| | |
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| | | def generate_with_vad(self, input, input_len=None, **cfg): |
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| | | |
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| | | # step.1: compute the vad model |
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| | | model = self.vad_model |
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| | | kwargs = self.vad_kwargs |
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| | | kwargs.update(cfg) |
|---|
| | | self.vad_kwargs.update(cfg) |
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| | | beg_vad = time.time() |
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| | | res = self.generate(input, input_len=input_len, model=model, kwargs=kwargs, **cfg) |
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| | | vad_res = res |
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| | | res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg) |
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| | | end_vad = time.time() |
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| | | print(f"time cost vad: {end_vad - beg_vad:0.3f}") |
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| | | |
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| | |
|---|
| | | if not len(sorted_data): |
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| | | logging.info("decoding, utt: {}, empty speech".format(key)) |
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| | | continue |
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| | | |
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| | | |
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| | | # if kwargs["device"] == "cpu": |
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| | | # batch_size = 0 |
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| | | if len(sorted_data) > 0 and len(sorted_data[0]) > 0: |
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| | | batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0]) |
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| | | |
|---|
