feat: VoiceCoach v2.1 - GPU local, Ollama LLM, settings panel, token server separation

- pipeline.py: switched LLM from OpenClaw/Claude to local Ollama, added settings loading, sentence aggregator, async SpeechBrain, tuned VAD
- main.py: simplified (no more token server thread, just pipeline)
- token_server.py: NEW - standalone token + settings API server
- docker-compose.yml: all services on GPU machine, added token-server service, CUDA speaches, Ollama LLM
- web-client/index.html: added settings panel with sliders
- web-client/app.js: settings management, localStorage persistence, push to server
- web-client/style.css: settings panel styles
- web-client/nginx.conf: proxy to token-server instead of pipecat-agent
- patch_livekit.py: updated with proper indentation-safe patching

docker-compose.yml

@@ -1,6 +1,6 @@
 services:
 
-  # LiveKit WebRTC Server (unchanged)
+  # LiveKit WebRTC Server
   livekit-server:
     image: livekit/livekit-server:latest
     container_name: voicecoach-livekit
@@ -27,24 +27,27 @@ services:
         max-size: "50m"
         max-file: "3"
 
-  # Speaches: self-hosted Whisper STT + Kokoro TTS (v2 - replaces Piper + in-process Whisper)
+  # Speaches: GPU-accelerated Whisper STT + Kokoro TTS
   speaches:
-    image: ghcr.io/speaches-ai/speaches:latest-cpu
+    image: ghcr.io/speaches-ai/speaches:latest-cuda
-    container_name: voicecoach-speaches
+    container_name: voicecoach-speaches-gpu
     restart: unless-stopped
     environment:
-      - WHISPER__MODEL=Systran/faster-whisper-large-v3-turbo
+      - WHISPER__INFERENCE_DEVICE=cuda
-      - WHISPER__INFERENCE_DEVICE=cpu
+      - WHISPER__COMPUTE_TYPE=float16
-      - WHISPER__COMPUTE_TYPE=int8
+      - WHISPER__TTL=-1
-      - TTS__MODEL=kokoro
-      - TTS__DEFAULT_VOICE=af_nova
-      - TTS__LANGUAGE=en-us
     volumes:
-      - /mnt/docker-data/configs/voicecoach/speaches-models:/root/.cache
+      - speaches-models:/home/ubuntu/.cache
     networks:
       - voicecoach
-    mem_limit: 3G
+    deploy:
-    memswap_limit: 3G
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    mem_limit: 4G
     labels:
       - "com.centurylinklabs.watchtower.enable=false"
     healthcheck:
@@ -52,29 +55,28 @@ services:
       interval: 30s
       timeout: 10s
       retries: 3
-      start_period: 120s
+      start_period: 180s
     logging:
       driver: "json-file"
       options:
         max-size: "50m"
         max-file: "3"
 
-  # SpeechBrain Emotion Analysis (unchanged, internal only)
+  # SpeechBrain Emotion Analysis
   speechbrain-service:
+    image: voicecoach-speechbrain:latest
     build:
       context: ./speechbrain-service
-    image: voicecoach-speechbrain:latest
     container_name: voicecoach-speechbrain
     restart: unless-stopped
     volumes:
-      - /mnt/docker-data/configs/voicecoach/speechbrain-models:/app/models
+      - speechbrain-models:/app/models
-      - /mnt/docker-data/configs/voicecoach/clips:/data/clips
+      - voicecoach-clips:/data/clips
     environment:
       - MODEL_DIR=/app/models
     networks:
       - voicecoach
     mem_limit: 3G
-    memswap_limit: 3G
     labels:
       - "com.centurylinklabs.watchtower.enable=false"
     logging:
@@ -83,7 +85,7 @@ services:
         max-size: "50m"
         max-file: "3"
 
-  # OpenClaw VoiceCoach Agent (v2 - LLM gateway with memory/skills)
+  # OpenClaw VoiceCoach Agent (LLM gateway -> Claude)
   voicecoach-openclaw:
     image: margaret-openclaw:latest
     container_name: voicecoach-openclaw
@@ -97,12 +99,11 @@ services:
       - OPENCLAW_STATE_DIR=/home/glyptic/.voicecoach
       - OPENCLAW_CONFIG_PATH=/home/glyptic/.voicecoach/openclaw.json
     volumes:
-      - /mnt/infrastructure/voicecoach:/home/glyptic/.voicecoach
+      - ./voicecoach:/home/glyptic/.voicecoach
     command: ["sh", "-c", "mkdir -p /home/glyptic && node --max-old-space-size=1500 /usr/local/lib/node_modules/openclaw/dist/index.js gateway --allow-unconfigured"]
     networks:
       - voicecoach
     mem_limit: 2G
-    memswap_limit: 2G
     labels:
       - "com.centurylinklabs.watchtower.enable=false"
     logging:
@@ -111,11 +112,11 @@ services:
         max-size: "50m"
         max-file: "3"
 
-  # Pipecat Voice Pipeline Agent (v2 - uses Speaches + OpenClaw gateway)
+  # Pipecat Voice Pipeline Agent
   pipecat-agent:
+    image: voicecoach-pipecat:latest
     build:
       context: ./pipecat-agent
-    image: voicecoach-pipecat:latest
     container_name: voicecoach-pipecat
     restart: unless-stopped
     env_file:
@@ -125,22 +126,22 @@ services:
       - LIVEKIT_PUBLIC_URL=wss://voice.hobbs.farm
       - SPEACHES_URL=http://speaches:8000
       - SPEECHBRAIN_URL=http://speechbrain-service:8001
-      - OPENCLAW_URL=http://voicecoach-openclaw:3100
+      - LLM_BASE_URL=http://host.docker.internal:11434/v1
-      - OPENCLAW_AGENT_ID=voicecoach
+      - LLM_MODEL=llama3.1:8b
-      - WHISPER_MODEL=Systran/faster-whisper-large-v3-turbo
+      - WHISPER_MODEL=deepdml/faster-whisper-large-v3-turbo-ct2
       - KOKORO_VOICE=af_nova
       - CLIPS_DIR=/data/clips/pending
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
     volumes:
-      - /mnt/docker-data/configs/voicecoach/clips:/data/clips
+      - voicecoach-clips:/data/clips
     depends_on:
       - livekit-server
       - speaches
       - speechbrain-service
-      - voicecoach-openclaw
     networks:
       - voicecoach
     mem_limit: 1G
-    memswap_limit: 1G
     labels:
       - "com.centurylinklabs.watchtower.enable=false"
     logging:
@@ -149,17 +150,47 @@ services:
         max-size: "50m"
         max-file: "3"
 
-  # Web Client (unchanged)
+  # Token Server + Settings API (separate from pipeline so it never goes down)
+  token-server:
+    image: voicecoach-pipecat:latest
+    container_name: voicecoach-token-server
+    restart: unless-stopped
+    env_file:
+      - .env
+    environment:
+      - LIVEKIT_PUBLIC_URL=wss://voice.hobbs.farm
+      - SETTINGS_PATH=/data/settings.json
+    command: ["python", "token_server.py"]
+    volumes:
+      - voicecoach-settings:/data
+    networks:
+      - voicecoach
+    mem_limit: 256M
+    healthcheck:
+      test: ["CMD", "python", "-c", "import httpx; httpx.get('http://localhost:8080/health').raise_for_status()"]
+      interval: 15s
+      timeout: 5s
+      retries: 3
+      start_period: 10s
+    labels:
+      - "com.centurylinklabs.watchtower.enable=false"
+    logging:
+      driver: "json-file"
+      options:
+        max-size: "10m"
+        max-file: "3"
+
+  # Web Client
   voicecoach-web:
+    image: voicecoach-web:latest
     build:
       context: ./web-client
-    image: voicecoach-web:latest
     container_name: voicecoach-web
     restart: unless-stopped
     ports:
       - "8085:80"
     depends_on:
-      - pipecat-agent
+      - token-server
     networks:
       - voicecoach
     mem_limit: 128M
@@ -174,3 +205,9 @@ services:
 networks:
   voicecoach:
     driver: bridge
+
+volumes:
+  speaches-models:
+  speechbrain-models:
+  voicecoach-clips:
+  voicecoach-settings:

pipecat-agent/Dockerfile

@@ -6,6 +6,11 @@ RUN apt-get update && apt-get install -y libsndfile1 ffmpeg && \
 WORKDIR /app
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
+
+# Patch pipecat LiveKit transport bugs
+COPY patch_livekit.py /tmp/patch_livekit.py
+RUN python3 /tmp/patch_livekit.py
+
 COPY . .
 
 HEALTHCHECK --interval=30s --timeout=10s --start-period=30s \

pipecat-agent/main.py

@@ -1,18 +1,15 @@
 """
-VoiceCoach pipeline entry point -- v2.
+VoiceCoach pipeline entry point.
-Runs token server + pipeline with unique identity per session.
+Token server runs as a separate container — this file only runs the pipeline.
+Pipeline exits on session end; Docker restarts the container for a fresh state.
 """
 import asyncio
 import logging
 import os
-import threading
 import uuid
-from pathlib import Path
+from datetime import timedelta
 
 import structlog
-import uvicorn
-from fastapi import FastAPI
-from pydantic import BaseModel
 from livekit import api as livekit_api
 
 from pipeline import run_voice_coach_pipeline
@@ -23,68 +20,23 @@ logger = structlog.get_logger()
 LIVEKIT_API_KEY = os.environ["LIVEKIT_API_KEY"]
 LIVEKIT_API_SECRET = os.environ["LIVEKIT_API_SECRET"]
 LIVEKIT_WS_URL = os.getenv("LIVEKIT_URL", "ws://livekit-server:7880")
-LIVEKIT_PUBLIC_URL = os.getenv("LIVEKIT_PUBLIC_URL", "wss://voice.hobbs.farm")
-OPENCLAW_TOKEN = os.getenv("OPENCLAW_TOKEN", "")
 
-# --- Token HTTP server ---
-
-token_app = FastAPI(title="VoiceCoach Token Service")
-
-
-class TokenRequest(BaseModel):
-    identity: str = "jeremy"
-
-
-class TokenResponse(BaseModel):
-    token: str
-    url: str
-
-
-@token_app.get("/health")
-async def health():
-    return {"status": "ok", "version": "2.0"}
-
-
-@token_app.post("/token")
-async def get_token(req: TokenRequest):
-    token = livekit_api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
-    token.with_grants(livekit_api.VideoGrants(
-        room_join=True, room="voicecoach",
-        can_publish=True, can_subscribe=True,
-    ))
-    token.identity = req.identity
-    return TokenResponse(token=token.to_jwt(), url=LIVEKIT_PUBLIC_URL)
-
-
-def run_token_server():
-    uvicorn.run(token_app, host="0.0.0.0", port=8080, log_level="info")
-
-
-# --- Main ---
 
 async def main():
-    server_thread = threading.Thread(target=run_token_server, daemon=True)
-    server_thread.start()
-    logger.info("Token server started on :8080")
-
-    while True:
     agent_identity = f"voicecoach-agent-{uuid.uuid4().hex[:8]}"
     token = livekit_api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
     token.with_grants(livekit_api.VideoGrants(room_join=True, room="voicecoach"))
     token.identity = agent_identity
+    token.ttl = timedelta(hours=24)
     livekit_token = token.to_jwt()
 
-        logger.info("Starting VoiceCoach v2 pipeline", identity=agent_identity)
+    logger.info("Starting VoiceCoach pipeline", identity=agent_identity)
-        try:
     await run_voice_coach_pipeline(
         livekit_url=LIVEKIT_WS_URL,
         livekit_token=livekit_token,
-                openclaw_token=OPENCLAW_TOKEN,
+        openclaw_token="",
     )
-        except Exception as e:
+    logger.info("Pipeline exited")
-            logger.error("Pipeline error, restarting in 35s", error=str(e))
-
-        await asyncio.sleep(35)
 
 
 if __name__ == "__main__":

pipecat-agent/patch_livekit.py

@@ -0,0 +1,58 @@
+"""Patch pipecat LiveKit transport bugs at build time."""
+import pipecat.transports.services.livekit as lk_mod
+
+lk_path = lk_mod.__file__
+print(f"Patching {lk_path}")
+content = open(lk_path).read()
+
+# FIX 1: Remove direct push_frame(pipecat_audio_frame) that bypasses VAD
+old1 = "await self.push_frame(pipecat_audio_frame)"
+if old1 in content:
+    content = content.replace(old1, "pass  # PATCHED: removed VAD bypass")
+    print("FIX 1: Removed push_frame(pipecat_audio_frame) VAD bypass")
+
+# FIX 2: Wrap the on_connected call in connect() method with try/except
+# The line is: "            await self._callbacks.on_connected()" (12 spaces)
+# We replace it with a try/except at the same indentation level
+old2 = "            await self._callbacks.on_connected()\n"
+new2 = (
+    "            try:\n"
+    "                await self._callbacks.on_connected()\n"
+    "            except Exception:\n"
+    "                pass  # PATCHED: suppress duplicate callback\n"
+)
+if old2 in content:
+    content = content.replace(old2, new2)
+    print("FIX 2: Wrapped on_connected with try/except")
+
+open(lk_path, "w").write(content)
+
+# Verify syntax
+import py_compile
+try:
+    py_compile.compile(lk_path, doraise=True)
+    print("Syntax check: PASS")
+except py_compile.PyCompileError as e:
+    print(f"Syntax check: FAIL - {e}")
+    # Show the problematic area
+    lines = open(lk_path).readlines()
+    for i in range(130, min(145, len(lines))):
+        print(f"  {i+1}: {lines[i]}", end="")
+
+# FIX 3: Guard set_result on already-resolved Future in livekit SDK
+try:
+    import livekit.rtc.room as room_mod
+    room_path = room_mod.__file__
+    room_content = open(room_path).read()
+    if ("self._first_sid_future.set_result" in room_content and
+            ".done()" not in room_content.split("_first_sid_future")[1][:200]):
+        room_content = room_content.replace(
+            "self._first_sid_future.set_result",
+            "not self._first_sid_future.done() and self._first_sid_future.set_result"
+        )
+        open(room_path, "w").write(room_content)
+        print(f"FIX 3: Patched {room_path}")
+except Exception as e:
+    print(f"FIX 3: Skipped: {e}")
+
+print("All patches complete")

pipecat-agent/pipeline.py

@@ -1,32 +1,39 @@
 """
-Pipecat pipeline -- v2 fully self-hosted.
+Pipecat pipeline -- v2.1 optimized for low latency.
 
-STT:  Speaches (faster-whisper) via OpenAI-compatible /v1/audio/transcriptions
+STT:  Speaches (faster-whisper) via /v1/audio/transcriptions (utterance-level)
-LLM:  OpenClaw VoiceCoach agent via OpenAI-compatible gateway
+LLM:  OpenClaw VoiceCoach agent via OpenAI-compatible gateway (streaming)
-TTS:  Speaches (Kokoro 82M) via OpenAI-compatible /v1/audio/speech
+TTS:  Speaches (Kokoro 82M) via OpenAI-compatible /v1/audio/speech (sentence-level)
+
+Optimizations over v2.0:
+- Sentence aggregator between LLM and TTS (stream first sentence while LLM generates rest)
+- SpeechBrain runs async in background (non-blocking, one-turn-behind vocal state)
+- VAD stop_secs reduced from 0.8 to 0.4
+- Whisper model default changed to large-v3-turbo (faster per-token on CPU/GPU)
 """
 import asyncio
 import io
 import json
 import logging
 import os
+import re
 import uuid
 import wave
 from datetime import datetime
 from pathlib import Path
-from typing import AsyncGenerator, Optional
+from typing import Optional
 
 import httpx
 from pipecat.frames.frames import (
-    AudioRawFrame, EndFrame, ErrorFrame, Frame, TextFrame, TranscriptionFrame,
+    AudioRawFrame, EndFrame, TextFrame, TranscriptionFrame,
-    UserStartedSpeakingFrame, UserStoppedSpeakingFrame,
+    TTSSpeakFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame,
+    ErrorFrame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameProcessor, FrameDirection
-from pipecat.services.ai_services import STTService
 from pipecat.services.openai import OpenAILLMService, OpenAITTSService
 from pipecat.transports.services.livekit import LiveKitTransport, LiveKitParams
 
@@ -41,78 +48,130 @@ from prompt_builder import build_user_message, VocalState
 
 logger = logging.getLogger(__name__)
 
+
+# ---------------------------------------------------------------------------
+# SpeachesTTSService — Kokoro TTS via Speaches OpenAI-compatible API
+# ---------------------------------------------------------------------------
+
+class SpeachesTTSService(OpenAITTSService):
+    """OpenAITTSService subclass that properly uses base_url and bypasses
+    the hardcoded VALID_VOICES dict for Kokoro voice IDs."""
+
+    def __init__(self, *, api_key=None, base_url=None, voice="alloy",
+                 model="tts-1", sample_rate=24000, **kwargs):
+        from pipecat.services.ai_services import TTSService
+        TTSService.__init__(self, sample_rate=sample_rate, **kwargs)
+        self._settings = {"sample_rate": sample_rate}
+        self.set_model_name(model)
+        self.set_voice(voice)
+        from openai import AsyncOpenAI
+        self._client = AsyncOpenAI(api_key=api_key, base_url=base_url)
+
+    async def run_tts(self, text: str):
+        logger.debug("TTS: [%s]", text[:80])
+        try:
+            await self.start_ttfb_metrics()
+            async with self._client.audio.speech.with_streaming_response.create(
+                input=text or " ",
+                model=self.model_name,
+                voice=self._voice_id,
+                response_format="pcm",
+            ) as r:
+                if r.status_code != 200:
+                    error = await r.text()
+                    logger.error("TTS error: %s %s", r.status_code, error)
+                    yield ErrorFrame(f"TTS error: {r.status_code}")
+                    return
+                await self.start_tts_usage_metrics(text)
+                yield TTSStartedFrame()
+                async for chunk in r.iter_bytes(8192):
+                    if chunk:
+                        await self.stop_ttfb_metrics()
+                        yield TTSAudioRawFrame(chunk, 24000, 1)
+                yield TTSStoppedFrame()
+        except Exception as e:
+            logger.exception("TTS error: %s", e)
+
+
+# ---------------------------------------------------------------------------
+# Sentence Aggregator — buffers LLM streaming tokens into sentences for TTS
+# ---------------------------------------------------------------------------
+
+SENTENCE_END = re.compile(r'[.!?]\s')
+
+
+class SentenceAggregator(FrameProcessor):
+    """Buffers streaming TextFrames from LLM and emits one TextFrame per
+    complete sentence. This lets TTS start on the first sentence while
+    the LLM is still generating the rest."""
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._buffer = ""
+
+    async def process_frame(self, frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TextFrame):
+            self._buffer += frame.text
+            while True:
+                match = SENTENCE_END.search(self._buffer)
+                if match:
+                    sentence = self._buffer[:match.end()].strip()
+                    self._buffer = self._buffer[match.end():]
+                    if sentence:
+                        logger.info("Sentence -> TTS: %s", sentence[:80])
+                        await self.push_frame(TextFrame(text=sentence), direction)
+                else:
+                    break
+        elif isinstance(frame, EndFrame):
+            if self._buffer.strip():
+                logger.info("Flush -> TTS: %s", self._buffer.strip()[:80])
+                await self.push_frame(TextFrame(text=self._buffer.strip()), direction)
+            self._buffer = ""
+            await self.push_frame(frame, direction)
+        else:
+            # Flush on any non-text frame (LLM response end, etc.)
+            if self._buffer.strip() and not isinstance(frame, AudioRawFrame):
+                logger.info("Flush -> TTS: %s", self._buffer.strip()[:80])
+                await self.push_frame(TextFrame(text=self._buffer.strip()), direction)
+                self._buffer = ""
+            await self.push_frame(frame, direction)
+
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
 SPEACHES_URL = os.getenv("SPEACHES_URL", "http://speaches:8000")
-OPENCLAW_URL = os.getenv("OPENCLAW_URL", "http://voicecoach-openclaw:3100")
+LLM_BASE_URL = os.getenv("LLM_BASE_URL", "http://host.docker.internal:11434/v1")
+LLM_MODEL = os.getenv("LLM_MODEL", "llama3.1:8b")
 SPEECHBRAIN_URL = os.getenv("SPEECHBRAIN_URL", "http://speechbrain-service:8001")
 
-WHISPER_MODEL = os.getenv("WHISPER_MODEL", "Systran/faster-whisper-large-v3-turbo")
+WHISPER_MODEL = os.getenv("WHISPER_MODEL", "deepdml/faster-whisper-large-v3-turbo-ct2")
+KOKORO_MODEL = os.getenv("KOKORO_MODEL", "speaches-ai/Kokoro-82M-v1.0-ONNX-fp16")
 KOKORO_VOICE = os.getenv("KOKORO_VOICE", "af_nova")
-OPENCLAW_AGENT_ID = os.getenv("OPENCLAW_AGENT_ID", "voicecoach")
 
 CLIPS_DIR = Path(os.getenv("CLIPS_DIR", "/data/clips/pending"))
 UNCERTAINTY_THRESHOLD = 0.60
 MIN_CLIP_DURATION = 1.0
 
 
-class SpeachesSTTService(STTService):
+# ---------------------------------------------------------------------------
-    """STT via Speaches OpenAI-compatible /v1/audio/transcriptions endpoint.
+# SpeachesSTTProcessor — buffers full utterance, sends once to Speaches
+# ---------------------------------------------------------------------------
 
-    pipecat-ai 0.0.48 does not include OpenAISTTService; this thin wrapper
+class SpeachesSTTProcessor(FrameProcessor):
-    calls the Speaches HTTP API directly using the STTService base class.
+    """Buffers audio during speech, sends complete utterance to Speaches STT."""
-    """
 
     def __init__(self, base_url: str, model: str, language: str = "en", **kwargs):
         super().__init__(**kwargs)
         self._base_url = base_url.rstrip("/")
         self._model = model
         self._language = language
-        self._http = httpx.AsyncClient(timeout=30.0)
+        self._http = httpx.AsyncClient(timeout=60.0)
-
-    async def set_model(self, model: str):
-        self._model = model
-
-    async def set_language(self, language):
-        self._language = str(language)
-
-    async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
-        if not audio:
-            return
-        # Wrap raw PCM in a WAV container for the API
-        buf = io.BytesIO()
-        with wave.open(buf, "wb") as wf:
-            wf.setnchannels(1)
-            wf.setsampwidth(2)
-            wf.setframerate(16000)
-            wf.writeframes(audio)
-        wav_bytes = buf.getvalue()
-
-        try:
-            r = await self._http.post(
-                f"{self._base_url}/v1/audio/transcriptions",
-                files={"file": ("audio.wav", wav_bytes, "audio/wav")},
-                data={"model": self._model, "language": self._language, "response_format": "json"},
-            )
-            r.raise_for_status()
-            text = r.json().get("text", "").strip()
-            if text:
-                logger.info("STT transcription: %s", text[:100])
-                yield TranscriptionFrame(text=text, user_id="user", timestamp=datetime.utcnow().isoformat())
-        except Exception as e:
-            logger.warning("Speaches STT error: %s", e)
-            yield ErrorFrame(error=str(e))
-
-
-class VocalStateInjector(FrameProcessor):
-    """Buffers audio per utterance, sends to SpeechBrain for analysis,
-    enriches TranscriptionFrame with [VOCAL_STATE] metadata,
-    and saves clips for active labeling."""
-
-    def __init__(self, session_id: str, **kwargs):
-        super().__init__(**kwargs)
-        self._session_id = session_id
         self._audio_buffer: list[bytes] = []
         self._is_speaking = False
-        self._http = httpx.AsyncClient(timeout=3.0)
 
     async def process_frame(self, frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
@@ -120,34 +179,113 @@ class VocalStateInjector(FrameProcessor):
         if isinstance(frame, UserStartedSpeakingFrame):
             self._is_speaking = True
             self._audio_buffer = []
-            logger.info("VAD: user started speaking")
+            logger.info("STT: user started speaking")
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, AudioRawFrame):
+            if self._is_speaking:
+                self._audio_buffer.append(frame.audio)
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, UserStoppedSpeakingFrame):
+            self._is_speaking = False
+            logger.info("STT: user stopped, %d chunks", len(self._audio_buffer))
+            if self._audio_buffer:
+                audio_bytes = b"".join(self._audio_buffer)
+                self._audio_buffer = []
+                buf = io.BytesIO()
+                with wave.open(buf, "wb") as wf:
+                    wf.setnchannels(1)
+                    wf.setsampwidth(2)
+                    wf.setframerate(16000)
+                    wf.writeframes(audio_bytes)
+                wav_bytes = buf.getvalue()
+                try:
+                    logger.info("STT: sending %d bytes...", len(wav_bytes))
+                    r = await self._http.post(
+                        f"{self._base_url}/v1/audio/transcriptions",
+                        files={"file": ("utterance.wav", wav_bytes, "audio/wav")},
+                        data={
+                            "model": self._model,
+                            "language": self._language,
+                            "response_format": "json",
+                            "vad_filter": "false",
+                        },
+                    )
+                    r.raise_for_status()
+                    text = r.json().get("text", "").strip()
+                    if text:
+                        logger.info("STT: %r", text)
+                        await self.push_frame(
+                            TranscriptionFrame(text=text, user_id="jeremy",
+                                               timestamp=datetime.utcnow().isoformat()),
+                            direction,
+                        )
+                    else:
+                        logger.info("STT: empty transcription")
+                except Exception as e:
+                    logger.warning("STT error: %s", e)
+            await self.push_frame(frame, direction)
+        else:
+            await self.push_frame(frame, direction)
+
+
+# ---------------------------------------------------------------------------
+# VocalStateInjector — async SpeechBrain (non-blocking, one-turn-behind)
+# ---------------------------------------------------------------------------
+
+class VocalStateInjector(FrameProcessor):
+    """Enriches TranscriptionFrame with [VOCAL_STATE] from SpeechBrain.
+    Analysis runs in background — uses previous turn's result to avoid
+    blocking the critical path."""
+
+    def __init__(self, session_id: str, **kwargs):
+        super().__init__(**kwargs)
+        self._session_id = session_id
+        self._audio_buffer: list[bytes] = []
+        self._is_speaking = False
+        self._http = httpx.AsyncClient(timeout=3.0)
+        self._last_vocal_state: Optional[VocalState] = None
+
+    async def process_frame(self, frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserStartedSpeakingFrame):
+            self._is_speaking = True
+            self._audio_buffer = []
             await self.push_frame(frame, direction)
         elif isinstance(frame, AudioRawFrame) and self._is_speaking:
             self._audio_buffer.append(frame.audio)
             await self.push_frame(frame, direction)
         elif isinstance(frame, UserStoppedSpeakingFrame):
             self._is_speaking = False
-            logger.info("VAD: user stopped speaking, buffer: %d chunks", len(self._audio_buffer))
             await self.push_frame(frame, direction)
         elif isinstance(frame, TranscriptionFrame):
-            wav_bytes = self._flush_audio_to_wav()
+            # Use PREVIOUS turn's vocal state (non-blocking)
-            self._audio_buffer = []
+            vocal_state = self._last_vocal_state
-
-            vocal_state = None
-            if wav_bytes:
-                vocal_state, analysis = await self._analyze(wav_bytes)
-                if analysis and self._clip_worth_saving(analysis, wav_bytes):
-                    await self._save_clip(wav_bytes, analysis)
-
             enriched_text = build_user_message(frame.text, vocal_state)
-            logger.info("Enriched transcription: %s", enriched_text[:200])
+            logger.info("-> LLM: %s", enriched_text[:120])
             await self.push_frame(
-                TranscriptionFrame(text=enriched_text, user_id=frame.user_id, timestamp=frame.timestamp),
+                TranscriptionFrame(text=enriched_text, user_id=frame.user_id,
+                                   timestamp=frame.timestamp),
                 direction,
             )
+            # Fire background analysis for NEXT turn
+            wav_bytes = self._flush_audio_to_wav()
+            self._audio_buffer = []
+            if wav_bytes:
+                asyncio.create_task(self._analyze_background(wav_bytes))
         else:
             await self.push_frame(frame, direction)
 
+    async def _analyze_background(self, wav_bytes: bytes):
+        """Run SpeechBrain in background, store result for next turn."""
+        try:
+            vocal_state, analysis = await self._analyze(wav_bytes)
+            self._last_vocal_state = vocal_state
+            if analysis and self._clip_worth_saving(analysis, wav_bytes):
+                await self._save_clip(wav_bytes, analysis)
+        except Exception as e:
+            logger.warning("Background SpeechBrain failed: %s", e)
+
     def _flush_audio_to_wav(self) -> Optional[bytes]:
         if not self._audio_buffer:
             return None
@@ -180,12 +318,11 @@ class VocalStateInjector(FrameProcessor):
                 d,
             )
         except Exception as e:
-            logger.warning("SpeechBrain unavailable: %s -- continuing without vocal state", e)
+            logger.warning("SpeechBrain unavailable: %s", e)
             return None, None
 
     def _clip_worth_saving(self, analysis: dict, wav_bytes: bytes) -> bool:
-        duration = len(wav_bytes) / (16000 * 2)
+        return len(wav_bytes) / (16000 * 2) >= MIN_CLIP_DURATION
-        return duration >= MIN_CLIP_DURATION
 
     async def _save_clip(self, wav_bytes: bytes, analysis: dict):
         try:
@@ -193,8 +330,7 @@ class VocalStateInjector(FrameProcessor):
             clip_id = f"{self._session_id}_{uuid.uuid4().hex[:8]}"
             (CLIPS_DIR / f"{clip_id}.wav").write_bytes(wav_bytes)
             meta = {
-                "clip_id": clip_id,
+                "clip_id": clip_id, "session_id": self._session_id,
-                "session_id": self._session_id,
                 "timestamp": datetime.utcnow().isoformat(),
                 "duration_seconds": len(wav_bytes) / (16000 * 2),
                 "speechbrain_emotion": analysis.get("emotion"),
@@ -203,22 +339,70 @@ class VocalStateInjector(FrameProcessor):
                 "pitch_mean_hz": analysis.get("pitch_mean_hz"),
                 "energy_rms": analysis.get("energy_rms"),
                 "speaking_rate_approx": analysis.get("speaking_rate_approx"),
-                "labeled": False,
+                "labeled": False, "confirmed_label": None,
-                "confirmed_label": None,
                 "uncertain": analysis.get("emotion_confidence", 1.0) < UNCERTAINTY_THRESHOLD,
             }
             (CLIPS_DIR / f"{clip_id}.json").write_text(json.dumps(meta, indent=2))
-            logger.info("Saved clip: %s", clip_id)
         except Exception as e:
             logger.warning("Failed to save clip: %s", e)
 
 
+# ---------------------------------------------------------------------------
+# Pre-warm
+# ---------------------------------------------------------------------------
+
+async def _prewarm_speaches():
+    """Pre-warm Whisper and Kokoro models so first request is fast."""
+    client = httpx.AsyncClient(timeout=120.0)
+    try:
+        import numpy as np
+        sr = 16000
+        audio = np.zeros(sr, dtype=np.int16)
+        buf = io.BytesIO()
+        with wave.open(buf, "wb") as wf:
+            wf.setnchannels(1); wf.setsampwidth(2); wf.setframerate(sr)
+            wf.writeframes(audio.tobytes())
+        r = await client.post(
+            f"{SPEACHES_URL}/v1/audio/transcriptions",
+            files={"file": ("warmup.wav", buf.getvalue(), "audio/wav")},
+            data={"model": WHISPER_MODEL, "language": "en",
+                  "response_format": "json", "vad_filter": "false"},
+        )
+        logger.info("Pre-warm Whisper: %s", r.status_code)
+        r2 = await client.post(
+            f"{SPEACHES_URL}/v1/audio/speech",
+            json={"model": KOKORO_MODEL, "input": "Ready.", "voice": KOKORO_VOICE},
+        )
+        logger.info("Pre-warm Kokoro: %s", r2.status_code)
+    except Exception as e:
+        logger.warning("Pre-warm failed: %s", e)
+    finally:
+        await client.aclose()
+
+
+# ---------------------------------------------------------------------------
+# Pipeline assembly
+# ---------------------------------------------------------------------------
+
 async def run_voice_coach_pipeline(
     livekit_url: str,
     livekit_token: str,
     openclaw_token: str,
 ):
     session_id = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
+    await _prewarm_speaches()
+
+    # Load settings from token server
+    settings = {"confidence": 0.7, "min_volume": 0.3, "start_secs": 0.4,
+                "stop_secs": 0.5, "max_tokens": 150, "tts_voice": "af_nova"}
+    try:
+        async with httpx.AsyncClient(timeout=5.0) as client:
+            r = await client.get("http://token-server:8080/settings")
+            if r.status_code == 200:
+                settings.update(r.json())
+                logger.info("Loaded settings: %s", settings)
+    except Exception as e:
+        logger.warning("Could not load settings, using defaults: %s", e)
 
     transport = LiveKitTransport(
         url=livekit_url,
@@ -229,41 +413,38 @@ async def run_voice_coach_pipeline(
             audio_out_enabled=True,
             vad_enabled=True,
             vad_analyzer=SileroVADAnalyzer(params=VADParams(
-                confidence=0.5,
+                confidence=float(settings["confidence"]),
-                start_secs=0.2,
+                start_secs=float(settings["start_secs"]),
-                stop_secs=0.8,
+                stop_secs=float(settings["stop_secs"]),
-                min_volume=0.1,
+                min_volume=float(settings["min_volume"]),
             )),
             vad_audio_passthrough=True,
         ),
     )
 
-    # STT: Speaches / faster-whisper via OpenAI-compatible API
+    stt = SpeachesSTTProcessor(base_url=SPEACHES_URL, model=WHISPER_MODEL, language="en")
-    stt = SpeachesSTTService(
-        base_url=SPEACHES_URL,
-        model=WHISPER_MODEL,
-        language="en",
-    )
-
     vocal_injector = VocalStateInjector(session_id=session_id)
+    sentence_agg = SentenceAggregator()
+
+    from prompt_builder import build_system_prompt
+    system_prompt = build_system_prompt()
 
-    # LLM: OpenClaw VoiceCoach agent via OpenAI-compatible gateway
     llm = OpenAILLMService(
-        api_key=openclaw_token,
+        api_key="ollama",
-        base_url=f"{OPENCLAW_URL}/v1",
+        base_url=LLM_BASE_URL,
-        model=f"openclaw/{OPENCLAW_AGENT_ID}",
+        model=LLM_MODEL,
-        max_tokens=512,
+        max_tokens=int(settings["max_tokens"]),
     )
-
+    context = OpenAILLMContext(messages=[
-    context = OpenAILLMContext(messages=[])
+        {"role": "system", "content": system_prompt},
+    ])
     context_aggregator = llm.create_context_aggregator(context)
 
-    # TTS: Speaches / Kokoro via OpenAI-compatible API
+    tts = SpeachesTTSService(
-    tts = OpenAITTSService(
         api_key="speaches",
         base_url=f"{SPEACHES_URL}/v1",
-        model="kokoro",
+        model=KOKORO_MODEL,
-        voice=KOKORO_VOICE,
+        voice=settings.get("tts_voice", KOKORO_VOICE),
     )
 
     pipeline = Pipeline([
@@ -272,25 +453,24 @@ async def run_voice_coach_pipeline(
         vocal_injector,
         context_aggregator.user(),
         llm,
+        sentence_agg,          # Fix 1: aggregate sentences before TTS
         tts,
         transport.output(),
         context_aggregator.assistant(),
     ])
 
-    task = PipelineTask(
+    task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
-        pipeline,
-        params=PipelineParams(allow_interruptions=True),
-    )
     runner = PipelineRunner()
 
     @transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):
         logger.info("Participant joined: %s", participant)
-        await task.queue_frames([TextFrame("session_start")])
+        await task.queue_frames([TTSSpeakFrame("VoiceCoach connected. How are you doing today?")])
 
     @transport.event_handler("on_participant_left")
     async def on_participant_left(transport, participant, reason):
-        logger.info("Session ending: %s", session_id)
+        logger.info("Session ending: %s (reason: %s)", session_id, reason)
-        await task.queue_frames([EndFrame()])
+        await task.cancel()
 
     await runner.run(task)
+    logger.info("Pipeline runner exited for session %s", session_id)

pipecat-agent/piper_tts.py

@@ -0,0 +1,34 @@
+"""
+Piper TTS client for Pipecat pipeline.
+Calls the self-hosted Piper HTTP service.
+"""
+import io
+import logging
+import wave
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+class PiperTTSClient:
+    """HTTP client for the Piper TTS service."""
+
+    def __init__(self, base_url: str = "http://piper-tts:8003"):
+        self.base_url = base_url.rstrip("/")
+        self._client = httpx.AsyncClient(timeout=10.0)
+
+    async def synthesize(self, text: str) -> bytes:
+        """Convert text to WAV audio bytes via Piper service."""
+        try:
+            response = await self._client.post(
+                f"{self.base_url}/synthesize",
+                json={"text": text, "length_scale": 0.95, "sentence_silence": 0.15},
+            )
+            response.raise_for_status()
+            return response.content
+        except Exception as e:
+            logger.error(f"Piper TTS failed: {e}")
+            return b""
+
+    async def close(self):
+        await self._client.aclose()

pipecat-agent/token_server.py

@@ -0,0 +1,95 @@
+"""
+Standalone LiveKit token server + settings API.
+Runs independently from the pipeline so it never goes down when the pipeline crashes.
+Settings are stored in a JSON file so the pipeline can read them on startup.
+"""
+import json
+import os
+from pathlib import Path
+import uvicorn
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import Optional
+from livekit import api as livekit_api
+
+LIVEKIT_API_KEY = os.environ["LIVEKIT_API_KEY"]
+LIVEKIT_API_SECRET = os.environ["LIVEKIT_API_SECRET"]
+LIVEKIT_PUBLIC_URL = os.getenv("LIVEKIT_PUBLIC_URL", "wss://voice.hobbs.farm")
+SETTINGS_PATH = Path(os.getenv("SETTINGS_PATH", "/data/settings.json"))
+
+app = FastAPI(title="VoiceCoach Token + Settings Service")
+
+DEFAULTS = {
+    "confidence": 0.7,
+    "min_volume": 0.3,
+    "start_secs": 0.4,
+    "stop_secs": 0.5,
+    "max_tokens": 150,
+    "tts_voice": "af_nova",
+}
+
+
+def load_settings() -> dict:
+    try:
+        if SETTINGS_PATH.exists():
+            return {**DEFAULTS, **json.loads(SETTINGS_PATH.read_text())}
+    except Exception:
+        pass
+    return {**DEFAULTS}
+
+
+def save_settings(settings: dict):
+    SETTINGS_PATH.parent.mkdir(parents=True, exist_ok=True)
+    SETTINGS_PATH.write_text(json.dumps(settings, indent=2))
+
+
+class TokenRequest(BaseModel):
+    identity: str = "jeremy"
+
+
+class TokenResponse(BaseModel):
+    token: str
+    url: str
+
+
+class Settings(BaseModel):
+    confidence: Optional[float] = None
+    min_volume: Optional[float] = None
+    start_secs: Optional[float] = None
+    stop_secs: Optional[float] = None
+    max_tokens: Optional[int] = None
+    tts_voice: Optional[str] = None
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "version": "2.1"}
+
+
+@app.post("/token")
+async def get_token(req: TokenRequest):
+    token = livekit_api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
+    token.with_grants(livekit_api.VideoGrants(
+        room_join=True, room="voicecoach",
+        can_publish=True, can_subscribe=True,
+    ))
+    token.identity = req.identity
+    return TokenResponse(token=token.to_jwt(), url=LIVEKIT_PUBLIC_URL)
+
+
+@app.get("/settings")
+async def get_settings():
+    return load_settings()
+
+
+@app.post("/settings")
+async def update_settings(s: Settings):
+    current = load_settings()
+    update = {k: v for k, v in s.dict().items() if v is not None}
+    current.update(update)
+    save_settings(current)
+    return current
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8080, log_level="info")

pipecat-agent/whisper_stt.py

@@ -0,0 +1,35 @@
+"""
+Local STT service using faster-whisper.
+Replaces Deepgram with fully self-hosted speech-to-text.
+"""
+import io
+import logging
+import wave
+import numpy as np
+from faster_whisper import WhisperModel
+
+logger = logging.getLogger(__name__)
+
+
+class LocalWhisperSTT:
+    """Wraps faster-whisper for use in the Pipecat pipeline."""
+
+    def __init__(self, model_size: str = "medium", device: str = "cpu", compute_type: str = "int8"):
+        logger.info(f"Loading faster-whisper model: {model_size} on {device}")
+        self.model = WhisperModel(model_size, device=device, compute_type=compute_type)
+        logger.info("faster-whisper model loaded.")
+
+    def transcribe(self, audio_bytes: bytes, sample_rate: int = 16000) -> str:
+        """Transcribe raw PCM audio bytes to text."""
+        audio_array = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0
+
+        segments, info = self.model.transcribe(
+            audio_array,
+            beam_size=5,
+            language="en",
+            vad_filter=True,
+            vad_parameters=dict(min_silence_duration_ms=500),
+        )
+
+        text = " ".join(seg.text.strip() for seg in segments)
+        return text

web-client/app.js

@@ -1,17 +1,119 @@
 const LIVEKIT_URL = "wss://voice.hobbs.farm";
 let room = null;
 
+// --- Settings ---
+const DEFAULTS = {
+  confidence: 0.7, min_volume: 0.3, start_secs: 0.4,
+  stop_secs: 0.5, max_tokens: 150, tts_voice: "af_nova"
+};
+
+function getSettings() {
+  try {
+    const saved = JSON.parse(localStorage.getItem("vc_settings"));
+    return saved ? { ...DEFAULTS, ...saved } : { ...DEFAULTS };
+  } catch (e) { return { ...DEFAULTS }; }
+}
+
+function loadSettingsUI() {
+  const s = getSettings();
+  for (const [key, val] of Object.entries(s)) {
+    const input = document.getElementById("set-" + key);
+    const display = document.getElementById("val-" + key);
+    if (input) input.value = val;
+    if (display) display.textContent = val;
+  }
+  // Bind live value display
+  document.querySelectorAll(".settings-body input[type=range]").forEach(function(el) {
+    el.addEventListener("input", function() {
+      const key = el.id.replace("set-", "");
+      document.getElementById("val-" + key).textContent = el.value;
+    });
+  });
+  var sel = document.getElementById("set-tts_voice");
+  if (sel) sel.addEventListener("change", function() {
+    document.getElementById("val-tts_voice").textContent = sel.value;
+  });
+}
+
+function saveSettings() {
+  var s = {};
+  for (var key of Object.keys(DEFAULTS)) {
+    var el = document.getElementById("set-" + key);
+    if (el) s[key] = el.tagName === "SELECT" ? el.value : parseFloat(el.value);
+  }
+  localStorage.setItem("vc_settings", JSON.stringify(s));
+  // Push to server
+  fetch("/api/settings", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(s),
+  }).then(function(r) {
+    if (r.ok) log("Settings saved and applied");
+    else log("Settings saved locally but server update failed", "warn");
+  }).catch(function() {
+    log("Settings saved locally (server unreachable)", "warn");
+  });
+}
+
+function resetSettings() {
+  localStorage.removeItem("vc_settings");
+  loadSettingsUI();
+  saveSettings();
+  log("Settings reset to defaults");
+}
+
+function toggleSettings() {
+  var body = document.getElementById("settingsBody");
+  var toggle = document.getElementById("settingsToggle");
+  if (body.style.display === "none") {
+    body.style.display = "block";
+    toggle.textContent = "-";
+  } else {
+    body.style.display = "none";
+    toggle.textContent = "+";
+  }
+}
+
+// --- Logging ---
+function log(msg, level) {
+  var el = document.getElementById("logOutput");
+  var time = new Date().toLocaleTimeString();
+  var cls = level === "error" ? "log-error" : level === "warn" ? "log-warn" : "log-info";
+  el.innerHTML += '<div class="' + cls + '"><span class="log-time">' + time + '</span> ' + msg + '</div>';
+  el.scrollTop = el.scrollHeight;
+}
+
+function clearLog() {
+  document.getElementById("logOutput").innerHTML = "";
+}
+
+// --- Connection ---
 async function connect() {
   setStatus("connecting", "Connecting...");
+  log("Requesting token...");
+
+  // Push current settings before connecting
+  var settings = getSettings();
   try {
-    // Request a token from the token service
+    await fetch("/api/settings", {
-    const response = await fetch("/api/token", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(settings),
+    });
+    log("Settings pushed: confidence=" + settings.confidence + " vol=" + settings.min_volume);
+  } catch (e) {
+    log("Could not push settings", "warn");
+  }
+
+  try {
+    var response = await fetch("/api/token", {
       method: "POST",
       headers: { "Content-Type": "application/json" },
       body: JSON.stringify({ identity: "jeremy-" + Date.now() }),
     });
-    if (!response.ok) throw new Error("Failed to get token");
+    if (!response.ok) throw new Error("Token " + response.status);
-    const { token } = await response.json();
+    var data = await response.json();
+    log("Token received");
 
     room = new LivekitClient.Room({
       audioCaptureDefaults: {
@@ -21,57 +123,77 @@ async function connect() {
       },
     });
 
-    room.on(LivekitClient.RoomEvent.Connected, () => {
+    room.on(LivekitClient.RoomEvent.Connected, function() {
       setStatus("connected", "Connected");
       document.getElementById("connectBtn").style.display = "none";
       document.getElementById("disconnectBtn").style.display = "block";
+      log("Connected to LiveKit");
     });
 
-    room.on(LivekitClient.RoomEvent.Disconnected, () => {
+    room.on(LivekitClient.RoomEvent.Disconnected, function(reason) {
       setStatus("disconnected", "Disconnected");
       document.getElementById("connectBtn").style.display = "block";
       document.getElementById("disconnectBtn").style.display = "none";
+      log("Disconnected" + (reason ? ": " + reason : ""), "warn");
     });
 
-    room.on(LivekitClient.RoomEvent.TrackSubscribed, (track) => {
+    room.on(LivekitClient.RoomEvent.ParticipantConnected, function(p) {
+      log("Agent joined: " + p.identity);
+    });
+
+    room.on(LivekitClient.RoomEvent.TrackSubscribed, function(track, pub, p) {
       if (track.kind === LivekitClient.Track.Kind.Audio) {
-        const element = track.attach();
+        var el = track.attach();
-        document.body.appendChild(element);
+        document.body.appendChild(el);
-        element.play();
+        el.play().catch(function(e) { log("Audio play error: " + e.message, "error"); });
       }
     });
 
-    await room.connect(LIVEKIT_URL, token);
+    room.on(LivekitClient.RoomEvent.MediaDevicesError, function(err) {
+      log("Mic error: " + err.message, "error");
+    });
+
+    await room.connect(data.url || LIVEKIT_URL, data.token);
     await room.localParticipant.setMicrophoneEnabled(true);
+    log("Mic enabled. Speak now.");
+
   } catch (err) {
     setStatus("disconnected", "Error: " + err.message);
-    console.error(err);
+    log("Error: " + err.message, "error");
   }
 }
 
 async function disconnect() {
-  if (room) {
+  if (room) { await room.disconnect(); room = null; }
-    await room.disconnect();
-    room = null;
-  }
 }
 
 function setMode(mode) {
   document.getElementById("modeDisplay").textContent = "Mode: " + mode;
+  log("Mode: " + mode);
   if (room && room.localParticipant) {
-    const encoder = new TextEncoder();
     room.localParticipant.publishData(
-      encoder.encode(JSON.stringify({ type: "mode_change", mode })),
+      new TextEncoder().encode(JSON.stringify({ type: "mode_change", mode })),
       { reliable: true }
     );
   }
 }
 
 function setStatus(state, text) {
-  const el = document.getElementById("status");
+  var el = document.getElementById("status");
   el.className = "status " + state;
   el.textContent = text;
 }
 
 document.getElementById("connectBtn").addEventListener("click", connect);
 document.getElementById("disconnectBtn").addEventListener("click", disconnect);
+
+// Init
+loadSettingsUI();
+// Push saved settings on load
+fetch("/api/settings", {
+  method: "POST",
+  headers: { "Content-Type": "application/json" },
+  body: JSON.stringify(getSettings()),
+}).catch(function() {});
+
+log("VoiceCoach loaded. Ready.");

web-client/index.html

@@ -30,6 +30,60 @@
       <button class="mode-btn" onclick="setMode('practice mode')">Practice</button>
       <button class="mode-btn" onclick="setMode('briefing mode')">Briefing</button>
     </div>
+
+    <div class="settings-panel">
+      <div class="settings-header" onclick="toggleSettings()">
+        <span>Settings</span>
+        <span id="settingsToggle">+</span>
+      </div>
+      <div id="settingsBody" class="settings-body" style="display:none">
+        <div class="setting-group">
+          <label>VAD Confidence <span class="setting-val" id="val-confidence">0.7</span></label>
+          <input type="range" id="set-confidence" min="0.3" max="0.95" step="0.05" value="0.7">
+          <div class="setting-hint">Higher = less false triggers from noise</div>
+        </div>
+        <div class="setting-group">
+          <label>Min Volume <span class="setting-val" id="val-min_volume">0.3</span></label>
+          <input type="range" id="set-min_volume" min="0.05" max="0.6" step="0.05" value="0.3">
+          <div class="setting-hint">Higher = ignores quieter sounds</div>
+        </div>
+        <div class="setting-group">
+          <label>Start Delay <span class="setting-val" id="val-start_secs">0.4</span>s</label>
+          <input type="range" id="set-start_secs" min="0.1" max="0.8" step="0.1" value="0.4">
+          <div class="setting-hint">Sustained speech needed before trigger</div>
+        </div>
+        <div class="setting-group">
+          <label>Stop Delay <span class="setting-val" id="val-stop_secs">0.5</span>s</label>
+          <input type="range" id="set-stop_secs" min="0.2" max="1.5" step="0.1" value="0.5">
+          <div class="setting-hint">Silence before turn ends (higher = less cutoff)</div>
+        </div>
+        <div class="setting-group">
+          <label>LLM Max Tokens <span class="setting-val" id="val-max_tokens">150</span></label>
+          <input type="range" id="set-max_tokens" min="50" max="500" step="25" value="150">
+          <div class="setting-hint">Longer = more detailed responses, slower</div>
+        </div>
+        <div class="setting-group">
+          <label>TTS Voice <span class="setting-val" id="val-tts_voice">af_nova</span></label>
+          <select id="set-tts_voice">
+            <option value="af_nova">Nova (Female, American)</option>
+            <option value="af_sky">Sky (Female, American)</option>
+            <option value="am_adam">Adam (Male, American)</option>
+            <option value="bf_emma">Emma (Female, British)</option>
+            <option value="bm_george">George (Male, British)</option>
+          </select>
+        </div>
+        <button class="btn btn-small" onclick="saveSettings()">Apply Settings</button>
+        <button class="btn btn-small btn-reset" onclick="resetSettings()">Reset Defaults</button>
+      </div>
+    </div>
+
+    <div class="log-panel">
+      <div class="log-header">
+        <span>Log</span>
+        <button class="log-clear" onclick="clearLog()">Clear</button>
+      </div>
+      <div id="logOutput" class="log-output"></div>
+    </div>
   </div>
 
   <script src="https://cdn.jsdelivr.net/npm/livekit-client/dist/livekit-client.umd.min.js"></script>

web-client/nginx.conf

@@ -4,7 +4,7 @@ server {
     index index.html;
 
     location /api/ {
-        proxy_pass http://pipecat-agent:8080/;
+        proxy_pass http://token-server:8080/;
         proxy_http_version 1.1;
         proxy_set_header Host $host;
     }

web-client/style.css

@@ -86,3 +86,125 @@ h1 { font-size: 24px; font-weight: 600; }
   transition: background 0.2s;
 }
 .mode-btn:hover { background: #1a2744; border-color: #4a6cf7; }
+
+.settings-panel {
+  margin-top: 16px;
+  background: #0d1117;
+  border: 1px solid #333;
+  border-radius: 8px;
+  overflow: hidden;
+}
+.settings-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 8px 12px;
+  background: #161b22;
+  font-size: 12px;
+  font-weight: 600;
+  color: #888;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+  cursor: pointer;
+}
+.settings-header:hover { color: #ccc; }
+.settings-body { padding: 12px; }
+.setting-group { margin-bottom: 14px; }
+.setting-group label {
+  display: flex;
+  justify-content: space-between;
+  font-size: 13px;
+  color: #ccc;
+  margin-bottom: 4px;
+}
+.setting-val {
+  color: #4a6cf7;
+  font-weight: 600;
+  font-family: 'SF Mono', monospace;
+}
+.setting-group input[type=range] {
+  width: 100%;
+  height: 6px;
+  -webkit-appearance: none;
+  background: #333;
+  border-radius: 3px;
+  outline: none;
+}
+.setting-group input[type=range]::-webkit-slider-thumb {
+  -webkit-appearance: none;
+  width: 18px;
+  height: 18px;
+  border-radius: 50%;
+  background: #4a6cf7;
+  cursor: pointer;
+}
+.setting-group select {
+  width: 100%;
+  padding: 6px 8px;
+  background: #16213e;
+  border: 1px solid #333;
+  border-radius: 4px;
+  color: #ccc;
+  font-size: 13px;
+}
+.setting-hint {
+  font-size: 11px;
+  color: #555;
+  margin-top: 2px;
+}
+.btn-small {
+  padding: 8px 16px;
+  font-size: 13px;
+  width: auto;
+  display: inline-block;
+  margin-right: 8px;
+  margin-top: 8px;
+}
+.btn-reset {
+  background: #333;
+  color: #aaa;
+}
+.btn-reset:hover { background: #444; }
+
+.log-panel {
+  margin-top: 16px;
+  background: #0d1117;
+  border: 1px solid #333;
+  border-radius: 8px;
+  overflow: hidden;
+}
+.log-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 6px 12px;
+  background: #161b22;
+  font-size: 12px;
+  font-weight: 600;
+  color: #888;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+}
+.log-clear {
+  background: none;
+  border: 1px solid #444;
+  border-radius: 4px;
+  color: #888;
+  font-size: 11px;
+  padding: 2px 8px;
+  cursor: pointer;
+}
+.log-clear:hover { border-color: #666; color: #ccc; }
+.log-output {
+  padding: 8px 12px;
+  max-height: 200px;
+  overflow-y: auto;
+  font-family: 'SF Mono', 'Fira Code', monospace;
+  font-size: 11px;
+  line-height: 1.6;
+}
+.log-output div { word-break: break-all; }
+.log-time { color: #555; margin-right: 6px; }
+.log-info { color: #8b949e; }
+.log-warn { color: #d29922; }
+.log-error { color: #f85149; }