IMPLEMENTATION_NOTES.md

Implementation Notes

Architecture Overview

The STT system is built in 5 progressive steps, each adding functionality on top of the previous:

1. Step 1: Basic offline transcription (Whisper/Vosk)
2. Step 2: HTTP API for file uploads
3. Step 3: WebSocket streaming for real-time audio
4. Step 4: Telephony audio format support (Twilio/Exotel)
5. Step 5: Production-ready with stability features

Key Components

Audio Processing

• TelephonyAudioConverter: Handles format conversion
  • Twilio: 8kHz μ-law → 16kHz PCM
  • Exotel: 8kHz PCM → 16kHz PCM
  • Uses scipy.signal.resample for sample rate conversion

Voice Activity Detection (VAD)

• Simple energy-based VAD in Step 5
• Threshold: 0.01 (configurable)
• Frame-based analysis (25ms frames)
• Detects speech vs silence

Audio Buffering

• AudioBuffer: Accumulates audio chunks
• Configurable chunk duration (default: 1.0s)
• Minimum interval between transcriptions (0.5s)
• Handles silence timeouts (3.0s)

Duplicate Prevention

• Compares new transcriptions with previous
• Prevents sending identical text multiple times
• Simple substring matching (can be enhanced)

Things to Consider

Performance

1. Model Loading: Whisper models are loaded per connection (lazy loading)

   • Consider model caching/pooling for production
   • Larger models (medium/large) are more accurate but slower

2. Chunk Size: Balance between latency and accuracy

   • Smaller chunks = lower latency but less context
   • Larger chunks = better accuracy but higher latency

3. Concurrent Connections: Each connection loads its own model

   • Consider shared model instances for multiple connections
   • Monitor memory usage with many concurrent calls

Audio Quality

1. Sample Rate: Whisper works best with 16kHz

   • Telephony audio (8kHz) must be upsampled
   • Quality may be reduced compared to native 16kHz

2. Noise: Telephony audio often has background noise

   • Consider noise reduction preprocessing
   • VAD helps filter silence but not noise

3. Format Conversion: μ-law to PCM conversion may introduce artifacts

   • Test with real telephony audio
   • Consider alternative conversion methods if quality is poor

Stability & Reliability

1. Disconnections: Handled gracefully in Step 5

   • Final transcription on remaining buffer
   • Session cleanup on disconnect

2. Error Handling: Comprehensive error catching

   • Logs errors per call
   • Continues processing on individual failures

3. Logging: Per-call logging in Step 5

   • Logs stored in logs/stt.log
   • Includes call_id for tracking

Scaling Considerations

1. Model Memory: Whisper models are large (base ~150MB, large ~3GB)

   • Consider GPU acceleration for faster inference
   • Model quantization for reduced memory

2. API Rate Limiting: No rate limiting implemented

   • Add rate limiting for production
   • Consider request queuing

3. Database: No persistent storage

   • Add database for call transcripts
   • Store session metadata

4. Load Balancing: Single server implementation

   • Consider multiple workers/instances
   • Use message queue for audio processing

Security

1. Authentication: No authentication implemented

   • Add API keys/tokens
   • WebSocket authentication

2. Input Validation: Basic validation

   • Validate audio format/size
   • Rate limit per client

3. Data Privacy: Transcripts logged

   • Consider encryption for sensitive data
   • Implement data retention policies

Testing Recommendations

1. Unit Tests: Test audio conversion functions
2. Integration Tests: Test WebSocket streaming with real audio
3. Load Tests: Test with multiple concurrent connections
4. Telephony Tests: Test with actual Twilio/Exotel audio streams

Future Enhancements

1. Better VAD: Use more sophisticated VAD (e.g., WebRTC VAD)
2. Streaming Model: Use streaming-capable models for lower latency
3. Language Detection: Auto-detect language
4. Speaker Diarization: Identify different speakers
5. Punctuation: Better punctuation in transcripts
6. Timestamping: Word-level timestamps
7. Confidence Scores: Return confidence scores per word