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https://github.com/modelscope/FunASR
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test
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parent
debafeac37
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@ -141,7 +141,7 @@ class AutoModel:
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kwargs = download_model(**kwargs)
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kwargs = download_model(**kwargs)
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set_all_random_seed(kwargs.get("seed", 0))
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set_all_random_seed(kwargs.get("seed", 0))
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pdb.set_trace()
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device = kwargs.get("device", "cuda")
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device = kwargs.get("device", "cuda")
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if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
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if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
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device = "cpu"
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device = "cpu"
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@ -274,15 +274,12 @@ class LCBNet(nn.Module):
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ind: int
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ind: int
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"""
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"""
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with autocast(False):
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with autocast(False):
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pdb.set_trace()
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# Data augmentation
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# Data augmentation
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if self.specaug is not None and self.training:
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if self.specaug is not None and self.training:
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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pdb.set_trace()
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# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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if self.normalize is not None:
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if self.normalize is not None:
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speech, speech_lengths = self.normalize(speech, speech_lengths)
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speech, speech_lengths = self.normalize(speech, speech_lengths)
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pdb.set_trace()
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# Forward encoder
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# Forward encoder
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# feats: (Batch, Length, Dim)
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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
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# -> encoder_out: (Batch, Length2, Dim2)
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