Billy D.
8fc5eb1193
- stt_streaming.py: HTTP-based STT using external Whisper service - stt_streaming_local.py: ROCm-based local Whisper inference - Voice Activity Detection (VAD) with WebRTC - Interrupt detection for barge-in support - Session state management (listening/responding) - OpenTelemetry instrumentation with HyperDX support - Dockerfile variants for HTTP and ROCm deployments
53 lines
2.0 KiB
Docker
53 lines
2.0 KiB
Docker
# STT Streaming Service with ROCm for AMD GPU Whisper inference
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# Targets AMD Strix Halo (gfx1151 / RDNA 3.5) but includes RDNA 3 compatibility
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#
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# Uses OpenAI Whisper with PyTorch ROCm backend
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#
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FROM docker.io/rocm/pytorch:rocm7.1_ubuntu24.04_py3.12_pytorch_release_2.9.1 AS base
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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ffmpeg \
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libsndfile1 \
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&& rm -rf /var/lib/apt/lists/*
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# WORKAROUND: ROCm/ROCm#5853 - Standard PyTorch ROCm wheels cause segfault in
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# libhsa-runtime64.so during VRAM allocation on gfx1151 (Strix Halo).
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# TheRock nightly builds work correctly. Install BEFORE other deps since
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# openai-whisper depends on torch.
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RUN pip install --no-cache-dir --break-system-packages \
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--index-url https://rocm.nightlies.amd.com/v2/gfx1151/ \
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torch torchaudio torchvision --force-reinstall
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# Install Python dependencies for STT streaming
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# Use pip directly (more reliable than uv in this base image)
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COPY requirements-rocm.txt .
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RUN pip install --no-cache-dir --break-system-packages -r requirements-rocm.txt
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# Download Whisper model at build time for faster startup
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# Using medium model for good accuracy/speed balance
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ARG WHISPER_MODEL=medium
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ENV WHISPER_MODEL_SIZE=${WHISPER_MODEL}
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# Pre-download the model during build (whisper is installed as openai-whisper)
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# Use python3 to ensure correct interpreter
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RUN python3 -c "import whisper; whisper.load_model('${WHISPER_MODEL}')" || echo "Model will be downloaded at runtime"
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# Copy application code
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COPY stt_streaming_local.py .
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COPY healthcheck.py .
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# Set ROCm environment for AMD Strix Halo (gfx1151 / RDNA 3.5)
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ENV HIP_VISIBLE_DEVICES=0
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ENV HSA_ENABLE_SDMA=0
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# Ensure PyTorch uses ROCm with expandable segments for large models
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ENV PYTORCH_HIP_ALLOC_CONF=expandable_segments:True,max_split_size_mb:512
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# Target gfx1151 (Strix Halo) - ROCm 7.1+ has native support
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# Falls back to runtime override if kernels not available
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ENV ROCM_TARGET_LST=gfx1151,gfx1100
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# Run the service
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CMD ["python", "stt_streaming_local.py"]
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