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RickRossTN commited on 8 days ago

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AI Voice Chat - hands-free voice assistant

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 ---
-title: Ai Voice Chat
-emoji: 📊
 colorFrom: green
-colorTo: green
 sdk: static
 pinned: false
 ---
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ---
+title: AI Voice Chat
+emoji: 🎙️
 colorFrom: green
+colorTo: blue
 sdk: static
 pinned: false
+license: mit
+short_description: 100% in-browser, hands-free AI voice chat
 ---
+# AI Voice Chat
+**A 100% in-browser solution for hands-free AI voice chat.** No API keys, no server, no data leaves your device. Uses Silero VAD, Whisper STT, WebLLM (Qwen 1.5B), and Supertonic TTS - all running locally via WebGPU.
+**Swap in your own LLM** - The built-in model is just a demo. The real value is the voice pipeline. Point it at Claude, GPT-4, Ollama, or any LLM in ~10 lines of code.
+## How It Works
+1. **Click the green phone button** to start a call
+2. **Speak naturally** - it detects when you're talking
+3. **Wait for the response** - the AI thinks and speaks back
+4. **Click the red button** to end the call
+## What's Running Locally
+| Component | Model | Purpose |
+|-----------|-------|---------|
+| 🎤 Speech-to-Text | Whisper | Converts your voice to text |
+| 🧠 LLM | Qwen 1.5B | Generates responses (swappable) |
+| 🔊 Text-to-Speech | Supertonic | Speaks the response |
+| 👂 Voice Detection | Silero VAD | Knows when you're talking |
+All models download once (~1GB) and are cached in your browser.
+## Requirements
+- **Browser**: Chrome 113+ or Edge 113+ (needs WebGPU)
+- **RAM**: ~4GB available
+- **Microphone**: Click "Allow" when prompted
+## Controls
+- 🎤 **Mic button** - Mute/unmute your microphone
+- 🔊 **Speaker button** - Mute/unmute the AI voice
+- 📢 **Voice selector** - Choose from 10 voices (F1-F5, M1-M5)
+- 📞 **Phone button** - Start/end the call
+## Privacy
+**100% local.** Your voice is processed in your browser. Nothing is sent to any server. The only network requests are to download the AI models (once, then cached).
+## Source Code
+Want to run this yourself or swap in a different LLM?
+👉 [GitHub Repository](https://github.com/iRelate-AI/voice-chat)
+Built with Next.js, WebLLM, Transformers.js, and Supertonic TTS.

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stt-worker-esm.js ADDED Viewed

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+/**
+ * STT Worker - ES Module version
+ * Handles VAD + Whisper transcription
+ */
+// Suppress noisy ONNX/hub warnings in worker
+const originalWarn = console.warn
+const originalError = console.error
+const suppress = (...args) => args.some(arg =>
+  typeof arg === 'string' && (
+    arg.includes('onnxruntime') ||
+    arg.includes('VerifyEachNodeIsAssignedToAnEp') ||
+    arg.includes('session_state.cc') ||
+    arg.includes('[W:onnxruntime') ||
+    arg.includes('content-length') ||
+    arg.includes('Unknown model class')
+  )
+)
+console.warn = function(...args) { if (!suppress(...args)) originalWarn.apply(console, args) }
+console.error = function(...args) { if (!suppress(...args)) originalError.apply(console, args) }
+import { AutoModel, pipeline, Tensor, env } from "https://cdn.jsdelivr.net/npm/@huggingface/[email protected]/+esm"
+// ============ Constants ============
+const INPUT_SAMPLE_RATE = 16000
+const INPUT_SAMPLE_RATE_MS = INPUT_SAMPLE_RATE / 1000
+const SPEECH_THRESHOLD = 0.5
+const EXIT_THRESHOLD = 0.15
+const MIN_SILENCE_DURATION_MS = 1000
+const MIN_SILENCE_DURATION_SAMPLES = MIN_SILENCE_DURATION_MS * INPUT_SAMPLE_RATE_MS
+const SPEECH_PAD_MS = 80
+const SPEECH_PAD_SAMPLES = SPEECH_PAD_MS * INPUT_SAMPLE_RATE_MS
+const MIN_SPEECH_DURATION_SAMPLES = 250 * INPUT_SAMPLE_RATE_MS
+const MAX_BUFFER_DURATION = 30
+const MAX_NUM_PREV_BUFFERS = Math.ceil(SPEECH_PAD_SAMPLES / 512)
+// ============ State ============
+let sileroVad = null
+let transcriber = null
+const BUFFER = new Float32Array(MAX_BUFFER_DURATION * INPUT_SAMPLE_RATE)
+let bufferPointer = 0
+let vadSr = null
+let vadState = null
+let isRecording = false
+let postSpeechSamples = 0
+const prevBuffers = []
+let isProcessing = false  // Lock to prevent concurrent processing
+const audioQueue = []     // Queue for audio chunks while processing
+// Configure - use HuggingFace CDN (has proper CORS)
+env.useBrowserCache = true
+env.allowLocalModels = false
+// Using default HuggingFace CDN - will switch to R2 once CORS is fixed
+// Detect WebGPU support and platform
+async function getDevice() {
+  // Check if iOS - use WASM there as WebGPU is unstable
+  const isIOS = /iPad|iPhone|iPod/.test(navigator.userAgent) ||
+    (navigator.platform === 'MacIntel' && navigator.maxTouchPoints > 1)
+  if (isIOS) {
+    console.debug("[STT Worker] iOS detected, using WASM for stability")
+    return "wasm"
+  }
+  if (typeof navigator !== "undefined" && navigator.gpu) {
+    try {
+      const adapter = await navigator.gpu.requestAdapter()
+      if (adapter) {
+        console.debug("[STT Worker] WebGPU available")
+        return "webgpu"
+      }
+    } catch (e) {
+      console.debug("[STT Worker] WebGPU check failed:", e)
+    }
+  }
+  console.debug("[STT Worker] Falling back to WASM")
+  return "wasm"
+}
+let selectedDevice = null
+console.debug("[STT Worker ESM] Loaded, AutoModel:", !!AutoModel)
+// ============ Model Loading ============
+async function loadModels() {
+  console.debug("[STT Worker] Starting model load...")
+  // Detect best available device
+  selectedDevice = await getDevice()
+  console.debug("[STT Worker] Using device:", selectedDevice)
+  self.postMessage({ type: "status", status: "loading", message: `Loading VAD model (${selectedDevice})...` })
+  // Load Silero VAD from onnx-community (public, no auth required)
+  console.debug("[STT Worker] Loading Silero VAD...")
+  sileroVad = await AutoModel.from_pretrained("onnx-community/silero-vad", {
+    config: { model_type: "custom" },
+    dtype: "fp32",
+    device: selectedDevice,
+    progress_callback: (progress) => {
+      if (progress.progress !== undefined) {
+        self.postMessage({ type: "progress", progress: progress.progress, message: `VAD: ${progress.status}` })
+      }
+    },
+  })
+  console.debug("[STT Worker] VAD loaded!")
+  // Init VAD tensors
+  vadSr = new Tensor("int64", [INPUT_SAMPLE_RATE], [])
+  vadState = new Tensor("float32", new Float32Array(2 * 1 * 128), [2, 1, 128])
+  self.postMessage({ type: "status", status: "loading", message: "Loading Whisper model..." })
+  // Load Whisper from onnx-community (public, no auth required)
+  console.debug("[STT Worker] Loading Whisper base...")
+  // TODO: Add whisper-tiny-en to R2 for mobile
+  const whisperModel = "onnx-community/whisper-base"
+  console.debug("[STT Worker] Using Whisper model:", whisperModel)
+  try {
+    transcriber = await pipeline("automatic-speech-recognition", whisperModel, {
+      dtype: "fp32",
+      device: selectedDevice,
+      progress_callback: (progress) => {
+        if (progress.progress !== undefined) {
+          self.postMessage({ type: "progress", progress: progress.progress, message: `Whisper: ${progress.status}` })
+        }
+      },
+    })
+    console.debug("[STT Worker] Whisper loaded!")
+  } catch (e) {
+    console.error("[STT Worker] Whisper load failed:", e)
+    self.postMessage({ type: "error", message: `Whisper failed: ${e.message}` })
+    return
+  }
+  // Warm up
+  try {
+    console.debug("[STT Worker] Warming up Whisper...")
+    await transcriber(new Float32Array(INPUT_SAMPLE_RATE))
+    console.debug("[STT Worker] Warmup complete!")
+  } catch (e) {
+    console.error("[STT Worker] Warmup failed:", e)
+    self.postMessage({ type: "error", message: `Warmup failed: ${e.message}` })
+    return
+  }
+  console.log("[STT Worker] Ready!")
+  self.postMessage({ type: "status", status: "ready", message: "Models loaded!" })
+}
+// ============ VAD ============
+async function vad(buffer) {
+  if (!sileroVad || !buffer || !vadSr) return false
+  const input = new Tensor("float32", buffer, [1, buffer.length])
+  const { stateN, output } = await sileroVad({ input, sr: vadSr, state: vadState })
+  vadState = stateN
+  const isSpeech = output.data[0]
+  return isSpeech > SPEECH_THRESHOLD || (isRecording && isSpeech >= EXIT_THRESHOLD)
+}
+// ============ Transcription ============
+async function transcribe(buffer) {
+  if (!transcriber) return ""
+  self.postMessage({ type: "status", status: "transcribing", message: "Transcribing..." })
+  // English-only model - don't specify language/task
+  const result = await transcriber(buffer)
+  return result.text.trim()
+}
+// ============ Buffer Management ============
+function resetAfterRecording(offset = 0) {
+  BUFFER.fill(0, offset)
+  bufferPointer = offset
+  isRecording = false
+  postSpeechSamples = 0
+  prevBuffers.length = 0
+}
+async function dispatchForTranscription(overflow) {
+  const overflowLength = overflow?.length ?? 0
+  const buffer = BUFFER.slice(0, bufferPointer + SPEECH_PAD_SAMPLES)
+  const prevLength = prevBuffers.reduce((acc, b) => acc + b.length, 0)
+  const paddedBuffer = new Float32Array(prevLength + buffer.length)
+  let offset = 0
+  for (const prev of prevBuffers) {
+    paddedBuffer.set(prev, offset)
+    offset += prev.length
+  }
+  paddedBuffer.set(buffer, offset)
+  const text = await transcribe(paddedBuffer)
+  if (text && !["", "[BLANK_AUDIO]"].includes(text)) {
+    self.postMessage({ type: "transcript", text, isFinal: true })
+  }
+  if (overflow) {
+    BUFFER.set(overflow, 0)
+  }
+  resetAfterRecording(overflowLength)
+  self.postMessage({ type: "status", status: "listening", message: "Listening..." })
+}
+// Queue audio and process sequentially
+function queueAudio(buffer) {
+  audioQueue.push(buffer)
+  if (!isProcessing) {
+    processQueue()
+  }
+}
+async function processQueue() {
+  if (isProcessing || audioQueue.length === 0) return
+  isProcessing = true
+  try {
+    while (audioQueue.length > 0) {
+      const buffer = audioQueue.shift()
+      await processAudioChunk(buffer)
+    }
+  } finally {
+    isProcessing = false
+  }
+}
+async function processAudioChunk(buffer) {
+  const wasRecording = isRecording
+  const isSpeech = await vad(buffer)
+  if (!wasRecording && !isSpeech) {
+    if (prevBuffers.length >= MAX_NUM_PREV_BUFFERS) {
+      prevBuffers.shift()
+    }
+    prevBuffers.push(buffer)
+    return
+  }
+  const remaining = BUFFER.length - bufferPointer
+  if (buffer.length >= remaining) {
+    BUFFER.set(buffer.subarray(0, remaining), bufferPointer)
+    bufferPointer += remaining
+    await dispatchForTranscription(buffer.subarray(remaining))
+    return
+  }
+  BUFFER.set(buffer, bufferPointer)
+  bufferPointer += buffer.length
+  if (isSpeech) {
+    if (!isRecording) {
+      self.postMessage({ type: "status", status: "recording", message: "Recording..." })
+    }
+    isRecording = true
+    postSpeechSamples = 0
+    return
+  }
+  postSpeechSamples += buffer.length
+  if (postSpeechSamples < MIN_SILENCE_DURATION_SAMPLES) {
+    return
+  }
+  if (bufferPointer < MIN_SPEECH_DURATION_SAMPLES) {
+    resetAfterRecording()
+    self.postMessage({ type: "status", status: "listening", message: "Listening..." })
+    return
+  }
+  await dispatchForTranscription()
+}
+// ============ Message Handler ============
+self.onmessage = async (event) => {
+  const { type, buffer } = event.data
+  switch (type) {
+    case "init":
+      try {
+        await loadModels()
+      } catch (err) {
+        console.error("[STT Worker] Init error:", err)
+        self.postMessage({ type: "error", message: err.toString() })
+      }
+      break
+    case "audio":
+      if (sileroVad && vadState) {
+        queueAudio(buffer)
+      }
+      break
+    case "stop":
+      if (bufferPointer > MIN_SPEECH_DURATION_SAMPLES) {
+        await dispatchForTranscription()
+      } else {
+        resetAfterRecording()
+      }
+      break
+  }
+}

vad-processor.js ADDED Viewed

	@@ -0,0 +1,40 @@

+/**
+ * VAD Audio Worklet Processor
+ * Captures audio chunks and sends them to the main thread for VAD processing
+ */
+const MIN_CHUNK_SIZE = 512
+let globalPointer = 0
+const globalBuffer = new Float32Array(MIN_CHUNK_SIZE)
+class VADProcessor extends AudioWorkletProcessor {
+  process(inputs, _outputs, _parameters) {
+    const buffer = inputs[0][0]
+    if (!buffer) return true // Keep alive even if no input
+    if (buffer.length >= MIN_CHUNK_SIZE) {
+      // Buffer is large enough, send directly
+      this.port.postMessage({ buffer: new Float32Array(buffer) })
+    } else {
+      const remaining = MIN_CHUNK_SIZE - globalPointer
+      if (buffer.length >= remaining) {
+        // Fill remaining space and send
+        globalBuffer.set(buffer.subarray(0, remaining), globalPointer)
+        this.port.postMessage({ buffer: new Float32Array(globalBuffer) })
+        // Reset and store overflow
+        globalBuffer.fill(0)
+        globalBuffer.set(buffer.subarray(remaining), 0)
+        globalPointer = buffer.length - remaining
+      } else {
+        // Accumulate
+        globalBuffer.set(buffer, globalPointer)
+        globalPointer += buffer.length
+      }
+    }
+    return true
+  }
+}
+registerProcessor("vad-processor", VADProcessor)

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+size 51712

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+size 51712

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+version https://git-lfs.github.com/spec/v1
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+size 51712

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+version https://git-lfs.github.com/spec/v1
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+size 51712