# Copyright ESPnet (https://github.com/espnet/espnet). All Rights Reserved.
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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from typing import Dict
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from typing import Tuple
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import numpy as np
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import torch
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import torch.nn as nn
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from typeguard import check_argument_types
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from funasr.models.frontend.wav_frontend import WavFrontendMel23
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from funasr.modules.eend_ola.encoder import EENDOLATransformerEncoder
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from funasr.modules.eend_ola.encoder_decoder_attractor import EncoderDecoderAttractor
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from funasr.modules.eend_ola.utils.power import generate_mapping_dict
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from funasr.torch_utils.device_funcs import force_gatherable
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from funasr.train.abs_espnet_model import AbsESPnetModel
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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pass
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else:
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# Nothing to do if torch<1.6.0
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@contextmanager
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def autocast(enabled=True):
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yield
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def pad_attractor(att, max_n_speakers):
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C, D = att.shape
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if C < max_n_speakers:
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att = torch.cat([att, torch.zeros(max_n_speakers - C, D).to(torch.float32).to(att.device)], dim=0)
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return att
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class DiarEENDOLAModel(AbsESPnetModel):
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"""EEND-OLA diarization model"""
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def __init__(
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self,
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frontend: WavFrontendMel23,
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encoder: EENDOLATransformerEncoder,
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encoder_decoder_attractor: EncoderDecoderAttractor,
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n_units: int = 256,
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max_n_speaker: int = 8,
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attractor_loss_weight: float = 1.0,
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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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super().__init__()
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self.frontend = frontend
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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.output_layer = nn.Linear(n_units, mapping_dict['oov'] + 1)
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def forward_encoder(self, xs, ilens):
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xs = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=-1)
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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.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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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.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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return outputs
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Frontend + Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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"""
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assert text_lengths.dim() == 1, text_lengths.shape
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# Check that batch_size is unified
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assert (
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speech.shape[0]
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== speech_lengths.shape[0]
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== text.shape[0]
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== text_lengths.shape[0]
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), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
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batch_size = speech.shape[0]
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# for data-parallel
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text = text[:, : text_lengths.max()]
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# 1. Encoder
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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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encoder_out = encoder_out[0]
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loss_att, acc_att, cer_att, wer_att = None, None, None, None
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loss_ctc, cer_ctc = None, None
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stats = dict()
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# 1. CTC branch
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if self.ctc_weight != 0.0:
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# Collect CTC branch stats
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stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
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stats["cer_ctc"] = cer_ctc
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# Intermediate CTC (optional)
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loss_interctc = 0.0
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if self.interctc_weight != 0.0 and intermediate_outs is not None:
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for layer_idx, intermediate_out in intermediate_outs:
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# we assume intermediate_out has the same length & padding
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# as those of encoder_out
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loss_ic, cer_ic = self._calc_ctc_loss(
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intermediate_out, encoder_out_lens, text, text_lengths
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)
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loss_interctc = loss_interctc + loss_ic
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# Collect Intermedaite CTC stats
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stats["loss_interctc_layer{}".format(layer_idx)] = (
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loss_ic.detach() if loss_ic is not None else None
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)
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stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
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loss_interctc = loss_interctc / len(intermediate_outs)
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# calculate whole encoder loss
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loss_ctc = (
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1 - self.interctc_weight
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) * loss_ctc + self.interctc_weight * loss_interctc
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# 2b. Attention decoder branch
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if self.ctc_weight != 1.0:
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loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# 3. CTC-Att loss definition
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if self.ctc_weight == 0.0:
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loss = loss_att
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elif self.ctc_weight == 1.0:
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loss = loss_ctc
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else:
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loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
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# Collect Attn branch stats
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stats["loss_att"] = loss_att.detach() if loss_att is not None else None
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stats["acc"] = acc_att
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stats["cer"] = cer_att
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stats["wer"] = wer_att
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# Collect total loss stats
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stats["loss"] = torch.clone(loss.detach())
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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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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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.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.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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att = att[:n_speakers, ]
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attractors_active.append(att)
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elif threshold is not None:
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silence = torch.nonzero(p < threshold)[0]
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n_spk = silence[0] if silence.size else None
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att = att[:n_spk, ]
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attractors_active.append(att)
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else:
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NotImplementedError('n_speakers or threshold has to be given.')
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raw_n_speakers = [att.shape[0] for att in attractors_active]
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attractors = [
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pad_attractor(att, self.max_n_speaker) if att.shape[0] <= self.max_n_speaker else att[:self.max_n_speaker]
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for att in attractors_active]
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ys = [torch.matmul(e, att.permute(1, 0)) for e, att in zip(emb, attractors)]
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logits = self.forward_post_net(ys, speech_lengths)
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ys = [self.recover_y_from_powerlabel(logit, raw_n_speaker) for logit, raw_n_speaker in
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zip(logits, raw_n_speakers)]
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return ys, emb, attractors, raw_n_speakers
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def recover_y_from_powerlabel(self, logit, n_speaker):
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pred = torch.argmax(torch.softmax(logit, dim=-1), dim=-1)
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oov_index = torch.where(pred == self.mapping_dict['oov'])[0]
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for i in oov_index:
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if i > 0:
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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.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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def inv_mapping_func(self, label):
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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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def collect_feats(self, **batch: torch.Tensor) -> Dict[str, torch.Tensor]:
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pass
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