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Peter Michael Gits
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Initial commit: STT GPU Service Python v4 with WebSocket streaming
16b78bc | # 🎙️ Real-Time Speech-to-Text Service with Kyutai Moshi | |
| Just built a production-ready STT service using Kyutai's Moshi model for ultra-low latency speech recognition! | |
| ## System Architecture | |
| ``` | |
| ┌─────────────────────────────────────────────────────────────┐ | |
| │ stt-gpu-service-python-v4 │ | |
| │ (Nvidia T4 Small) │ | |
| │ │ | |
| │ ┌─────────────────┐ ┌─────────────────────────────────┐ │ | |
| │ │ Moshi Model │ │ API Interfaces │ │ | |
| │ │ kyutai/stt-1b │ │ │ │ | |
| │ │ (Cached) │ │ 🌐 WebSocket /ws/stream │ │ | |
| │ │ │ │ ↓ 80ms audio chunks │ │ | |
| │ │ • 0.5s delay │◄───┤ ↑ Real-time transcription │ │ | |
| │ │ • EN/FR │ │ │ │ | |
| │ │ • 1B params │ │ 📡 REST /transcribe │ │ | |
| │ │ │ │ ↓ Audio file upload │ │ | |
| │ └─────────────────┘ │ ↑ JSON transcription │ │ | |
| │ │ │ │ | |
| │ │ 💓 GET /health │ │ | |
| │ │ ↑ Service status check │ │ | |
| │ └─────────────────────────────────┘ │ | |
| └─────────────────────────────────────────────────────────────┘ | |
| │ | |
| ┌──────────────┼──────────────┐ | |
| │ │ │ | |
| ┌───────▼─────┐ ┌─────▼─────┐ ┌─────▼─────┐ | |
| │ Client 1 │ │ Client 2 │ │ Test │ | |
| │ (Streaming) │ │(Streaming)│ │ (Upload) │ | |
| └─────────────┘ └───────────┘ └───────────┘ | |
| ``` | |
| ## API Interface Details | |
| ### 🌐 **WebSocket Streaming** `/ws/stream` | |
| Primary interface for real-time speech recognition with 80ms audio chunks. Achieves ~200ms end-to-end latency with bidirectional communication for live conversations. | |
| ### 📡 **REST Upload** `/transcribe` | |
| Secondary testing endpoint for complete audio file processing. Simple POST request with audio file returns full transcription with word-level timestamps. | |
| ### 💓 **Health Check** `/health` | |
| Basic service monitoring endpoint for deployment status verification. Returns model readiness and GPU resource availability. | |
| ## Technical Highlights | |
| - **Ultra-Low Latency**: 80ms frame processing with Moshi's native streaming | |
| - **Model Optimization**: Pre-cached in Docker image for instant startup | |
| - **Cost Efficient**: T4 Small GPU with 30-minute auto-sleep | |
| - **Production Ready**: Supports 2 concurrent streaming connections | |
| - **Multi-Language**: English and French recognition support | |
| Perfect for real-time voice applications, live transcription services, and conversational AI systems! | |
| #AI #SpeechRecognition #RealTime #MachineLearning #HuggingFace #Python #Docker |