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@@ -59,8 +59,21 @@ The default value is 30 minutes.
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python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600
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```
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You may also use `vad_process_timeout` with a single device (`--vad_parallel_devices 0`), if you prefer to always free video memory after a period of time.
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# Docker
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To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU.
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python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600
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```
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To execute the Silero VAD itself in parallel, use the `vad_cpu_cores` option:
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```
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python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600 --vad_cpu_cores 4
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```
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You may also use `vad_process_timeout` with a single device (`--vad_parallel_devices 0`), if you prefer to always free video memory after a period of time.
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### Auto Parallel
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You can also set `auto_parallel` to `True`. This will set `vad_parallel_devices` to use all the GPU devices on the system, and `vad_cpu_cores` to be equal to the number of
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cores (up to 8):
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```
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python app.py --input_audio_max_duration -1 --auto_parallel True
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```
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# Docker
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To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU.
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