Phase 4 STT pipeline implemented — Silero VAD + faster-whisper — still not working well at all
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STT_VOICE_TESTING.md
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STT_VOICE_TESTING.md
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# STT Voice Testing Guide
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## Phase 4B: Bot-Side STT Integration - COMPLETE ✅
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All code has been deployed to containers. Ready for testing!
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## Architecture Overview
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```
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Discord Voice (User) → Opus 48kHz stereo
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↓
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VoiceReceiver.write()
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↓
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Opus decode → Stereo-to-mono → Resample to 16kHz
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↓
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STTClient.send_audio() → WebSocket
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↓
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miku-stt:8001 (Silero VAD + Faster-Whisper)
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↓
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JSON events (vad, partial, final, interruption)
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↓
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VoiceReceiver callbacks → voice_manager
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↓
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on_final_transcript() → _generate_voice_response()
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↓
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LLM streaming → TTS tokens → Audio playback
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```
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## New Voice Commands
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### 1. Start Listening
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```
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!miku listen
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```
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- Starts listening to **your** voice in the current voice channel
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- You must be in the same channel as Miku
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- Miku will transcribe your speech and respond with voice
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```
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!miku listen @username
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```
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- Start listening to a specific user's voice
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- Useful for moderators or testing with multiple users
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### 2. Stop Listening
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```
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!miku stop-listening
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```
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- Stop listening to your voice
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- Miku will no longer transcribe or respond to your speech
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```
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!miku stop-listening @username
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```
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- Stop listening to a specific user
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## Testing Procedure
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### Test 1: Basic STT Connection
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1. Join a voice channel
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2. `!miku join` - Miku joins your channel
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3. `!miku listen` - Start listening to your voice
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4. Check bot logs for "Started listening to user"
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5. Check STT logs: `docker logs miku-stt --tail 50`
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- Should show: "WebSocket connection from user {user_id}"
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- Should show: "Session started for user {user_id}"
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### Test 2: VAD Detection
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1. After `!miku listen`, speak into your microphone
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2. Say something like: "Hello Miku, can you hear me?"
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3. Check STT logs for VAD events:
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```
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[DEBUG] VAD: speech_start probability=0.85
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[DEBUG] VAD: speaking probability=0.92
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[DEBUG] VAD: speech_end probability=0.15
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```
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4. Bot logs should show: "VAD event for user {id}: speech_start/speaking/speech_end"
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### Test 3: Transcription
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1. Speak clearly into microphone: "Hey Miku, tell me a joke"
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2. Watch bot logs for:
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- "Partial transcript from user {id}: Hey Miku..."
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- "Final transcript from user {id}: Hey Miku, tell me a joke"
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3. Miku should respond with LLM-generated speech
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4. Check channel for: "🎤 Miku: *[her response]*"
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### Test 4: Interruption Detection
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1. `!miku listen`
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2. `!miku say Tell me a very long story about your favorite song`
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3. While Miku is speaking, start talking yourself
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4. Speak loudly enough to trigger VAD (probability > 0.7)
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5. Expected behavior:
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- Miku's audio should stop immediately
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- Bot logs: "User {id} interrupted Miku (probability={prob})"
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- STT logs: "Interruption detected during TTS playback"
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- RVC logs: "Interrupted: Flushed {N} ZMQ chunks"
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### Test 5: Multi-User (if available)
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1. Have two users join voice channel
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2. `!miku listen @user1` - Listen to first user
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3. `!miku listen @user2` - Listen to second user
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4. Both users speak separately
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5. Verify Miku responds to each user individually
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6. Check STT logs for multiple active sessions
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## Logs to Monitor
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### Bot Logs
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```bash
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docker logs -f miku-bot | grep -E "(listen|STT|transcript|interrupt)"
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```
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Expected output:
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```
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[INFO] Started listening to user 123456789 (username)
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[DEBUG] VAD event for user 123456789: speech_start
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[DEBUG] Partial transcript from user 123456789: Hello Miku...
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[INFO] Final transcript from user 123456789: Hello Miku, how are you?
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[INFO] User 123456789 interrupted Miku (probability=0.82)
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```
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### STT Logs
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```bash
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docker logs -f miku-stt
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```
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Expected output:
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```
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[INFO] WebSocket connection from user_123456789
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[INFO] Session started for user 123456789
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[DEBUG] Received 320 audio samples from user_123456789
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[DEBUG] VAD speech_start: probability=0.87
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[INFO] Transcribing audio segment (duration=2.5s)
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[INFO] Final transcript: "Hello Miku, how are you?"
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```
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### RVC Logs (for interruption)
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```bash
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docker logs -f miku-rvc-api | grep -i interrupt
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```
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Expected output:
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```
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[INFO] Interrupted: Flushed 15 ZMQ chunks, cleared 48000 RVC buffer samples
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```
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## Component Status
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### ✅ Completed
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- [x] STT container running (miku-stt:8001)
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- [x] Silero VAD on CPU with chunk buffering
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- [x] Faster-Whisper on GTX 1660 (1.3GB VRAM)
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- [x] STTClient WebSocket client
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- [x] VoiceReceiver Discord audio sink
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- [x] VoiceSession STT integration
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- [x] listen/stop-listening commands
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- [x] /interrupt endpoint in RVC API
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- [x] LLM response generation from transcripts
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- [x] Interruption detection and cancellation
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### ⏳ Pending Testing
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- [ ] Basic STT connection test
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- [ ] VAD speech detection test
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- [ ] End-to-end transcription test
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- [ ] LLM voice response test
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- [ ] Interruption cancellation test
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- [ ] Multi-user testing (if available)
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### 🔧 Configuration Tuning (after testing)
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- VAD sensitivity (currently threshold=0.5)
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- VAD timing (min_speech=250ms, min_silence=500ms)
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- Interruption threshold (currently 0.7)
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- Whisper beam size and patience
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- LLM streaming chunk size
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## API Endpoints
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### STT Container (port 8001)
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- WebSocket: `ws://localhost:8001/ws/stt/{user_id}`
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- Health: `http://localhost:8001/health`
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### RVC Container (port 8765)
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- WebSocket: `ws://localhost:8765/ws/stream`
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- Interrupt: `http://localhost:8765/interrupt` (POST)
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- Health: `http://localhost:8765/health`
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## Troubleshooting
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### No audio received from Discord
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- Check bot logs for "write() called with data"
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- Verify user is in same voice channel as Miku
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- Check Discord permissions (View Channel, Connect, Speak)
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### VAD not detecting speech
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- Check chunk buffer accumulation in STT logs
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- Verify audio format: PCM int16, 16kHz mono
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- Try speaking louder or more clearly
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- Check VAD threshold (may need adjustment)
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### Transcription empty or gibberish
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- Verify Whisper model loaded (check STT startup logs)
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- Check GPU VRAM usage: `nvidia-smi`
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- Ensure audio segments are at least 1-2 seconds long
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- Try speaking more clearly with less background noise
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### Interruption not working
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- Verify Miku is actually speaking (check miku_speaking flag)
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- Check VAD probability in logs (must be > 0.7)
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- Verify /interrupt endpoint returns success
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- Check RVC logs for flushed chunks
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### Multiple users causing issues
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- Check STT logs for per-user session management
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- Verify each user has separate STTClient instance
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- Check for resource contention on GTX 1660
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## Next Steps After Testing
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### Phase 4C: LLM KV Cache Precomputation
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- Use partial transcripts to start LLM generation early
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- Precompute KV cache for common phrases
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- Reduce latency between speech end and response start
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### Phase 4D: Multi-User Refinement
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- Queue management for multiple simultaneous speakers
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- Priority system for interruptions
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- Resource allocation for multiple Whisper requests
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### Phase 4E: Latency Optimization
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- Profile each stage of the pipeline
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- Optimize audio chunk sizes
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- Reduce WebSocket message overhead
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- Tune Whisper beam search parameters
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- Implement VAD lookahead for quicker detection
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## Hardware Utilization
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### Current Allocation
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- **AMD RX 6800**: LLaMA text models (idle during listen/speak)
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- **GTX 1660**:
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- Listen phase: Faster-Whisper (1.3GB VRAM)
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- Speak phase: Soprano TTS + RVC (time-multiplexed)
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- **CPU**: Silero VAD, audio preprocessing
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### Expected Performance
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- VAD latency: <50ms (CPU processing)
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- Transcription latency: 200-500ms (Whisper inference)
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- LLM streaming: 20-30 tokens/sec (RX 6800)
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- TTS synthesis: Real-time (GTX 1660)
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- Total latency (speech → response): 1-2 seconds
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## Testing Checklist
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Before marking Phase 4B as complete:
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- [ ] Test basic STT connection with `!miku listen`
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- [ ] Verify VAD detects speech start/end correctly
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- [ ] Confirm transcripts are accurate and complete
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- [ ] Test LLM voice response generation works
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- [ ] Verify interruption cancels TTS playback
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- [ ] Check multi-user handling (if possible)
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- [ ] Verify resource cleanup on `!miku stop-listening`
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- [ ] Test edge cases (silence, background noise, overlapping speech)
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- [ ] Profile latencies at each stage
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- [ ] Document any configuration tuning needed
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---
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**Status**: Code deployed, ready for user testing! 🎤🤖
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