from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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import soundfile
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if __name__ == '__main__':
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output_dir = None
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inference_pipline = pipeline(
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task=Tasks.voice_activity_detection,
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model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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model_revision='v1.1.9',
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output_dir=None,
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batch_size=1,
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)
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speech, sample_rate = soundfile.read("./vad_example_16k.wav")
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speech_length = speech.shape[0]
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sample_offset = 0
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step = 160 * 10
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param_dict = {'in_cache': dict()}
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for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
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if sample_offset + step >= speech_length - 1:
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step = speech_length - sample_offset
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is_final = True
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else:
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is_final = False
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param_dict['is_final'] = is_final
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segments_result = inference_pipline(audio_in=speech[sample_offset: sample_offset + step],
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param_dict=param_dict)
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print(segments_result)
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