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| | | import torch |
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| | | import torch.nn as nn |
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| | | import torch.nn.functional as F |
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| | | from typeguard import check_argument_types |
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| | | |
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| | | from funasr.models.base_model import FunASRModel |
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| | | from funasr.models.frontend.wav_frontend import WavFrontendMel23 |
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| | |
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| | | mapping_dict=None, |
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| | | **kwargs, |
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| | | ): |
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| | | assert check_argument_types() |
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| | | |
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| | | super().__init__() |
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| | | self.frontend = frontend |
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| | | self.enc = encoder |
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| | |
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| | | |
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| | | def estimate_sequential(self, |
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| | | speech: torch.Tensor, |
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| | | speech_lengths: torch.Tensor, |
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| | | n_speakers: int = None, |
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| | | shuffle: bool = True, |
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| | | threshold: float = 0.5, |
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| | | **kwargs): |
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| | | speech = [s[:s_len] for s, s_len in zip(speech, speech_lengths)] |
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| | | speech_lengths = torch.tensor([len(sph) for sph in speech]).to(torch.int64) |
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| | | emb = self.forward_encoder(speech, speech_lengths) |
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| | | if shuffle: |
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| | | orders = [np.arange(e.shape[0]) for e in emb] |
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