Merge branch 'alibaba-damo-academy:main' into main
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| | | fbanks, fbanks_len = self.frontend.forward_fbank(speech, speech_lengths) |
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| | | feats, feats_len = self.frontend.forward_lfr_cmvn(fbanks, fbanks_len) |
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| | | fbanks = to_device(fbanks, device=self.device) |
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| | | # feats = to_device(feats, device=self.device) |
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| | | feats = to_device(feats, device=self.device) |
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| | | feats_len = feats_len.int() |
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| | | else: |
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| | | raise Exception("Need to extract feats first, please configure frontend configuration") |
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| | | "in_cache": in_cache |
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| | | } |
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| | | # a. To device |
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| | | batch = to_device(batch, device=self.device) |
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| | | #batch = to_device(batch, device=self.device) |
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| | | segments_part, in_cache = self.vad_model(**batch) |
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| | | if segments_part: |
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| | | for batch_num in range(0, self.batch_size): |
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| | | means = np.array(means_list).astype(np.float) |
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| | | vars = np.array(vars_list).astype(np.float) |
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| | | cmvn = np.array([means, vars]) |
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| | | cmvn = torch.as_tensor(cmvn, dype=torch.float32) |
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| | | cmvn = torch.as_tensor(cmvn, dtype=torch.float32) |
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| | | return cmvn |
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| | | # CPU Benchmark (ONNX) |
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| | | # CPU Benchmark (ONNX-python) |
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| | | ## Configuration |
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| | | ### Data set: |
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