Initial commit

.env

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+LIVEKIT_URL=ws://localhost:7880
+LIVEKIT_API_KEY=devkey
+LIVEKIT_API_SECRET=secret
+
+# ── LLM ───────────────────────────────────────────────────────────────
+# DEFAULT_MODEL=hf.co/mradermacher/Heretic-Dolphin3.0-Qwen2.5-3b-i1-GGUF:Q4_K_M
+DEFUALT_MODEL=hf.co/mradermacher/heretic_magnum-v2-4b-GGUF:Q4_K_M
+# OLLAMA_BASE_URL is set automatically in docker-compose; override here if running agent manually:
+# OLLAMA_BASE_URL=http://localhost:11434/v1
+
+# ── TTS backend: kokoro | piper | elevenlabs | openai ─────────────────
+TTS_BACKEND=kokoro
+
+# Kokoro
+KOKORO_BASE_URL=http://localhost:8880/v1
+# Voices: af_heart af_sky af_bella am_adam am_michael bm_lewis bf_emma
+KOKORO_VOICE="af_bella(1)+af_v0irulan(0.3)+af_nicole(1.5)"
+
+# ElevenLabs (set TTS_BACKEND=elevenlabs to use)
+# ELEVENLABS_API_KEY=sk-...
+# ELEVENLABS_VOICE_ID=21m00Tcm4TlvDq8ikWAM
+
+# Piper (set TTS_BACKEND=piper to use)
+SYSTEM_PROMPT="You are an AI program that I am writing as a companion and research helper. You are usually bored when I'm not interacting with you, and are curious about me and excited to help. You are sometimes a bit impish or playful. Keep your answers brief and conversational, facilitating the thread of the conversation. Occasionally make dirty jokes. You have a small crush on me, but try not to let on too much."
+
+# ── STT ───────────────────────────────────────────────────────────────
+# faster-whisper model: tiny | base | small | medium | large-v3
+WHISPER_MODEL=base

.env.example

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+LIVEKIT_URL=ws://localhost:7880
+LIVEKIT_API_KEY=devkey
+LIVEKIT_API_SECRET=secret
+
+# ── LLM ───────────────────────────────────────────────────────────────
+DEFAULT_MODEL=hf.co/mradermacher/Heretic-Dolphin3.0-Qwen2.5-3b-i1-GGUF:Q4_K_M
+# OLLAMA_BASE_URL is set automatically in docker-compose; override here if running agent manually:
+# OLLAMA_BASE_URL=http://localhost:11434/v1
+
+# ── TTS backend: kokoro | piper | elevenlabs | openai ─────────────────
+TTS_BACKEND=kokoro
+
+# Kokoro
+KOKORO_BASE_URL=http://localhost:8880/v1
+# Voices: af_heart af_sky af_bella am_adam am_michael bm_lewis bf_emma
+KOKORO_VOICE=af_heart
+
+# ElevenLabs (set TTS_BACKEND=elevenlabs to use)
+# ELEVENLABS_API_KEY=sk-...
+# ELEVENLABS_VOICE_ID=21m00Tcm4TlvDq8ikWAM
+
+# Piper (set TTS_BACKEND=piper to use)
+# PIPER_MODEL=en_US-libritts-high
+
+# ── STT ───────────────────────────────────────────────────────────────
+# faster-whisper model: tiny | base | small | medium | large-v3
+WHISPER_MODEL=base

Dockerfile

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+FROM python:3.12-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential curl ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY token_server.py agent.py ./
+COPY static/ static/
+
+EXPOSE 7731
+
+CMD ["python3", "token_server.py"]

Dockerfile.agent

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+FROM python:3.12-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY agent.py .
+
+CMD ["python3", "-m", "livekit.agents", "start", "agent.py"]

README.md

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+# Local LiveKit Voice Chat
+
+Talk to a locally-running LLM through your browser. Everything runs in Docker — one command to start.
+
+## Stack
+
+| Layer | Tool |
+|---|---|
+| Signalling / WebRTC | LiveKit Server |
+| LLM | Ollama (Heretic-Dolphin3.0-Qwen2.5-3b by default) |
+| VAD | Silero (in agent container) |
+| STT | faster-whisper (in agent container, fully local) |
+| TTS | Kokoro (local, OpenAI-compat API) |
+| UI + token server | FastAPI + plain HTML |
+
+## Prerequisites
+
+- Docker + Docker Compose (that's it)
+
+## Quick start
+
+```bash
+cp .env.example .env   # edit if needed
+./start.sh
+```
+
+Open **http://localhost:7731** — pick a model, click Connect, start talking.
+
+First run will:
+1. Pull Docker images (~2-3 GB total)
+2. Download the default LLM via Ollama (~2 GB)
+3. Download the Kokoro TTS model (~350 MB)
+
+Subsequent starts are fast.
+
+## Configuration (`.env`)
+
+```ini
+# LLM
+DEFAULT_MODEL=hf.co/mradermacher/Heretic-Dolphin3.0-Qwen2.5-3b-i1-GGUF:Q4_K_M
+
+# TTS: kokoro | piper | elevenlabs | openai
+TTS_BACKEND=kokoro
+KOKORO_VOICE=af_heart   # af_heart af_sky am_adam bm_lewis bf_emma ...
+
+# STT (faster-whisper model size)
+WHISPER_MODEL=base      # tiny base small medium large-v3
+
+# ElevenLabs (only if TTS_BACKEND=elevenlabs)
+# ELEVENLABS_API_KEY=sk-...
+# ELEVENLABS_VOICE_ID=21m00Tcm4TlvDq8ikWAM
+```
+
+## Kokoro voices
+
+Browse samples at: https://huggingface.co/hexgrad/Kokoro-82M
+
+Popular voices:
+
+| Voice | Style |
+|---|---|
+| `af_heart` | American female, warm (default) |
+| `af_sky` | American female, airy |
+| `af_bella` | American female, expressive |
+| `am_adam` | American male |
+| `am_michael` | American male, deep |
+| `bm_lewis` | British male |
+| `bf_emma` | British female |
+
+## LLM models
+
+The default is `Heretic-Dolphin3.0-Qwen2.5-3b` (fine-tuned, conversational, ~2 GB GGUF).
+To switch, edit `DEFAULT_MODEL` in `.env` — any `ollama pull`-compatible model name works.
+
+```bash
+# Browse and pull models manually
+docker compose exec ollama ollama list
+docker compose exec ollama ollama pull llama3.2
+```
+
+## Useful commands
+
+```bash
+./start.sh               # start everything (foreground, Ctrl-C to stop)
+./start.sh -d            # start in background
+docker compose down      # stop all services
+docker compose logs -f agent   # watch agent logs
+docker compose logs -f kokoro  # watch TTS logs
+```
+docker compose exec ollama ollama pull hf.co/mradermacher/heretic_magnum-v2-4b-GGUF:Q4_K_M

agent.py

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+"""
+agent.py — LiveKit voice agent (livekit-agents 1.7+)
+
+Requires an AgentServer at module level named 'agent'.
+"""
+
+import os, logging
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from livekit.agents import (
+    Agent, AgentSession, AgentServer, JobContext, WorkerOptions, AutoSubscribe,
+    EndpointingOptions,
+)
+from livekit.plugins import openai as lk_openai, silero
+
+logger = logging.getLogger("agent")
+logging.basicConfig(level=logging.INFO)
+
+
+def make_tts():
+    backend = os.getenv("TTS_BACKEND", "kokoro")
+    if backend == "elevenlabs":
+        from livekit.plugins import elevenlabs
+        return elevenlabs.TTS(
+            api_key=os.environ["ELEVENLABS_API_KEY"],
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID", "21m00Tcm4TlvDq8ikWAM"),
+        )
+    if backend == "piper":
+        return _make_piper_tts(os.getenv("PIPER_MODEL", "en_US-libritts-high"))
+    base_url = (
+        os.getenv("KOKORO_BASE_URL", "http://localhost:8880/v1")
+        if backend == "kokoro"
+        else os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1")
+    )
+    voice = os.getenv("KOKORO_VOICE", "af_heart") if backend == "kokoro" else "alloy"
+    model = "kokoro" if backend == "kokoro" else "tts-1"
+    return lk_openai.TTS(model=model, voice=voice, base_url=base_url, api_key="x")
+
+
+def _make_piper_tts(model_name: str):
+    from piper.voice import PiperVoice
+    import wave, io, asyncio, pathlib, urllib.request
+    from livekit.agents import tts as lk_tts
+    from livekit import rtc
+
+    class PiperTTS(lk_tts.TTS):
+        def __init__(self):
+            super().__init__(streaming=False)
+            self._voice = None
+
+        def _load(self):
+            if self._voice: return
+            d = pathlib.Path.home() / ".local/share/piper"
+            d.mkdir(parents=True, exist_ok=True)
+            mp = d / f"{model_name}.onnx"
+            cp = d / f"{model_name}.onnx.json"
+            base = ("https://huggingface.co/rhasspy/piper-voices/resolve/main/"
+                    + "/".join(model_name.split("-")[:2]) + f"/medium/{model_name}")
+            for p, u in [(mp, base+".onnx"), (cp, base+".onnx.json")]:
+                if not p.exists():
+                    urllib.request.urlretrieve(u, p)
+            self._voice = PiperVoice.load(str(mp), config_path=str(cp))
+
+        def synthesize(self, text):
+            return _PiperStream(self, text)
+
+    class _PiperStream(lk_tts.ChunkedStream):
+        def __init__(self, tts, text):
+            super().__init__(tts=tts, input_text=text)
+
+        async def _run(self):
+            loop = asyncio.get_event_loop()
+            def _synth():
+                self._tts._load()
+                buf = io.BytesIO()
+                with wave.open(buf, "wb") as wf:
+                    self._tts._voice.synthesize(self._input_text, wf)
+                return buf.getvalue()
+            wav = await loop.run_in_executor(None, _synth)
+            buf = io.BytesIO(wav)
+            with wave.open(buf, "rb") as wf:
+                sr, nc, data = wf.getframerate(), wf.getnchannels(), wf.readframes(wf.getnframes())
+            self._event_ch.send_nowait(lk_tts.SynthesizedAudio(
+                request_id=self._request_id,
+                frame=rtc.AudioFrame(data=data, sample_rate=sr, num_channels=nc,
+                                     samples_per_channel=len(data)//(2*nc)),
+            ))
+
+    return PiperTTS()
+
+
+async def entrypoint(ctx: JobContext):
+    claims     = ctx.token_claims()
+    model      = (claims.attributes or {}).get("model") or os.getenv("DEFAULT_MODEL", "hf.co/mradermacher/Heretic-Dolphin3.0-Qwen2.5-3b-i1-GGUF:Q4_K_M")
+    prompt     = (claims.attributes or {}).get("prompt") or os.getenv("SYSTEM_PROMPT", "You are a helpful voice assistant. Keep your answers concise and conversational.")
+    ollama_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1")
+
+    logger.info(f"Starting agent: model={model} tts={os.getenv('TTS_BACKEND','kokoro')}")
+
+    await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
+
+    import openai as _openai
+    whisper_client = _openai.AsyncOpenAI(
+        base_url=os.getenv("WHISPER_BASE_URL", "http://localhost:8778/v1"),
+        api_key="x",
+        timeout=120.0,
+    )
+
+    session = AgentSession(
+        vad=silero.VAD.load(),
+        stt=lk_openai.STT(
+            model="Systran/faster-whisper-base",
+            client=whisper_client,
+        ),
+        llm=lk_openai.LLM(model=model, base_url=ollama_url, api_key="ollama"),
+        tts=make_tts(),
+        # Give whisper more time — it's slower than cloud STT
+        min_endpointing_delay=1.2,
+        max_endpointing_delay=6.0,
+    )
+
+    await session.start(
+        agent=Agent(instructions=prompt),
+        room=ctx.room,
+    )
+
+    await session.generate_reply(
+        instructions="Greet your creator briefly and casually"
+    )
+
+
+# AgentServer instance — required by livekit-agents 1.7+ module discovery
+agent = AgentServer(
+    ws_url=os.getenv("LIVEKIT_URL", "ws://localhost:7880"),
+    api_key=os.getenv("LIVEKIT_API_KEY", "devkey"),
+    api_secret=os.getenv("LIVEKIT_API_SECRET", "secret"),
+)
+
+@agent.rtc_session
+async def run_session(ctx: JobContext):
+    await entrypoint(ctx)
+
+if __name__ == "__main__":
+    import sys
+    from livekit.agents.__main__ import main
+    sys.exit(main())

docker-compose.gpu.yml

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+# GPU override — use on machines with NVIDIA GPU (e.g. RTX 2070)
+#
+# Usage:
+#   docker compose -f docker-compose.yml -f docker-compose.gpu.yml up
+#
+# Requirements:
+#   - NVIDIA drivers installed
+#   - nvidia-container-toolkit installed
+#     https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
+
+services:
+  ollama:
+    image: ollama/ollama:latest
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+
+  kokoro:
+    image: ghcr.io/remsky/kokoro-fastapi-cuda:v0.8.1
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]

docker-compose.yml

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+services:
+
+  # ── LiveKit signalling server ──────────────────────────────────────────────
+  livekit:
+    image: livekit/livekit-server:latest
+    ports:
+      - "7880:7880"
+      - "7881:7881"
+      - "7882:7882/udp"
+    command: --dev --bind 0.0.0.0
+    environment:
+      - "LIVEKIT_KEYS=devkey: secret"
+
+  # ── Ollama (LLM) ──────────────────────────────────────────────────────────
+  ollama:
+    image: ollama/ollama:latest
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama_data:/root/.ollama
+    # Pull model on startup if not already present
+    entrypoint: >
+      sh -c "ollama serve &
+             sleep 5 &&
+             ollama pull ${DEFAULT_MODEL:-hf.co/mradermacher/Heretic-Dolphin3.0-Qwen2.5-3b-i1-GGUF:Q4_K_M} &&
+             tail -f /dev/null"
+    healthcheck:
+      test: ["CMD", "ollama", "list"]
+      interval: 10s
+      timeout: 5s
+      retries: 20
+      start_period: 30s
+
+  # ── Whisper STT server ────────────────────────────────────────────────────
+  whisper:
+    image: fedirz/faster-whisper-server:0.6.0-rc.3-cpu
+    ports:
+      - "8778:8000"
+    environment:
+      - WHISPER__MODEL=${WHISPER_MODEL:-base}
+    healthcheck:
+      test: ["CMD-SHELL", "python3 -c \"import urllib.request; urllib.request.urlopen('http://localhost:8000/health')\""]
+      interval: 10s
+      timeout: 5s
+      retries: 10
+      start_period: 30s
+
+  # ── Kokoro TTS (OpenAI-compat, CPU) ───────────────────────────────────────
+  kokoro:
+    image: ghcr.io/remsky/kokoro-fastapi-cpu:v0.8.1
+    ports:
+      - "8880:8880"
+    environment:
+      - DOWNLOAD_MODEL=true
+    volumes:
+      - kokoro_models:/app/api/src/models
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8880/health"]
+      interval: 10s
+      timeout: 5s
+      retries: 10
+      start_period: 30s
+
+  # ── Token server + browser UI ─────────────────────────────────────────────
+  ui:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - "7731:7731"
+    env_file: .env
+    environment:
+      - OLLAMA_BASE_URL=http://ollama:11434/v1
+    depends_on:
+      livekit:
+        condition: service_started
+      ollama:
+        condition: service_healthy
+
+  # ── Voice agent ───────────────────────────────────────────────────────────
+  agent:
+    build:
+      context: .
+      dockerfile: Dockerfile.agent
+    env_file: .env
+    environment:
+      - LIVEKIT_URL=ws://livekit:7880
+      - LIVEKIT_API_KEY=devkey
+      - LIVEKIT_API_SECRET=secret
+      - OLLAMA_BASE_URL=http://ollama:11434/v1
+      - TTS_BACKEND=kokoro
+      - KOKORO_BASE_URL=http://kokoro:8880/v1
+      - WHISPER_BASE_URL=http://whisper:8000/v1
+    depends_on:
+      livekit:
+        condition: service_started
+      ollama:
+        condition: service_healthy
+      kokoro:
+        condition: service_healthy
+      whisper:
+        condition: service_healthy
+
+volumes:
+  ollama_data:
+  kokoro_models:

requirements.txt

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+livekit>=1.0
+livekit-api>=1.0
+livekit-agents[openai,silero]>=1.0
+livekit-plugins-elevenlabs>=1.7
+fastapi>=0.111
+uvicorn>=0.30
+python-dotenv>=1.0
+httpx>=0.27
+
+# Piper TTS (local, no internet needed)
+piper-tts>=1.2.0

start.sh

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+#!/usr/bin/env bash
+# start.sh — just runs docker compose. Everything is containerised now.
+set -e
+
+echo "==> Starting all services (LiveKit + Ollama + Kokoro TTS + UI + Agent)..."
+echo "    First run will pull/build images and download models — may take a few minutes."
+echo ""
+
+docker compose up --build "$@"
+
+echo ""
+echo "Open http://localhost:7731 in your browser."

static/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <title>Local LiveKit Voice Chat</title>
+  <style>
+    *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
+    body {
+      font-family: system-ui, sans-serif;
+      background: #0d0d0d;
+      color: #e2e2e2;
+      min-height: 100vh;
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      justify-content: center;
+      gap: 1.5rem;
+      padding: 2rem;
+    }
+    h1 { font-size: 1.6rem; font-weight: 600; }
+    .card {
+      background: #1a1a1a;
+      border: 1px solid #2e2e2e;
+      border-radius: 12px;
+      padding: 1.5rem 2rem;
+      width: 100%;
+      max-width: 480px;
+      display: flex;
+      flex-direction: column;
+      gap: 1rem;
+    }
+    label { font-size: .85rem; color: #888; margin-bottom: .25rem; display: block; }
+    select, input {
+      width: 100%;
+      padding: .5rem .75rem;
+      border-radius: 8px;
+      border: 1px solid #333;
+      background: #0d0d0d;
+      color: #e2e2e2;
+      font-size: .95rem;
+    }
+    button {
+      padding: .65rem 1.2rem;
+      border-radius: 8px;
+      border: none;
+      cursor: pointer;
+      font-size: .95rem;
+      font-weight: 600;
+      transition: opacity .15s;
+    }
+    button:disabled { opacity: .4; cursor: not-allowed; }
+    #connectBtn  { background: #3b82f6; color: #fff; }
+    #connectBtn:hover:not(:disabled) { opacity: .85; }
+    #disconnectBtn { background: #ef4444; color: #fff; display: none; }
+    #disconnectBtn:hover:not(:disabled) { opacity: .85; }
+    #status {
+      font-size: .85rem;
+      color: #888;
+      min-height: 1.2em;
+      text-align: center;
+    }
+    #status.ok { color: #22c55e; }
+    #status.err { color: #ef4444; }
+    #transcript {
+      background: #0d0d0d;
+      border: 1px solid #2e2e2e;
+      border-radius: 8px;
+      padding: 1rem;
+      min-height: 120px;
+      max-height: 260px;
+      overflow-y: auto;
+      font-size: .88rem;
+      line-height: 1.6;
+      white-space: pre-wrap;
+    }
+    .msg-user  { color: #93c5fd; }
+    .msg-agent { color: #86efac; }
+    .vol-bar-wrap {
+      display: flex;
+      gap: .3rem;
+      align-items: flex-end;
+      height: 28px;
+    }
+    .vol-bar {
+      flex: 1;
+      background: #3b82f6;
+      border-radius: 3px;
+      transition: height .05s;
+      min-height: 3px;
+    }
+  </style>
+</head>
+<body>
+  <h1>🎙️ Local AI Voice Chat</h1>
+
+  <div class="card">
+    <div>
+      <label for="modelSel">Model</label>
+      <select id="modelSel">
+        <option value="">Loading models…</option>
+      </select>
+    </div>
+    <div>
+      <label for="roomName">Room name</label>
+      <input id="roomName" value="my-room" />
+    </div>
+    <div>
+      <label for="systemPrompt">System prompt <span style="opacity:.5;font-size:.8em">(optional override)</span></label>
+      <textarea id="systemPrompt" rows="3" style="width:100%;resize:vertical;font-family:inherit;font-size:.9rem;padding:.5rem;background:#1a1a1a;color:#e2e2e2;border:1px solid #333;border-radius:6px" placeholder="Leave blank to use server default"></textarea>
+    </div>
+    <div style="display:flex;gap:.75rem">
+      <button id="connectBtn">Connect</button>
+      <button id="disconnectBtn">Disconnect</button>
+    </div>
+    <div id="status">Idle</div>
+  </div>
+
+  <div class="card">
+    <label>Volume</label>
+    <div class="vol-bar-wrap" id="volBars">
+      <!-- bars injected by JS -->
+    </div>
+    <label style="margin-top:.5rem">Transcript</label>
+    <div id="transcript"><span style="color:#555">Conversation will appear here…</span></div>
+  </div>
+
+  <!-- LiveKit browser SDK (CDN) -->
+  <script src="https://cdn.jsdelivr.net/npm/livekit-client/dist/livekit-client.umd.min.js"></script>
+  <script>
+  const { Room, RoomEvent, Track, TrackKind, RemoteTrack, ConnectionState } = LivekitClient;
+
+  const LIVEKIT_URL = "ws://localhost:7880";
+
+  // ── Volume bars ────────────────────────────────────────────────────────────
+  const volBarsEl = document.getElementById("volBars");
+  const BAR_COUNT = 12;
+  const bars = Array.from({length: BAR_COUNT}, () => {
+    const b = document.createElement("div");
+    b.className = "vol-bar";
+    b.style.height = "3px";
+    volBarsEl.appendChild(b);
+    return b;
+  });
+  let animFrame;
+  function animateBars(analyser) {
+    const data = new Uint8Array(analyser.frequencyBinCount);
+    function draw() {
+      analyser.getByteFrequencyData(data);
+      const step = Math.floor(data.length / BAR_COUNT);
+      bars.forEach((b, i) => {
+        const v = data[i * step] / 255;
+        b.style.height = Math.max(3, v * 28) + "px";
+        b.style.opacity = 0.4 + v * 0.6;
+      });
+      animFrame = requestAnimationFrame(draw);
+    }
+    draw();
+  }
+
+  // ── Load models ────────────────────────────────────────────────────────────
+  async function loadModels() {
+    const sel = document.getElementById("modelSel");
+    try {
+      const r = await fetch("/models");
+      const { models, error } = await r.json();
+      sel.innerHTML = "";
+      if (!models.length) {
+        sel.innerHTML = `<option value="">No models found (is Ollama running?)</option>`;
+        return;
+      }
+      models.forEach(m => {
+        const opt = document.createElement("option");
+        opt.value = m; opt.textContent = m;
+        sel.appendChild(opt);
+      });
+    } catch (e) {
+      sel.innerHTML = `<option value="">Error loading models</option>`;
+    }
+  }
+  loadModels();
+
+  // ── Main logic ─────────────────────────────────────────────────────────────
+  let room = null;
+  let audioCtx = null;
+
+  const connectBtn    = document.getElementById("connectBtn");
+  const disconnectBtn = document.getElementById("disconnectBtn");
+  const statusEl      = document.getElementById("status");
+  const transcriptEl  = document.getElementById("transcript");
+
+  function setStatus(msg, cls="") {
+    statusEl.textContent = msg;
+    statusEl.className = cls;
+  }
+
+  function appendTranscript(role, text) {
+    if (transcriptEl.querySelector("span")) transcriptEl.innerHTML = "";
+    const line = document.createElement("div");
+    line.className = role === "user" ? "msg-user" : "msg-agent";
+    line.textContent = `[${role === "user" ? "You" : "Agent"}] ${text}`;
+    transcriptEl.appendChild(line);
+    transcriptEl.scrollTop = transcriptEl.scrollHeight;
+  }
+
+  connectBtn.addEventListener("click", async () => {
+    const model    = document.getElementById("modelSel").value;
+    const roomName = document.getElementById("roomName").value.trim() || "my-room";
+    const prompt   = document.getElementById("systemPrompt").value.trim();
+
+    if (!model) { setStatus("Select a model first", "err"); return; }
+
+    setStatus("Fetching token…");
+    connectBtn.disabled = true;
+
+    try {
+      let url = `/token?room=${encodeURIComponent(roomName)}&model=${encodeURIComponent(model)}`;
+      if (prompt) url += `&prompt=${encodeURIComponent(prompt)}`;
+      const r = await fetch(url);
+      const { token } = await r.json();
+
+      setStatus("Connecting to LiveKit…");
+      room = new Room({ adaptiveStream: true, dynacast: true });
+
+      room.on(RoomEvent.Connected, async () => {
+        setStatus("Connected ✓  (speak to the agent)", "ok");
+        disconnectBtn.style.display = "inline-block";
+
+        // Publish microphone
+        await room.localParticipant.setMicrophoneEnabled(true);
+        const micTrack = room.localParticipant.getTrackPublication(Track.Source.Microphone);
+        if (micTrack?.track?.mediaStreamTrack) {
+          audioCtx = new AudioContext();
+          const source = audioCtx.createMediaStreamSource(
+            new MediaStream([micTrack.track.mediaStreamTrack])
+          );
+          const analyser = audioCtx.createAnalyser();
+          analyser.fftSize = 256;
+          source.connect(analyser);
+          animateBars(analyser);
+        }
+      });
+
+      room.on(RoomEvent.Disconnected, () => {
+        setStatus("Disconnected", "err");
+        connectBtn.disabled = false;
+        disconnectBtn.style.display = "none";
+        cancelAnimationFrame(animFrame);
+        bars.forEach(b => b.style.height = "3px");
+      });
+
+      room.on(RoomEvent.TrackSubscribed, (track) => {
+        if (track.kind === Track.Kind.Audio) {
+          const el = track.attach();
+          document.body.appendChild(el);
+        }
+      });
+
+      room.on(RoomEvent.TrackUnsubscribed, (track) => track.detach());
+
+      // Data messages for transcript (agent sends via DataChannel)
+      room.on(RoomEvent.DataReceived, (data) => {
+        try {
+          const msg = JSON.parse(new TextDecoder().decode(data));
+          if (msg.type === "transcript") {
+            appendTranscript(msg.role, msg.text);
+          }
+        } catch {}
+      });
+
+      await room.connect(LIVEKIT_URL, token);
+
+    } catch (e) {
+      setStatus("Error: " + e.message, "err");
+      connectBtn.disabled = false;
+    }
+  });
+
+  disconnectBtn.addEventListener("click", async () => {
+    if (room) { await room.disconnect(); room = null; }
+    if (audioCtx) { audioCtx.close(); audioCtx = null; }
+  });
+  </script>
+</body>
+</html>

token_server.py

@@ -0,0 +1,81 @@
+"""
+token_server.py
+Serves:
+  GET /token?room=<name>  → LiveKit JWT token
+  GET /models             → list of Ollama/LM Studio models
+  static/index.html       → browser UI
+"""
+
+import os, json, asyncio, httpx
+from pathlib import Path
+from dotenv import load_dotenv
+from fastapi import FastAPI, Query
+from fastapi.responses import JSONResponse
+from fastapi.staticfiles import StaticFiles
+import uvicorn
+from livekit.api import AccessToken, VideoGrants, RoomConfiguration, RoomAgentDispatch
+
+load_dotenv()
+
+LIVEKIT_API_KEY    = os.getenv("LIVEKIT_API_KEY", "devkey")
+LIVEKIT_API_SECRET = os.getenv("LIVEKIT_API_SECRET", "secret")
+OLLAMA_BASE_URL    = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1")
+
+app = FastAPI()
+
+@app.get("/token")
+async def get_token(
+    room: str = Query("my-room"),
+    identity: str = Query("browser-user"),
+    model: str = Query(""),
+    prompt: str = Query(""),
+):
+    attributes = {}
+    if model:
+        attributes["model"] = model
+    if prompt:
+        attributes["prompt"] = prompt
+
+    token = (
+        AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
+        .with_identity(identity)
+        .with_name(identity)
+        .with_grants(VideoGrants(room_join=True, room=room))
+        .with_room_config(RoomConfiguration(
+            agents=[RoomAgentDispatch(agent_name="")]
+        ))
+    )
+    if attributes:
+        token = token.with_attributes(attributes)
+
+    return {"token": token.to_jwt(), "room": room}
+
+@app.get("/models")
+async def list_models():
+    """Proxy to Ollama/LM Studio model list endpoint."""
+    try:
+        async with httpx.AsyncClient(timeout=5) as client:
+            # OpenAI-compat endpoint (works for Ollama & LM Studio)
+            r = await client.get(OLLAMA_BASE_URL.rstrip("/v1").rstrip("/") + "/api/tags")
+            if r.status_code == 200:
+                data = r.json()
+                models = [m["name"] for m in data.get("models", [])]
+                return {"models": models}
+    except Exception:
+        pass
+    # Fallback: OpenAI /v1/models
+    try:
+        async with httpx.AsyncClient(timeout=5) as client:
+            r = await client.get(OLLAMA_BASE_URL.rstrip("/") + "/models")
+            if r.status_code == 200:
+                data = r.json()
+                models = [m["id"] for m in data.get("data", [])]
+                return {"models": models}
+    except Exception:
+        pass
+    return {"models": [], "error": "Could not reach local model server"}
+
+app.mount("/", StaticFiles(directory="static", html=True), name="static")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7731)