| | |
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| | | from modelscope.pipelines import pipeline |
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| | | from modelscope.utils.constant import Tasks |
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| | | from modelscope.utils.logger import get_logger |
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| | | import logging |
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| | | logger = get_logger(log_level=logging.CRITICAL) |
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| | | logger.setLevel(logging.CRITICAL) |
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| | | import soundfile |
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| | | |
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| | | |
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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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| | | model_revision='v1.2.0', |
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| | | output_dir=output_dir, |
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| | | batch_size=1, |
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| | | mode='online', |
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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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| | |
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| | | sample_offset = 0 |
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| | | |
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| | | step = 160 * 10 |
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| | | param_dict = {'in_cache': dict()} |
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| | | param_dict = {'in_cache': dict(), 'max_end_sil': 800} |
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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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