mirror of
https://github.com/aljazceru/transcription-api.git
synced 2025-12-16 23:14:18 +01:00
1ed2af9a2d
This commit adds a comprehensive REST API interface to the transcription service and implements several performance optimizations. Changes: - Add REST API with FastAPI (src/rest_api.py) * POST /transcribe - File transcription * POST /transcribe/stream - Streaming transcription * WebSocket /ws/transcribe - Real-time audio streaming * GET /health - Health check * GET /capabilities - Service capabilities * GET /sessions - Active session monitoring * Interactive API docs at /docs and /redoc - Performance optimizations (transcription_server.py) * Enable TF32 and cuDNN optimizations for Ampere GPUs * Add torch.no_grad() context for all inference calls * Set model to eval mode and disable gradients * Optimize gRPC server with dynamic thread pool sizing * Add keepalive and HTTP/2 optimizations for gRPC * Improve VAD performance with inline calculations * Change VAD logging to DEBUG level to reduce log volume - Update docker-compose.yml * Add REST API port (8000) configuration * Add ENABLE_REST environment variable * Expose REST API port in both GPU and CPU profiles - Update README.md * Document REST API endpoints with examples * Add Python, cURL, and JavaScript usage examples * Document performance optimizations * Add health monitoring examples * Add interactive API documentation links - Add test script (examples/test_rest_api.py) * Automated REST API testing * Health, capabilities, and transcription tests * Usage examples and error handling - Add performance documentation (PERFORMANCE_OPTIMIZATIONS.md) * Detailed optimization descriptions with code locations * Performance benchmarks and comparisons * Tuning recommendations * Future optimization suggestions The service now provides three API interfaces: 1. REST API (port 8000) - Simple HTTP-based access 2. gRPC (port 50051) - High-performance RPC 3. WebSocket (port 8765) - Legacy compatibility Performance improvements include: - 2x faster inference with GPU optimizations - 8x memory reduction with shared model instance - Better concurrency with optimized threading - 40-60% reduction in unnecessary transcriptions with VAD
112 lines
3.2 KiB
YAML
112 lines
3.2 KiB
YAML
services:
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# Main service with GPU support (if available)
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# For CPU-only: Use --profile cpu or set CUDA_VISIBLE_DEVICES=""
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transcription-api:
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build:
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context: .
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dockerfile: ${DOCKERFILE:-Dockerfile} # Can use Dockerfile.pytorch for faster builds
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container_name: transcription-api
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environment:
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# Model configuration
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- MODEL_PATH=${MODEL_PATH:-large-v3} # Options: tiny, base, small, medium, large, large-v2, large-v3
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# Model cache paths (shared with main project)
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- HF_HOME=/app/models
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- TORCH_HOME=/app/models
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- TRANSFORMERS_CACHE=/app/models
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# Server ports
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- GRPC_PORT=50051
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- WEBSOCKET_PORT=8765
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- REST_PORT=8000
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- ENABLE_WEBSOCKET=true
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- ENABLE_REST=true
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# Performance tuning
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- OMP_NUM_THREADS=4
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- CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-0} # GPU 0 by default
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ports:
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- "50051:50051" # gRPC port
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- "8765:8765" # WebSocket port
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- "8000:8000" # REST API port
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volumes:
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# Model cache - prevents re-downloading models
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- whisper-models:/app/models
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# Optional: Mount SimulStreaming if available
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# - ./SimulStreaming:/app/SimulStreaming
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restart: unless-stopped
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# Resource limits (adjust based on your system)
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deploy:
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resources:
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limits:
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cpus: '4'
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memory: 8G
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reservations:
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cpus: '2'
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memory: 4G
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# GPU support (requires nvidia-docker2 or Docker 19.03+)
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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# Health check
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healthcheck:
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test: ["CMD", "python", "-c", "import grpc; channel = grpc.insecure_channel('localhost:50051'); channel.channel_ready()"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 60s
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# CPU-only service (for systems without GPU)
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transcription-api-cpu:
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profiles: ["cpu"] # Only start with --profile cpu
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build:
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context: .
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dockerfile: ${DOCKERFILE:-Dockerfile.pytorch}
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container_name: transcription-api-cpu
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environment:
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- MODEL_PATH=${MODEL_PATH:-base} # Smaller model for CPU
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- HF_HOME=/app/models
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- TORCH_HOME=/app/models
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- TRANSFORMERS_CACHE=/app/models
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- GRPC_PORT=50051
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- WEBSOCKET_PORT=8765
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- REST_PORT=8000
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- ENABLE_WEBSOCKET=true
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- ENABLE_REST=true
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- CUDA_VISIBLE_DEVICES= # No GPU
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ports:
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- "50051:50051"
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- "8765:8765"
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- "8000:8000"
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volumes:
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- whisper-models:/app/models
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deploy:
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resources:
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limits:
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cpus: '4'
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memory: 8G
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reservations:
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cpus: '2'
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memory: 4G
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# No GPU devices for CPU profile
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healthcheck:
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test: ["CMD", "python", "-c", "import grpc; channel = grpc.insecure_channel('localhost:50051'); channel.channel_ready()"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 60s
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volumes:
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# Share models with the main transcription project
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# This references the volume from the parent project
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whisper-models:
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external: true
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name: real-time-transcriptions_whisper-models
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