Merge pull request #565 from alibaba-damo-academy/dev_zly2
support in_cache
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| | | batch_size = speech.shape[0] |
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| | | segments = [[]] * batch_size |
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| | | if self.frontend is not None: |
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| | | feats, feats_len = self.frontend.forward(speech, speech_lengths, is_final) |
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| | | reset = in_cache == dict() |
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| | | feats, feats_len = self.frontend.forward(speech, speech_lengths, is_final, reset) |
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| | | fbanks, _ = self.frontend.get_fbank() |
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| | | else: |
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| | | raise Exception("Need to extract feats first, please configure frontend configuration") |
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| | | self.vad_opts.frame_in_ms) |
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| | | self.encoder = encoder |
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| | | # init variables |
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| | | self.is_final = False |
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| | | self.data_buf_start_frame = 0 |
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| | | self.frm_cnt = 0 |
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| | | self.latest_confirmed_speech_frame = 0 |
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| | | self.frontend = frontend |
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| | | def AllResetDetection(self): |
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| | | self.is_final = False |
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| | | self.data_buf_start_frame = 0 |
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| | | self.frm_cnt = 0 |
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| | | self.latest_confirmed_speech_frame = 0 |
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| | | def forward(self, feats: torch.Tensor, waveform: torch.tensor, in_cache: Dict[str, torch.Tensor] = dict(), |
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| | | is_final: bool = False |
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| | | ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]: |
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| | | if not in_cache: |
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| | | self.AllResetDetection() |
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| | | self.waveform = waveform # compute decibel for each frame |
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| | | self.ComputeDecibel() |
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| | | self.ComputeScores(feats, in_cache) |
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| | | return feats_pad, feats_lens, lfr_splice_frame_idxs |
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| | | |
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| | | def forward( |
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| | | self, input: torch.Tensor, input_lengths: torch.Tensor, is_final: bool = False |
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| | | self, input: torch.Tensor, input_lengths: torch.Tensor, is_final: bool = False, reset: bool = False |
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| | | ) -> Tuple[torch.Tensor, torch.Tensor]: |
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| | | if reset: |
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| | | self.cache_reset() |
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| | | batch_size = input.shape[0] |
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| | | assert batch_size == 1, 'we support to extract feature online only when the batch size is equal to 1 now' |
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| | | waveforms, feats, feats_lengths = self.forward_fbank(input, input_lengths) # input shape: B T D |
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