.	2025-09-27 07:29:30 +00:00
Quentin Fuxa	d55490cd27	typo and simpler conditions	2025-09-26 20:38:26 +02:00
Quentin Fuxa	1fa9e1f656	Merge pull request #238 from CorentinvdBdO/fix_install fix: translation in pyproject	2025-09-26 20:35:29 +02:00
cvandenbroek	994f30e1ed	fix: translation in pyproject	2025-09-26 20:08:35 +02:00
Quentin Fuxa	b22478c0b4	correct silences handling when language not auto	2025-09-25 23:20:00 +02:00
Quentin Fuxa	94c34efd90	chrome extension ws default to localhost	2025-09-25 23:04:00 +02:00
Quentin Fuxa	32099b9275	demo extension	2025-09-25 23:59:24 +02:00
Quentin Fuxa	9fc6654a4a	common frontend for web/ and chrome extension	2025-09-25 23:14:25 +02:00
Quentin Fuxa	cd9a32a36b	update archi to show fastapi server is independent from core	2025-09-21 11:03:00 +02:00
Quentin Fuxa	6caf3e0485	correct silence handling in translation	2025-09-27 11:58:00 +02:00
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