| | |
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| | | |
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| | | super().__init__() |
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| | | self.frontend = frontend |
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| | | self.encoder = encoder |
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| | | self.encoder_decoder_attractor = encoder_decoder_attractor |
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| | | self.enc = encoder |
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| | | self.eda = encoder_decoder_attractor |
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| | | self.attractor_loss_weight = attractor_loss_weight |
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| | | self.max_n_speaker = max_n_speaker |
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| | | if mapping_dict is None: |
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| | | mapping_dict = generate_mapping_dict(max_speaker_num=self.max_n_speaker) |
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| | | self.mapping_dict = mapping_dict |
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| | | # PostNet |
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| | | self.PostNet = nn.LSTM(self.max_n_speaker, n_units, 1, batch_first=True) |
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| | | self.postnet = nn.LSTM(self.max_n_speaker, n_units, 1, batch_first=True) |
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| | | self.output_layer = nn.Linear(n_units, mapping_dict['oov'] + 1) |
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| | | |
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| | | def forward_encoder(self, xs, ilens): |
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| | |
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| | | pad_shape = xs.shape |
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| | | xs_mask = [torch.ones(ilen).to(xs.device) for ilen in ilens] |
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| | | xs_mask = torch.nn.utils.rnn.pad_sequence(xs_mask, batch_first=True, padding_value=0).unsqueeze(-2) |
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| | | emb = self.encoder(xs, xs_mask) |
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| | | emb = self.enc(xs, xs_mask) |
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| | | emb = torch.split(emb.view(pad_shape[0], pad_shape[1], -1), 1, dim=0) |
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| | | emb = [e[0][:ilen] for e, ilen in zip(emb, ilens)] |
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| | | return emb |
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| | |
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| | | def forward_post_net(self, logits, ilens): |
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| | | maxlen = torch.max(ilens).to(torch.int).item() |
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| | | logits = nn.utils.rnn.pad_sequence(logits, batch_first=True, padding_value=-1) |
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| | | logits = nn.utils.rnn.pack_padded_sequence(logits, ilens, batch_first=True, enforce_sorted=False) |
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| | | outputs, (_, _) = self.PostNet(logits) |
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| | | logits = nn.utils.rnn.pack_padded_sequence(logits, ilens.cpu().to(torch.int64), batch_first=True, enforce_sorted=False) |
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| | | outputs, (_, _) = self.postnet(logits) |
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| | | outputs = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True, padding_value=-1, total_length=maxlen)[0] |
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| | | outputs = [output[:ilens[i].to(torch.int).item()] for i, output in enumerate(outputs)] |
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| | | outputs = [self.output_layer(output) for output in outputs] |
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| | |
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| | | text = text[:, : text_lengths.max()] |
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| | | |
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| | | # 1. Encoder |
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| | | encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) |
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| | | encoder_out, encoder_out_lens = self.enc(speech, speech_lengths) |
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| | | intermediate_outs = None |
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| | | if isinstance(encoder_out, tuple): |
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| | | intermediate_outs = encoder_out[1] |
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| | |
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| | | shuffle: bool = True, |
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| | | threshold: float = 0.5, |
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| | | **kwargs): |
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| | | if self.frontend is not None: |
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| | | speech = self.frontend(speech) |
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| | | speech = [s[:s_len] for s, s_len in zip(speech, speech_lengths)] |
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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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| | | for order in orders: |
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| | | np.random.shuffle(order) |
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| | | attractors, probs = self.encoder_decoder_attractor.estimate( |
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| | | attractors, probs = self.eda.estimate( |
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| | | [e[torch.from_numpy(order).to(torch.long).to(speech[0].device)] for e, order in zip(emb, orders)]) |
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| | | else: |
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| | | attractors, probs = self.encoder_decoder_attractor.estimate(emb) |
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| | | attractors, probs = self.eda.estimate(emb) |
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| | | attractors_active = [] |
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| | | for p, att, e in zip(probs, attractors, emb): |
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| | | if n_speakers and n_speakers >= 0: |
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| | |
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| | | pred[i] = pred[i - 1] |
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| | | else: |
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| | | pred[i] = 0 |
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| | | pred = [self.reporter.inv_mapping_func(i, self.mapping_dict) for i in pred] |
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| | | pred = [self.inv_mapping_func(i) for i in pred] |
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| | | decisions = [bin(num)[2:].zfill(self.max_n_speaker)[::-1] for num in pred] |
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| | | decisions = torch.from_numpy( |
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| | | np.stack([np.array([int(i) for i in dec]) for dec in decisions], axis=0)).to(logit.device).to( |
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| | | torch.float32) |
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| | | decisions = decisions[:, :n_speaker] |
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| | | return decisions |
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| | | |
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| | | def inv_mapping_func(self, label): |
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| | | |
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| | | if not isinstance(label, int): |
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| | | label = int(label) |
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| | | if label in self.mapping_dict['label2dec'].keys(): |
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| | | num = self.mapping_dict['label2dec'][label] |
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| | | else: |
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| | | num = -1 |
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| | | return num |
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| | | |
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| | | def collect_feats(self, **batch: torch.Tensor) -> Dict[str, torch.Tensor]: |
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| | | pass |
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