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在Linux机器上测试paddlespeech_stream模型时,CPU占用极高,基本上打满了:
./build/examples/paddlespeech_stream models/paddlespeech_stream/ models/paddlespeech_stream/long.wav
我可以通过设置线程数OPENBLAS_NUM_THREADS解决cpu核数问题,但会造成性能低,且单核打满。
这种情况导致我无法在生产环境使用,请问有没有解决方案?
The text was updated successfully, but these errors were encountered:
用这个项目吧,https://github.com/k2-fsa/sherpa-onnx 里面包含很多预训练模型,https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html 如果太占CPU可以选择模型小一些的模型,或者使用int8量化的版本。
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conformer_online,这个流式的在测试长音频的时候, 运行到一半就卡住,或者报 Segmentation fault (core dumped)
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在Linux机器上测试paddlespeech_stream模型时,CPU占用极高,基本上打满了:
我可以通过设置线程数OPENBLAS_NUM_THREADS解决cpu核数问题,但会造成性能低,且单核打满。
这种情况导致我无法在生产环境使用,请问有没有解决方案?
The text was updated successfully, but these errors were encountered: