Speech synthesis deployment needs to focus on real-time and naturalness:
1. Model preparation::
- Select a speech model supported by FastDeploy (e.g., PaddleSpeech's TTS model)
- Download the pre-trained model (containing.pdmodelcap (a poem).pdiparams)
2. Accelerated configuration::
- Enable multi-token prediction:model.enable_multi_token_prediction()
- GPU deployment with TensorRT turned on (ENABLE_TRT_BACKEND=ON)
3. Service encapsulation::
- Using FastDeploy's OpenAPI-compatible interface (--service-type openai)
- Example startup command:fastdeploy serve --model_dir tts_model --port 8000
4. Optimization of results::
- pass (a bill or inspection etc)infer_cfg.ymlAdjusting the speech rate/pitch parameter
- Monitor metrics such as first packet response time (RTF) in conjunction with VisualDL
This answer comes from the articleFastDeploy: an open source tool for rapid deployment of AI modelsThe
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