63 lines
2.5 KiB
Plaintext
63 lines
2.5 KiB
Plaintext
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# VideoTranscriber Docker Configuration
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# Copy this file to .env and modify the values as needed
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# =============================================================================
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# DOCKER VOLUME PATHS (Host Directories)
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# =============================================================================
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# Path to your video files directory on the host
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# This directory will be mounted into the container at /app/data/videos
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VIDEO_PATH=./videos
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# Path where outputs (transcripts, summaries) will be saved on the host
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# This directory will be mounted into the container at /app/data/outputs
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OUTPUT_PATH=./outputs
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# Path for caching ML models and processed files (improves performance)
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# This directory will be mounted into the container at /app/data/cache
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CACHE_PATH=./cache
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# Optional: Configuration directory for custom settings
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CONFIG_PATH=./config
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# =============================================================================
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# OLLAMA CONFIGURATION
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# =============================================================================
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# Ollama API URL - how the container accesses your host Ollama service
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# For Windows/Mac with Docker Desktop: use host.docker.internal
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# For Linux: use host networking or the actual host IP
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OLLAMA_API_URL=http://host.docker.internal:11434/api
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# =============================================================================
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# ML MODEL CONFIGURATION
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# =============================================================================
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# HuggingFace token for advanced features (speaker diarization, etc.)
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# Get your token at: https://huggingface.co/settings/tokens
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# Leave empty if not using advanced features
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HF_TOKEN=
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# GPU Configuration
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# Specify which GPU devices to use (leave empty for all available)
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# Examples: "0" for first GPU, "0,1" for first two GPUs
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CUDA_VISIBLE_DEVICES=
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# =============================================================================
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# DOCKER-SPECIFIC SETTINGS
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# =============================================================================
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# Container name (change if you want to run multiple instances)
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CONTAINER_NAME=videotranscriber
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# Port mapping (host:container)
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HOST_PORT=8501
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# =============================================================================
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# EXAMPLE USAGE
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# =============================================================================
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# 1. Copy this file: cp docker.env.example .env
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# 2. Edit the paths to match your system
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# 3. Make sure Ollama is running on your host: ollama serve
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# 4. Start the container: docker-compose up -d
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# 5. Access the app at: http://localhost:8501
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