atlas/home-voice-agent/asr

ASR (Automatic Speech Recognition) Service

Speech-to-text service using faster-whisper for real-time transcription.

Features

• HTTP endpoint for file transcription
• WebSocket endpoint for streaming transcription
• Support for multiple audio formats (WAV, MP3, FLAC, etc.)
• Auto language detection
• Low-latency processing
• GPU acceleration support (CUDA)

Installation

# Install Python dependencies
pip install -r requirements.txt

# For GPU support (optional)
# CUDA toolkit must be installed
# faster-whisper will use GPU automatically if available

Usage

Standalone Service

# Run as HTTP/WebSocket server
python3 -m asr.server

# Or use uvicorn directly
uvicorn asr.server:app --host 0.0.0.0 --port 8001

Python API

from asr.service import ASRService

service = ASRService(
    model_size="small",
    device="cpu",  # or "cuda" for GPU
    language="en"
)

# Transcribe file
with open("audio.wav", "rb") as f:
    result = service.transcribe_file(f.read())
    print(result["text"])

API Endpoints

HTTP

• GET /health - Health check
• POST /transcribe - Transcribe audio file
  • audio: Audio file (multipart/form-data)
  • language: Language code (optional)
  • format: Response format ("text" or "json")
• GET /languages - Get supported languages

WebSocket

• WS /stream - Streaming transcription
  • Send audio chunks (binary)
  • Send {"action": "end"} to finish
  • Receive partial and final results

Configuration

• Model Size: small (default), tiny, base, medium, large
• Device: cpu (default), cuda (if GPU available)
• Compute Type: int8 (default), int8_float16, float16, float32
• Language: en (default), or None for auto-detect

Performance

• CPU (small model): ~2-4s latency
• GPU (small model): ~0.5-1s latency
• GPU (medium model): ~1-2s latency

Integration

The ASR service is triggered by:

1. Wake-word detection events
2. Direct HTTP/WebSocket requests
3. Audio file uploads

Output is sent to:

1. LLM for processing
2. Conversation manager
3. Response generation

Testing

# Test health
curl http://localhost:8001/health

# Test transcription
curl -X POST http://localhost:8001/transcribe \
  -F "audio=@test.wav" \
  -F "language=en" \
  -F "format=json"

Notes

• First run downloads the model (~500MB for small)
• GPU acceleration requires CUDA
• Streaming transcription needs proper audio format handling
• Supports many languages (see /languages endpoint)