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- Python 3.8-3.11 (Python 3.12 is not supported due to removal of pkgutil.ImpImporter)
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- CUDA-compatible GPU (recommended: RTX series for best performance)
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-Separate LLM inference server running the Orpheus model (e.g., LM Studio or llama.cpp server)
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-Using docker compose or separate LLM inference server running the Orpheus model (e.g., LM Studio or llama.cpp server)
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### Installation
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### 🐳 Docker compose
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The docker compose file orchestrates the Orpheus-FastAPI for audio and a llama.cpp inference server for the base model token generation. The GGUF model is downloaded with the model-init service.
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```bash
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cp .env.example .env # Nothing needs to be changed, but the file is required
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```
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```bash
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docker compose up --build
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```
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### FastAPI Service Native Installation
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1. Clone the repository:
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```bash
@@ -271,7 +285,7 @@ You can easily integrate this TTS solution with [OpenWebUI](https://github.com/o
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### External Inference Server
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This application requires a separate LLM inference server running the Orpheus model. You can use:
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This application requires a separate LLM inference server running the Orpheus model. For easy setup, use Docker Compose, which automatically handles this for you. Alternatively, you can use:
-`ORPHEUS_SAMPLE_RATE`: Audio sample rate in Hz (default: 24000)
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-`ORPHEUS_PORT`: Web server port (default: 5005)
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-`ORPHEUS_HOST`: Web server host (default: 0.0.0.0)
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-`ORPHEUS_MODEL_NAME`: Model name for inference server
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The system now supports loading environment variables from a `.env` file in the project root, making it easier to configure without modifying system-wide environment settings. See `.env.example` for a template.
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