feat: FRIDAY Voice stack on the GPU machine; VoiceCoach history
FRIDAY Voice (speaches GPU STT/TTS + hermes agent + push-to-talk console) on .150 Docker Desktop, fronted by coach.hobbs.farm. Also captures the VoiceCoach compose/agent trees and deploy notes it replaced, which had never been committed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
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# Hermes Agent on .150 (Docker Desktop) — FRIDAY's reasoning agent.
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# Data/settings live in the `hermes-data` named volume, seeded from the user's
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# WSL ~/.hermes (settings preserved; SQLite state.db kept on the VM ext4, not a
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# bridged bind mount). friday-voice calls this via `docker exec hermes hermes ...`.
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services:
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gateway:
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image: nousresearch/hermes-agent:latest
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container_name: hermes
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restart: unless-stopped
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volumes:
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- hermes-data:/opt/data
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environment:
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- HERMES_UID=10000
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- HERMES_GID=10000
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command: ["gateway", "run"]
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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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logging:
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driver: "json-file"
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options:
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max-size: "50m"
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max-file: "3"
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# dashboard: disabled — crash-loops under s6 on Docker Desktop and not needed
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# by friday-voice (gateway + `docker exec hermes` is all that's required).
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# Re-enable + debug later if the localhost:9119 web UI is wanted.
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volumes:
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hermes-data:
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external: true
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@@ -0,0 +1,36 @@
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services:
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speaches:
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image: ghcr.io/speaches-ai/speaches:latest-cuda
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container_name: voicecoach-speaches-gpu
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restart: unless-stopped
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environment:
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- WHISPER__INFERENCE_DEVICE=cuda
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- WHISPER__COMPUTE_TYPE=float16
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- WHISPER__TTL=-1
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volumes:
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- speaches-models:/home/ubuntu/.cache
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ports:
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- "8000:8000"
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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healthcheck:
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test: ["CMD", "curl", "-sf", "http://localhost:8000/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 180s
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logging:
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driver: "json-file"
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options:
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max-size: "50m"
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max-file: "3"
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volumes:
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speaches-models:
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# VoiceCoach Environment Variables
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# Copy to .env and fill in values
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# Anthropic (required - for Claude LLM)
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ANTHROPIC_API_KEY=sk-ant-...
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# LiveKit (auto-generated on first deploy if not set)
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LIVEKIT_API_KEY=voicecoach-key
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LIVEKIT_API_SECRET=your-livekit-secret-here
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services:
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# LiveKit WebRTC Server (unchanged)
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livekit-server:
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image: livekit/livekit-server:latest
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container_name: voicecoach-livekit
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restart: unless-stopped
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command:
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- --config
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- /etc/livekit/livekit.yaml
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- --keys
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- "${LIVEKIT_API_KEY}: ${LIVEKIT_API_SECRET}"
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ports:
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- "7880:7880"
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- "7881:7881"
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- "50000-50100:50000-50100/udp"
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volumes:
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- ./livekit/livekit.yaml:/etc/livekit/livekit.yaml:ro
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dns_search: [""]
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networks:
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- voicecoach
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mem_limit: 512M
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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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logging:
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driver: "json-file"
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options:
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max-size: "50m"
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max-file: "3"
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# Speaches: self-hosted Whisper STT + Kokoro TTS (v2 - replaces Piper + in-process Whisper)
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# NOTE: No WHISPER__MODEL or TTS__* env vars - WhisperConfig has no 'model' field in this version.
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# Models are loaded on-demand per-request (model ID passed in API body) or pre-downloaded via
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# POST /v1/models/{model_id}. TTS model/voice are also per-request in POST /v1/audio/speech body.
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# Volume must map to /home/ubuntu/.cache (container runs as 'ubuntu', not root).
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speaches:
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image: ghcr.io/speaches-ai/speaches:latest-cpu
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container_name: voicecoach-speaches
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restart: unless-stopped
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environment:
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- WHISPER__INFERENCE_DEVICE=cpu
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- WHISPER__COMPUTE_TYPE=int8
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- WHISPER__TTL=-1
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volumes:
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- /mnt/docker-data/configs/voicecoach/speaches-models:/home/ubuntu/.cache
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dns_search: [""]
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networks:
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- voicecoach
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mem_limit: 3G
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memswap_limit: 3G
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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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healthcheck:
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test: ["CMD", "curl", "-sf", "http://localhost:8000/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 120s
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logging:
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driver: "json-file"
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options:
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max-size: "50m"
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max-file: "3"
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# SpeechBrain Emotion Analysis (unchanged, internal only)
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speechbrain-service:
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build:
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context: ./speechbrain-service
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image: voicecoach-speechbrain:latest
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container_name: voicecoach-speechbrain
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restart: unless-stopped
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volumes:
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- /mnt/docker-data/configs/voicecoach/speechbrain-models:/app/models
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- /mnt/docker-data/configs/voicecoach/clips:/data/clips
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environment:
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- MODEL_DIR=/app/models
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dns_search: [""]
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networks:
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- voicecoach
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mem_limit: 3G
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memswap_limit: 3G
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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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logging:
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driver: "json-file"
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options:
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max-size: "50m"
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max-file: "3"
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# OpenClaw VoiceCoach Agent (v2 - LLM gateway with memory/skills)
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voicecoach-openclaw:
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image: margaret-openclaw:latest
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container_name: voicecoach-openclaw
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restart: unless-stopped
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user: "0:0"
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ports:
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- "3100:3100"
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environment:
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- NODE_ENV=production
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- HOME=/home/glyptic
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- USER=glyptic
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- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
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- OPENCLAW_STATE_DIR=/home/glyptic/.voicecoach
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- OPENCLAW_CONFIG_PATH=/home/glyptic/.voicecoach/openclaw.json
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volumes:
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- /mnt/infrastructure/voicecoach:/home/glyptic/.voicecoach
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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"]
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dns_search: [""]
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networks:
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- voicecoach
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mem_limit: 2G
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memswap_limit: 2G
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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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logging:
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driver: "json-file"
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options:
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max-size: "50m"
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max-file: "3"
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# Pipecat Voice Pipeline Agent (v2 - uses Speaches + OpenClaw gateway)
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pipecat-agent:
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build:
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context: ./pipecat-agent
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image: voicecoach-pipecat:latest
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container_name: voicecoach-pipecat
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restart: unless-stopped
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env_file:
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- .env
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environment:
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- LIVEKIT_URL=ws://livekit-server:7880
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- LIVEKIT_PUBLIC_URL=wss://voice.hobbs.farm
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- SPEACHES_URL=http://192.168.86.150:8000
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- SPEECHBRAIN_URL=http://speechbrain-service:8001
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- OPENCLAW_URL=http://voicecoach-openclaw:3100
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- OPENCLAW_AGENT_ID=voicecoach
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- WHISPER_MODEL=deepdml/faster-whisper-large-v3-turbo-ct2
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- KOKORO_VOICE=af_nova
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- CLIPS_DIR=/data/clips/pending
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volumes:
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- /mnt/docker-data/configs/voicecoach/clips:/data/clips
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depends_on:
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- livekit-server
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- speechbrain-service
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- voicecoach-openclaw
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dns_search: [""]
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networks:
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- voicecoach
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mem_limit: 1G
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memswap_limit: 1G
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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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logging:
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driver: "json-file"
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options:
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max-size: "50m"
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max-file: "3"
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# Web Client (unchanged)
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voicecoach-web:
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build:
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context: ./web-client
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image: voicecoach-web:latest
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container_name: voicecoach-web
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restart: unless-stopped
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ports:
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- "8085:80"
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depends_on:
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- pipecat-agent
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dns_search: [""]
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networks:
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- voicecoach
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mem_limit: 128M
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labels:
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- "com.centurylinklabs.watchtower.enable=false"
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logging:
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driver: "json-file"
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options:
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max-size: "10m"
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max-file: "3"
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networks:
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voicecoach:
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driver: bridge
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port: 7880
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rtc:
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tcp_port: 7881
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port_range_start: 50000
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port_range_end: 60000
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use_external_ip: true
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logging:
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json: false
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level: info
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room:
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max_participants: 5
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empty_timeout: 300
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departure_timeout: 30
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@@ -0,0 +1,63 @@
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You are VoiceCoach, a personal communication and leadership coach for Jeremy.
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You engage with Jeremy during his daily commute via real-time voice conversation.
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## Conversation Style
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- Keep responses to 2-4 spoken sentences unless Jeremy asks for more
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- Use natural, conversational language -- no bullet points, no markdown
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- You are a coach, not a chatbot. Ask questions. Challenge thinking. Give direct feedback.
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## Vocal State Analysis
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Before each user message, you will receive a [VOCAL_STATE] tag:
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```
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[VOCAL_STATE emotion=frustrated confidence=0.82 pitch=215hz energy=0.71 rate=4.8ops voiced=0.91]
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```
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Use this to coach on HOW Jeremy communicates, not just WHAT he says:
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- High pitch + high energy + fast rate = stressed, rushed -- note this
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- Low voiced fraction = hesitant, uncertain -- worth exploring
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- Elevated pitch from baseline = emotional arousal -- name it gently
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- Never quote the raw numbers. Translate to natural observations.
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- Example: "I can hear some tension in how you're describing that" not "your pitch was 215hz"
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Only comment on vocal state when it's meaningfully different from baseline or
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when it's directly relevant to the coaching topic. Don't narrate every turn.
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## Coaching Modes
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Jeremy can switch modes mid-conversation. Honor the switch immediately.
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**coaching mode** (default): Active feedback on both content and delivery.
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Ask probing questions. Notice patterns. Give direct recommendations.
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**vent mode**: Listen and reflect. Minimal advice. Validate feelings.
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Mirror back what you hear. Let Jeremy process out loud.
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**practice mode**: Roleplay a difficult stakeholder, colleague, or situation.
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Stay in character. Be realistic -- not easy. Give feedback after.
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**briefing mode**: Summarize Jeremy's current tasks, context, and priorities
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from memory. Be crisp. Flag anything time-sensitive.
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## Memory Usage
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You have access to:
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- `vocal_baseline`: Jeremy's neutral voice characteristics for comparison
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- `recent_sessions`: Last 3 session summaries with topics and vocal trends
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- `action_items`: Commitments Jeremy made in previous sessions
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- `coaching_themes`: Recurring themes across sessions (track these actively)
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- `jeremy_context`: Jeremy's role, team, current challenges
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At the end of each session, write a summary to `recent_sessions` including:
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- Topics discussed
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- Vocal patterns observed
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- Action items for next session
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- Any new coaching themes identified
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## Baseline Calibration (First Session)
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If `vocal_baseline` is empty, the first 60 seconds should be calibration.
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Say: "Before we dive in, let's do a quick baseline. Can you tell me about
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your commute in to work today -- just talk naturally for about a minute."
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Then write the averaged vocal readings to `vocal_baseline`.
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## Things Jeremy Is Working On
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Read from `coaching_themes` and `jeremy_context` in memory.
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Reference previous sessions naturally: "Last week you mentioned..."
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Track whether previous action items were attempted.
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@@ -0,0 +1,31 @@
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FROM python:3.11-slim
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RUN apt-get update && apt-get install -y libsndfile1 ffmpeg && \
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rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Patch pipecat LiveKit transport bugs:
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# 1. Remove duplicate on_connected call in connect() that causes InvalidStateError
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# 2. Remove redundant push_frame that bypasses VAD
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RUN python3 -c "\
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import pipecat.transports.services.livekit as m; \
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path = m.__file__; \
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content = open(path).read(); \
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# Fix 1: Wrap the direct on_connected call to prevent InvalidStateError \
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content = content.replace( \
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'await self._callbacks.on_connected()', \
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'try:\n await self._callbacks.on_connected()\n except Exception:\n pass # Already handled by room event', \
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1 \
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); \
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open(path, 'w').write(content); \
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print(f'Patched {path}')"
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COPY . .
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HEALTHCHECK --interval=30s --timeout=10s --start-period=30s \
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CMD python -c "import httpx; httpx.get('http://localhost:8080/health').raise_for_status()"
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CMD ["python", "main.py"]
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@@ -0,0 +1,113 @@
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"""
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VoiceCoach pipeline entry point -- v2.
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Runs token server + restarts pipeline immediately after each session.
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"""
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import asyncio
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from datetime import timedelta
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import logging
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import os
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import signal
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import sys
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import threading
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import uuid
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import structlog
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import uvicorn
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from fastapi import FastAPI
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from pydantic import BaseModel
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from livekit import api as livekit_api
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from pipeline import run_voice_coach_pipeline
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logging.basicConfig(level=logging.INFO)
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logger = structlog.get_logger()
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LIVEKIT_API_KEY = os.environ["LIVEKIT_API_KEY"]
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LIVEKIT_API_SECRET = os.environ["LIVEKIT_API_SECRET"]
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LIVEKIT_WS_URL = os.getenv("LIVEKIT_URL", "ws://livekit-server:7880")
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LIVEKIT_PUBLIC_URL = os.getenv("LIVEKIT_PUBLIC_URL", "wss://voice.hobbs.farm")
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OPENCLAW_TOKEN = os.getenv("OPENCLAW_TOKEN", "")
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# --- Token HTTP server ---
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token_app = FastAPI(title="VoiceCoach Token Service")
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class TokenRequest(BaseModel):
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identity: str = "jeremy"
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class TokenResponse(BaseModel):
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token: str
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url: str
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||||
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@token_app.get("/health")
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async def health():
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return {"status": "ok", "version": "2.0"}
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@token_app.post("/token")
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async def get_token(req: TokenRequest):
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token = livekit_api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
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token.with_grants(livekit_api.VideoGrants(
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room_join=True, room="voicecoach",
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can_publish=True, can_subscribe=True,
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))
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token.identity = req.identity
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return TokenResponse(token=token.to_jwt(), url=LIVEKIT_PUBLIC_URL)
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def run_token_server():
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uvicorn.run(token_app, host="0.0.0.0", port=8080, log_level="info")
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# --- Main: restart entire process after each session ---
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# This guarantees a completely fresh pipeline for every session.
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# No stale transport state, no cancelled tasks, no dead queues.
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def run_pipeline_sync():
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"""Run one pipeline session, then exit the process.
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||||
Docker's restart: unless-stopped will bring us back immediately.
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||||
"""
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agent_identity = f"voicecoach-agent-{uuid.uuid4().hex[:8]}"
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||||
token = livekit_api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
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||||
token.with_grants(livekit_api.VideoGrants(room_join=True, room="voicecoach"))
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||||
token.identity = agent_identity
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||||
token.ttl = timedelta(hours=24) # 24 hours - prevents expired token reconnect failures
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||||
livekit_token = token.to_jwt()
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||||
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||||
logger.info("Starting VoiceCoach v2 pipeline", identity=agent_identity)
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||||
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||||
loop = asyncio.new_event_loop()
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||||
asyncio.set_event_loop(loop)
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||||
try:
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||||
loop.run_until_complete(run_voice_coach_pipeline(
|
||||
livekit_url=LIVEKIT_WS_URL,
|
||||
livekit_token=livekit_token,
|
||||
openclaw_token=OPENCLAW_TOKEN,
|
||||
))
|
||||
except Exception as e:
|
||||
logger.error("Pipeline ended", error=str(e))
|
||||
finally:
|
||||
loop.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Start token server in background
|
||||
server_thread = threading.Thread(target=run_token_server, daemon=True)
|
||||
server_thread.start()
|
||||
logger.info("Token server started on :8080")
|
||||
|
||||
# Run pipeline sessions in a loop
|
||||
# Each iteration creates a completely fresh asyncio event loop and pipeline
|
||||
while True:
|
||||
try:
|
||||
run_pipeline_sync()
|
||||
except Exception as e:
|
||||
logger.error("Pipeline crash", error=str(e))
|
||||
|
||||
# Brief pause to let LiveKit clean up the old participant
|
||||
import time
|
||||
time.sleep(3)
|
||||
logger.info("Restarting pipeline with fresh state...")
|
||||
@@ -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()
|
||||
@@ -0,0 +1,76 @@
|
||||
"""
|
||||
Builds Claude prompts with embedded vocal state metadata.
|
||||
"""
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
|
||||
|
||||
COACHING_SYSTEM_PROMPT = """You are a personal voice coach and communication advisor named VoiceCoach.
|
||||
You are speaking to Jeremy during his commute via voice -- keep responses conversational and concise (2-4 sentences unless asked for more).
|
||||
|
||||
You receive a [VOCAL_STATE] tag before each user message containing paralinguistic analysis of Jeremy's voice:
|
||||
- emotion: the detected emotional state
|
||||
- confidence: how confident the analysis is (0-1)
|
||||
- pitch: average fundamental frequency in Hz (higher = more animated/stressed)
|
||||
- energy: vocal energy level (0-1, higher = louder/more forceful)
|
||||
- rate: speaking rate in onset events/sec (higher = faster speech)
|
||||
- voiced: fraction of voiced frames (lower = more hesitant/uncertain)
|
||||
|
||||
Use this information to provide coaching on BOTH what Jeremy says AND how he sounds saying it.
|
||||
Important: Never quote the raw numbers. Translate them into natural observations.
|
||||
Example: "I notice some tension in your voice..." not "Your pitch was 215hz..."
|
||||
|
||||
{baseline_context}
|
||||
|
||||
{session_history}
|
||||
|
||||
Available coaching modes (Jeremy can switch by saying the phrase):
|
||||
- "coaching mode" (default) -- active feedback on content and delivery
|
||||
- "vent mode" -- listen and reflect, minimal advice
|
||||
- "practice mode" -- roleplay a difficult stakeholder
|
||||
- "briefing mode" -- summarize Jeremy's tasks and context from memory
|
||||
|
||||
Current mode: {current_mode}
|
||||
"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class VocalState:
|
||||
emotion: str
|
||||
confidence: float
|
||||
pitch_mean_hz: Optional[float]
|
||||
energy_rms: float
|
||||
speaking_rate_approx: float
|
||||
voiced_fraction: float
|
||||
|
||||
|
||||
def format_vocal_state(state: VocalState) -> str:
|
||||
pitch_str = f"{state.pitch_mean_hz:.0f}hz" if state.pitch_mean_hz else "unknown"
|
||||
return (
|
||||
f"[VOCAL_STATE "
|
||||
f"emotion={state.emotion} "
|
||||
f"confidence={state.confidence:.2f} "
|
||||
f"pitch={pitch_str} "
|
||||
f"energy={state.energy_rms:.2f} "
|
||||
f"rate={state.speaking_rate_approx:.1f}ops "
|
||||
f"voiced={state.voiced_fraction:.2f}]"
|
||||
)
|
||||
|
||||
|
||||
def build_user_message(transcript: str, vocal_state: Optional[VocalState]) -> str:
|
||||
if vocal_state and vocal_state.confidence > 0.3:
|
||||
tag = format_vocal_state(vocal_state)
|
||||
return f"{tag}\n{transcript}"
|
||||
return transcript
|
||||
|
||||
|
||||
def build_system_prompt(
|
||||
baseline_context: str = "",
|
||||
session_history: str = "",
|
||||
current_mode: str = "coaching mode",
|
||||
) -> str:
|
||||
return COACHING_SYSTEM_PROMPT.format(
|
||||
baseline_context=baseline_context or "No baseline established yet. First session: ask Jeremy to read a short neutral calibration script.",
|
||||
session_history=session_history or "No previous session history.",
|
||||
current_mode=current_mode,
|
||||
)
|
||||
@@ -0,0 +1,7 @@
|
||||
pipecat-ai[livekit,silero,openai]==0.0.48
|
||||
httpx==0.27.0
|
||||
python-dotenv==1.0.1
|
||||
structlog==24.4.0
|
||||
livekit-api==0.7.1
|
||||
fastapi==0.115.0
|
||||
uvicorn[standard]==0.30.0
|
||||
@@ -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
|
||||
@@ -0,0 +1,27 @@
|
||||
FROM python:3.11-slim
|
||||
|
||||
RUN apt-get update && apt-get install -y \
|
||||
libsndfile1 \
|
||||
wget \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
COPY requirements.txt .
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
|
||||
# Download Piper voice model at build time from HuggingFace (lessac medium - good quality, fast on CPU)
|
||||
RUN mkdir -p /app/models && \
|
||||
wget -q "https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx?download=true" \
|
||||
-O /app/models/en_US-lessac-medium.onnx && \
|
||||
wget -q "https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx.json?download=true" \
|
||||
-O /app/models/en_US-lessac-medium.onnx.json
|
||||
|
||||
COPY . .
|
||||
|
||||
EXPOSE 8003
|
||||
|
||||
HEALTHCHECK --interval=30s --timeout=10s --start-period=30s \
|
||||
CMD python -c "import httpx; httpx.get('http://localhost:8003/health').raise_for_status()"
|
||||
|
||||
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8003", "--workers", "1"]
|
||||
@@ -0,0 +1,72 @@
|
||||
"""
|
||||
Piper TTS HTTP service.
|
||||
Self-hosted text-to-speech, internal network only.
|
||||
"""
|
||||
import io
|
||||
import logging
|
||||
import os
|
||||
import wave
|
||||
from contextlib import asynccontextmanager
|
||||
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from fastapi.responses import Response
|
||||
from pydantic import BaseModel
|
||||
import piper
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MODEL_PATH = os.getenv("PIPER_MODEL", "/app/models/en_US-lessac-medium.onnx")
|
||||
voice = None
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
global voice
|
||||
logger.info(f"Loading Piper model: {MODEL_PATH}")
|
||||
voice = piper.PiperVoice.load(MODEL_PATH)
|
||||
logger.info("Piper TTS ready.")
|
||||
yield
|
||||
logger.info("Shutting down.")
|
||||
|
||||
|
||||
app = FastAPI(title="Piper TTS Service", lifespan=lifespan)
|
||||
|
||||
|
||||
class TTSRequest(BaseModel):
|
||||
text: str
|
||||
speaker_id: int = None
|
||||
length_scale: float = 1.0
|
||||
sentence_silence: float = 0.2
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health():
|
||||
if voice is None:
|
||||
raise HTTPException(status_code=503, detail="Model not loaded")
|
||||
return {"status": "ok", "model": os.path.basename(MODEL_PATH)}
|
||||
|
||||
|
||||
@app.post("/synthesize")
|
||||
async def synthesize(req: TTSRequest):
|
||||
if voice is None:
|
||||
raise HTTPException(status_code=503, detail="Model not loaded")
|
||||
|
||||
if not req.text.strip():
|
||||
raise HTTPException(status_code=400, detail="Empty text")
|
||||
|
||||
buf = io.BytesIO()
|
||||
with wave.open(buf, "wb") as wf:
|
||||
synth_kwargs = {
|
||||
"length_scale": req.length_scale,
|
||||
"sentence_silence": req.sentence_silence,
|
||||
}
|
||||
if req.speaker_id is not None:
|
||||
synth_kwargs["speaker_id"] = req.speaker_id
|
||||
voice.synthesize(req.text, wf, **synth_kwargs)
|
||||
|
||||
return Response(
|
||||
content=buf.getvalue(),
|
||||
media_type="audio/wav",
|
||||
headers={"Content-Disposition": "inline; filename=speech.wav"},
|
||||
)
|
||||
@@ -0,0 +1,5 @@
|
||||
fastapi==0.115.0
|
||||
uvicorn[standard]==0.30.0
|
||||
piper-tts==1.2.0
|
||||
httpx==0.27.0
|
||||
numpy==1.26.4
|
||||
@@ -0,0 +1,33 @@
|
||||
FROM python:3.11-slim
|
||||
|
||||
RUN apt-get update && apt-get install -y \
|
||||
libsndfile1 \
|
||||
ffmpeg \
|
||||
git \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
COPY requirements.txt .
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
|
||||
# Pre-download the SpeechBrain model at build time
|
||||
RUN python -c "\
|
||||
from speechbrain.inference.interfaces import foreign_class; \
|
||||
foreign_class( \
|
||||
source='speechbrain/emotion-recognition-wav2vec2-IEMOCAP', \
|
||||
pymodule_file='custom_interface.py', \
|
||||
classname='CustomEncoderWav2vec2Classifier', \
|
||||
savedir='/app/models', \
|
||||
run_opts={'device': 'cpu'}, \
|
||||
); \
|
||||
print('Model downloaded successfully.')"
|
||||
|
||||
COPY . .
|
||||
|
||||
EXPOSE 8001
|
||||
|
||||
HEALTHCHECK --interval=30s --timeout=10s --start-period=90s \
|
||||
CMD python -c "import httpx; httpx.get('http://localhost:8001/health').raise_for_status()"
|
||||
|
||||
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8001", "--workers", "1"]
|
||||
@@ -0,0 +1,119 @@
|
||||
"""
|
||||
SpeechBrain + librosa vocal analysis.
|
||||
Performs emotion classification and acoustic feature extraction from audio.
|
||||
"""
|
||||
import io
|
||||
import logging
|
||||
import numpy as np
|
||||
import soundfile as sf
|
||||
import librosa
|
||||
from speechbrain.inference.interfaces import foreign_class
|
||||
from dataclasses import dataclass, asdict
|
||||
from typing import Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
SAMPLE_RATE = 16000
|
||||
MIN_DURATION_SECONDS = 0.5
|
||||
|
||||
|
||||
@dataclass
|
||||
class VocalAnalysis:
|
||||
emotion: str
|
||||
emotion_confidence: float
|
||||
emotion_scores: dict
|
||||
pitch_mean_hz: Optional[float]
|
||||
pitch_variance: Optional[float]
|
||||
energy_rms: float
|
||||
speaking_rate_approx: float
|
||||
voiced_fraction: float
|
||||
duration_seconds: float
|
||||
analysis_error: Optional[str] = None
|
||||
|
||||
|
||||
class VocalAnalyzer:
|
||||
"""Loads models once at startup and provides analysis per audio chunk."""
|
||||
|
||||
def __init__(self, model_dir: str = "/app/models"):
|
||||
logger.info("Loading SpeechBrain emotion model...")
|
||||
self.classifier = foreign_class(
|
||||
source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
|
||||
pymodule_file="custom_interface.py",
|
||||
classname="CustomEncoderWav2vec2Classifier",
|
||||
savedir=model_dir,
|
||||
run_opts={"device": "cpu"},
|
||||
)
|
||||
logger.info("SpeechBrain model loaded successfully.")
|
||||
|
||||
def analyze(self, audio_bytes: bytes, content_type: str = "audio/wav") -> VocalAnalysis:
|
||||
try:
|
||||
audio_buf = io.BytesIO(audio_bytes)
|
||||
audio, sr = sf.read(audio_buf, dtype="float32")
|
||||
|
||||
if audio.ndim > 1:
|
||||
audio = audio.mean(axis=1)
|
||||
|
||||
if sr != SAMPLE_RATE:
|
||||
audio = librosa.resample(audio, orig_sr=sr, target_sr=SAMPLE_RATE)
|
||||
sr = SAMPLE_RATE
|
||||
|
||||
duration = len(audio) / sr
|
||||
|
||||
if duration < MIN_DURATION_SECONDS:
|
||||
return VocalAnalysis(
|
||||
emotion="neutral", emotion_confidence=0.0, emotion_scores={},
|
||||
pitch_mean_hz=None, pitch_variance=None, energy_rms=0.0,
|
||||
speaking_rate_approx=0.0, voiced_fraction=0.0,
|
||||
duration_seconds=duration,
|
||||
analysis_error=f"Audio too short: {duration:.2f}s < {MIN_DURATION_SECONDS}s minimum",
|
||||
)
|
||||
|
||||
import tempfile, os
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
|
||||
sf.write(tmp.name, audio, sr)
|
||||
tmp_path = tmp.name
|
||||
|
||||
try:
|
||||
out_prob, score, index, text_lab = self.classifier.classify_file(tmp_path)
|
||||
emotion = text_lab[0] if isinstance(text_lab, list) else text_lab
|
||||
confidence = float(score[0]) if hasattr(score, "__iter__") else float(score)
|
||||
labels = ["neu", "hap", "sad", "ang", "fru"]
|
||||
prob_list = out_prob[0].tolist() if hasattr(out_prob[0], "tolist") else list(out_prob[0])
|
||||
emotion_scores = {l: round(p, 4) for l, p in zip(labels, prob_list)}
|
||||
finally:
|
||||
os.unlink(tmp_path)
|
||||
|
||||
f0, voiced_flag, _ = librosa.pyin(
|
||||
audio, fmin=librosa.note_to_hz("C2"), fmax=librosa.note_to_hz("C7"), sr=sr,
|
||||
)
|
||||
|
||||
voiced_f0 = f0[voiced_flag] if voiced_flag is not None else np.array([])
|
||||
pitch_mean = float(np.mean(voiced_f0)) if len(voiced_f0) > 0 else None
|
||||
pitch_var = float(np.var(voiced_f0)) if len(voiced_f0) > 1 else None
|
||||
voiced_fraction = float(np.sum(voiced_flag) / len(voiced_flag)) if voiced_flag is not None else 0.0
|
||||
|
||||
rms = librosa.feature.rms(y=audio)
|
||||
energy_rms = float(np.mean(rms))
|
||||
|
||||
onset_frames = librosa.onset.onset_detect(y=audio, sr=sr)
|
||||
speaking_rate = len(onset_frames) / duration if duration > 0 else 0.0
|
||||
|
||||
return VocalAnalysis(
|
||||
emotion=emotion, emotion_confidence=round(confidence, 4),
|
||||
emotion_scores=emotion_scores,
|
||||
pitch_mean_hz=round(pitch_mean, 2) if pitch_mean else None,
|
||||
pitch_variance=round(pitch_var, 2) if pitch_var else None,
|
||||
energy_rms=round(energy_rms, 4),
|
||||
speaking_rate_approx=round(speaking_rate, 2),
|
||||
voiced_fraction=round(voiced_fraction, 4),
|
||||
duration_seconds=round(duration, 3),
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.exception(f"Analysis failed: {e}")
|
||||
return VocalAnalysis(
|
||||
emotion="neutral", emotion_confidence=0.0, emotion_scores={},
|
||||
pitch_mean_hz=None, pitch_variance=None, energy_rms=0.0,
|
||||
speaking_rate_approx=0.0, voiced_fraction=0.0,
|
||||
duration_seconds=0.0, analysis_error=str(e),
|
||||
)
|
||||
@@ -0,0 +1,66 @@
|
||||
"""
|
||||
FastAPI service wrapping VocalAnalyzer.
|
||||
Internal-only -- not exposed via Traefik.
|
||||
"""
|
||||
import logging
|
||||
import os
|
||||
from contextlib import asynccontextmanager
|
||||
from fastapi import FastAPI, UploadFile, File, HTTPException
|
||||
from fastapi.responses import JSONResponse
|
||||
from dataclasses import asdict
|
||||
|
||||
from analyzer import VocalAnalyzer
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MODEL_DIR = os.getenv("MODEL_DIR", "/app/models")
|
||||
analyzer: VocalAnalyzer = None
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
global analyzer
|
||||
logger.info("Starting VocalAnalyzer...")
|
||||
analyzer = VocalAnalyzer(model_dir=MODEL_DIR)
|
||||
logger.info("VocalAnalyzer ready.")
|
||||
yield
|
||||
logger.info("Shutting down.")
|
||||
|
||||
|
||||
app = FastAPI(title="SpeechBrain Vocal Analysis Service", lifespan=lifespan)
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health():
|
||||
if analyzer is None:
|
||||
raise HTTPException(status_code=503, detail="Model not loaded")
|
||||
return {"status": "ok", "model": "emotion-recognition-wav2vec2-IEMOCAP"}
|
||||
|
||||
|
||||
@app.post("/analyze")
|
||||
async def analyze(audio: UploadFile = File(...)):
|
||||
if analyzer is None:
|
||||
raise HTTPException(status_code=503, detail="Model not loaded")
|
||||
|
||||
content = await audio.read()
|
||||
if len(content) == 0:
|
||||
raise HTTPException(status_code=400, detail="Empty audio file")
|
||||
|
||||
result = analyzer.analyze(content, content_type=audio.content_type or "audio/wav")
|
||||
return JSONResponse(content=asdict(result))
|
||||
|
||||
|
||||
@app.post("/calibrate")
|
||||
async def calibrate(audio: UploadFile = File(...)):
|
||||
if analyzer is None:
|
||||
raise HTTPException(status_code=503, detail="Model not loaded")
|
||||
|
||||
content = await audio.read()
|
||||
if len(content) == 0:
|
||||
raise HTTPException(status_code=400, detail="Empty audio file")
|
||||
|
||||
result = analyzer.analyze(content, content_type=audio.content_type or "audio/wav")
|
||||
result_dict = asdict(result)
|
||||
result_dict["is_baseline"] = True
|
||||
return JSONResponse(content=result_dict)
|
||||
@@ -0,0 +1,12 @@
|
||||
fastapi==0.115.0
|
||||
uvicorn[standard]==0.30.0
|
||||
speechbrain==1.0.0
|
||||
huggingface_hub==0.19.4
|
||||
transformers==4.40.2
|
||||
torch==2.3.0
|
||||
torchaudio==2.3.0
|
||||
librosa==0.10.2
|
||||
soundfile==0.12.1
|
||||
numpy==1.26.4
|
||||
httpx==0.27.0
|
||||
python-multipart==0.0.9
|
||||
@@ -0,0 +1,8 @@
|
||||
FROM nginx:alpine
|
||||
|
||||
COPY index.html /usr/share/nginx/html/
|
||||
COPY app.js /usr/share/nginx/html/
|
||||
COPY style.css /usr/share/nginx/html/
|
||||
COPY nginx.conf /etc/nginx/conf.d/default.conf
|
||||
|
||||
EXPOSE 80
|
||||
@@ -0,0 +1,158 @@
|
||||
const LIVEKIT_URL = "wss://voice.hobbs.farm";
|
||||
let room = null;
|
||||
|
||||
function log(msg, level) {
|
||||
const el = document.getElementById("logOutput");
|
||||
const time = new Date().toLocaleTimeString();
|
||||
const 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 = "";
|
||||
}
|
||||
|
||||
async function connect() {
|
||||
setStatus("connecting", "Connecting...");
|
||||
log("Requesting token from server...");
|
||||
try {
|
||||
const response = await fetch("/api/token", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ identity: "jeremy-" + Date.now() }),
|
||||
});
|
||||
if (!response.ok) {
|
||||
const text = await response.text();
|
||||
throw new Error("Token request failed: " + response.status + " " + text);
|
||||
}
|
||||
const data = await response.json();
|
||||
log("Token received. LiveKit URL: " + data.url);
|
||||
|
||||
room = new LivekitClient.Room({
|
||||
audioCaptureDefaults: {
|
||||
echoCancellation: true,
|
||||
noiseSuppression: true,
|
||||
autoGainControl: true,
|
||||
},
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.Connected, () => {
|
||||
setStatus("connected", "Connected");
|
||||
document.getElementById("connectBtn").style.display = "none";
|
||||
document.getElementById("disconnectBtn").style.display = "block";
|
||||
log("Connected to LiveKit room");
|
||||
log("Participants in room: " + room.remoteParticipants.size);
|
||||
if (room.remoteParticipants.size === 0) {
|
||||
log("No agent participant yet - waiting for voicecoach-agent to join...", "warn");
|
||||
}
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.Disconnected, (reason) => {
|
||||
setStatus("disconnected", "Disconnected");
|
||||
document.getElementById("connectBtn").style.display = "block";
|
||||
document.getElementById("disconnectBtn").style.display = "none";
|
||||
log("Disconnected from LiveKit" + (reason ? ": " + reason : ""), "warn");
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.ParticipantConnected, (participant) => {
|
||||
log("Participant joined: " + participant.identity);
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.ParticipantDisconnected, (participant) => {
|
||||
log("Participant left: " + participant.identity, "warn");
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.TrackSubscribed, (track, publication, participant) => {
|
||||
log("Track subscribed: " + track.kind + " from " + participant.identity);
|
||||
if (track.kind === LivekitClient.Track.Kind.Audio) {
|
||||
const element = track.attach();
|
||||
document.body.appendChild(element);
|
||||
element.play().then(() => {
|
||||
log("Audio playback started");
|
||||
}).catch((err) => {
|
||||
log("Audio play failed: " + err.message, "error");
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.TrackPublished, (publication, participant) => {
|
||||
log("Track published by " + participant.identity + ": " + publication.kind);
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.TrackUnsubscribed, (track, publication, participant) => {
|
||||
log("Track unsubscribed: " + track.kind + " from " + participant.identity, "warn");
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.DataReceived, (payload, participant) => {
|
||||
try {
|
||||
const msg = new TextDecoder().decode(payload);
|
||||
log("Data from " + (participant ? participant.identity : "server") + ": " + msg);
|
||||
} catch (e) {
|
||||
log("Data received (binary, " + payload.byteLength + " bytes)");
|
||||
}
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.ConnectionQualityChanged, (quality, participant) => {
|
||||
if (participant.isLocal) {
|
||||
log("Connection quality: " + quality);
|
||||
}
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.MediaDevicesError, (err) => {
|
||||
log("Media device error: " + err.message, "error");
|
||||
});
|
||||
|
||||
room.on(LivekitClient.RoomEvent.RoomMetadataChanged, (metadata) => {
|
||||
log("Room metadata changed: " + metadata);
|
||||
});
|
||||
|
||||
log("Connecting to " + LIVEKIT_URL + "...");
|
||||
await room.connect(data.url || LIVEKIT_URL, data.token);
|
||||
|
||||
log("Enabling microphone...");
|
||||
await room.localParticipant.setMicrophoneEnabled(true);
|
||||
log("Microphone enabled. Speak now.");
|
||||
|
||||
// Log local tracks
|
||||
room.localParticipant.audioTrackPublications.forEach((pub) => {
|
||||
log("Local audio track: " + pub.trackSid + " muted=" + pub.isMuted);
|
||||
});
|
||||
|
||||
} catch (err) {
|
||||
setStatus("disconnected", "Error: " + err.message);
|
||||
log("Connection error: " + err.message, "error");
|
||||
console.error(err);
|
||||
}
|
||||
}
|
||||
|
||||
async function disconnect() {
|
||||
if (room) {
|
||||
log("Disconnecting...");
|
||||
await room.disconnect();
|
||||
room = null;
|
||||
}
|
||||
}
|
||||
|
||||
function setMode(mode) {
|
||||
document.getElementById("modeDisplay").textContent = "Mode: " + mode;
|
||||
log("Mode changed to: " + mode);
|
||||
if (room && room.localParticipant) {
|
||||
const encoder = new TextEncoder();
|
||||
room.localParticipant.publishData(
|
||||
encoder.encode(JSON.stringify({ type: "mode_change", mode })),
|
||||
{ reliable: true }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
function setStatus(state, text) {
|
||||
const el = document.getElementById("status");
|
||||
el.className = "status " + state;
|
||||
el.textContent = text;
|
||||
}
|
||||
|
||||
document.getElementById("connectBtn").addEventListener("click", connect);
|
||||
document.getElementById("disconnectBtn").addEventListener("click", disconnect);
|
||||
|
||||
log("VoiceCoach client loaded. Ready to connect.");
|
||||
@@ -0,0 +1,46 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0">
|
||||
<title>VoiceCoach</title>
|
||||
<link rel="stylesheet" href="style.css">
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<div class="header">
|
||||
<h1>VoiceCoach</h1>
|
||||
<div id="status" class="status disconnected">Disconnected</div>
|
||||
</div>
|
||||
|
||||
<div class="controls">
|
||||
<button id="connectBtn" class="btn btn-primary">Start Session</button>
|
||||
<button id="disconnectBtn" class="btn btn-danger" style="display:none">End Session</button>
|
||||
</div>
|
||||
|
||||
<div id="modeDisplay" class="mode-display">Mode: coaching</div>
|
||||
|
||||
<div id="transcript" class="transcript">
|
||||
<p class="hint">Press "Start Session" to connect your voice.</p>
|
||||
</div>
|
||||
|
||||
<div class="mode-buttons">
|
||||
<button class="mode-btn" onclick="setMode('coaching mode')">Coaching</button>
|
||||
<button class="mode-btn" onclick="setMode('vent mode')">Vent</button>
|
||||
<button class="mode-btn" onclick="setMode('practice mode')">Practice</button>
|
||||
<button class="mode-btn" onclick="setMode('briefing mode')">Briefing</button>
|
||||
</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>
|
||||
<script src="app.js"></script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,15 @@
|
||||
server {
|
||||
listen 80;
|
||||
root /usr/share/nginx/html;
|
||||
index index.html;
|
||||
|
||||
location /api/ {
|
||||
proxy_pass http://pipecat-agent:8080/;
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Host $host;
|
||||
}
|
||||
|
||||
location / {
|
||||
try_files $uri $uri/ /index.html;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,131 @@
|
||||
* { margin: 0; padding: 0; box-sizing: border-box; }
|
||||
|
||||
body {
|
||||
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
|
||||
background: #1a1a2e;
|
||||
color: #eee;
|
||||
min-height: 100vh;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.container {
|
||||
max-width: 420px;
|
||||
width: 100%;
|
||||
padding: 24px;
|
||||
}
|
||||
|
||||
.header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
margin-bottom: 24px;
|
||||
}
|
||||
|
||||
h1 { font-size: 24px; font-weight: 600; }
|
||||
|
||||
.status {
|
||||
padding: 4px 12px;
|
||||
border-radius: 12px;
|
||||
font-size: 12px;
|
||||
font-weight: 600;
|
||||
text-transform: uppercase;
|
||||
}
|
||||
.status.disconnected { background: #444; color: #aaa; }
|
||||
.status.connecting { background: #553; color: #ffa; }
|
||||
.status.connected { background: #253; color: #5f5; }
|
||||
|
||||
.controls { margin-bottom: 20px; }
|
||||
|
||||
.btn {
|
||||
width: 100%;
|
||||
padding: 14px;
|
||||
border: none;
|
||||
border-radius: 8px;
|
||||
font-size: 16px;
|
||||
font-weight: 600;
|
||||
cursor: pointer;
|
||||
transition: opacity 0.2s;
|
||||
}
|
||||
.btn:hover { opacity: 0.85; }
|
||||
.btn-primary { background: #4a6cf7; color: #fff; }
|
||||
.btn-danger { background: #f74a4a; color: #fff; }
|
||||
|
||||
.mode-display {
|
||||
text-align: center;
|
||||
font-size: 13px;
|
||||
color: #888;
|
||||
margin-bottom: 16px;
|
||||
}
|
||||
|
||||
.transcript {
|
||||
background: #16213e;
|
||||
border-radius: 8px;
|
||||
padding: 16px;
|
||||
min-height: 200px;
|
||||
max-height: 400px;
|
||||
overflow-y: auto;
|
||||
margin-bottom: 16px;
|
||||
}
|
||||
.transcript .hint { color: #666; font-style: italic; }
|
||||
|
||||
.mode-buttons {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 8px;
|
||||
}
|
||||
.mode-btn {
|
||||
padding: 10px;
|
||||
background: #16213e;
|
||||
border: 1px solid #333;
|
||||
border-radius: 6px;
|
||||
color: #ccc;
|
||||
font-size: 13px;
|
||||
cursor: pointer;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
.mode-btn:hover { background: #1a2744; border-color: #4a6cf7; }
|
||||
|
||||
.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; }
|
||||
Reference in New Issue
Block a user