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Merge pull request #114 from needabetterusername/implement-69-clean
(Re-) Implement #69 (Dockerfile)
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82
Dockerfile
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82
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FROM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu22.04
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ENV DEBIAN_FRONTEND=noninteractive
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ENV PYTHONUNBUFFERED=1
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WORKDIR /app
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ARG EXTRAS
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ARG HF_PRECACHE_DIR
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ARG HF_TKN_FILE
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# Install system dependencies
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#RUN apt-get update && \
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# apt-get install -y ffmpeg git && \
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# apt-get clean && \
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# rm -rf /var/lib/apt/lists/*
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# 2) Install system dependencies + Python + pip
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RUN apt-get update && \
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apt-get install -y --no-install-recommends \
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python3 \
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python3-pip \
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ffmpeg \
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git && \
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rm -rf /var/lib/apt/lists/*
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RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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COPY . .
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# Install WhisperLiveKit directly, allowing for optional dependencies
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# Note: For gates modedls, need to add your HF toke. See README.md
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# for more details.
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RUN if [ -n "$EXTRAS" ]; then \
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echo "Installing with extras: [$EXTRAS]"; \
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pip install --no-cache-dir .[$EXTRAS]; \
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else \
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echo "Installing base package only"; \
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pip install --no-cache-dir .; \
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fi
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# Enable in-container caching for Hugging Face models by:
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# Note: If running multiple containers, better to map a shared
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# bucket.
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#
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# A) Make the cache directory persistent via an anonymous volume.
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# Note: This only persists for a single, named container. This is
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# only for convenience at de/test stage.
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# For prod, it is better to use a named volume via host mount/k8s.
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VOLUME ["/root/.cache/huggingface/hub"]
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# or
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# B) Conditionally copy a local pre-cache from the build context to the
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# container's cache via the HF_PRECACHE_DIR build-arg.
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# WARNING: This will copy ALL files in the pre-cache location.
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# Conditionally copy a cache directory if provided
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RUN if [ -n "$HF_PRECACHE_DIR" ]; then \
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echo "Copying Hugging Face cache from $HF_PRECACHE_DIR"; \
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mkdir -p /root/.cache/huggingface/hub && \
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cp -r $HF_PRECACHE_DIR/* /root/.cache/huggingface/hub; \
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else \
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echo "No local Hugging Face cache specified, skipping copy"; \
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fi
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# Conditionally copy a Hugging Face token if provided
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RUN if [ -n "$HF_TKN_FILE" ]; then \
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echo "Copying Hugging Face token from $HF_TKN_FILE"; \
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mkdir -p /root/.cache/huggingface && \
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cp $HF_TKN_FILE /root/.cache/huggingface/token; \
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else \
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echo "No Hugging Face token file specified, skipping token setup"; \
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fi
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# Expose port for the transcription server
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EXPOSE 8000
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ENTRYPOINT ["whisperlivekit-server", "--host", "0.0.0.0"]
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# Default args
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CMD ["--model", "tiny.en"]
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29
README.md
29
README.md
@@ -250,6 +250,35 @@ To deploy WhisperLiveKit in production:
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4. **HTTPS Support**: For secure deployments, use "wss://" instead of "ws://" in WebSocket URL
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### 🐋 Docker
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A basic Dockerfile is provided which allows re-use of python package installation options. See below useage examples:
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***NOTE:** For **larger** models, ensure that your **docker runtime** has enough **memory** available.*
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#### All defaults
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- Create a reuseable image with only the basics and then run as a named container.
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```bash
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docker build -t whisperlivekit-defaults .
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docker create --gpus all --name whisperlivekit -p 8000:8000 whisperlivekit-defaults
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docker start -i whisperlivekit
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```
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> **Note**: If you're running on a system without NVIDIA GPU support (such as Mac with Apple Silicon or any system without CUDA capabilities), you need to **remove the `--gpus all` flag** from the `docker create` command. Without GPU acceleration, transcription will use CPU only, which may be significantly slower. Consider using small models for better performance on CPU-only systems.
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#### Customization
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- Customise the container options.
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```bash
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docker build -t whisperlivekit-defaults .
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docker create --gpus all --name whisperlivekit-base -p 8000:8000 whisperlivekit-defaults --model base
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docker start -i whisperlivekit-base
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```
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- `--build-arg` Options
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- `EXTRAS="whisper-timestamped"` - Add extras to the image's installation (no spaces). Remember to set necessary container options!
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- `HF_PRECACHE_DIR=./.cache/` - Pre-load a model cache for faster first-time start
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- `HF_TOKEN=./token` - Add your Hugging Face Hub access token to download gated models
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## 🔮 Use Cases
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- **Meeting Transcription**: Capture discussions in real-time
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