from contextlib import contextmanager
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from distutils.version import LooseVersion
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from typing import Dict, List, Tuple, Optional
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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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import torch.nn.functional as F
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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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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.losses import standard_loss, cal_power_loss, fast_batch_pit_n_speaker_loss
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from funasr.modules.eend_ola.utils.power import create_powerlabel
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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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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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def pad_labels(ts, out_size):
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for i, t in enumerate(ts):
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if t.shape[1] < out_size:
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ts[i] = F.pad(
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t,
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(0, out_size - t.shape[1], 0, 0),
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mode='constant',
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value=0.
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)
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return ts
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def pad_results(ys, out_size):
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ys_padded = []
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for i, y in enumerate(ys):
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if y.shape[1] < out_size:
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ys_padded.append(
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torch.cat([y, torch.zeros(y.shape[0], out_size - y.shape[1]).to(torch.float32).to(y.device)], dim=1))
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else:
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ys_padded.append(y)
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return ys_padded
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class DiarEENDOLAModel(FunASRModel):
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"""EEND-OLA diarization model"""
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def __init__(
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self,
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frontend: Optional[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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super().__init__()
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self.frontend = frontend
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self.enc = encoder
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self.encoder_decoder_attractor = 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,
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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: List[torch.Tensor],
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speaker_labels: List[torch.Tensor],
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orders: torch.Tensor,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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# Check that batch_size is unified
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assert (len(speech) == len(speaker_labels)), (len(speech), len(speaker_labels))
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speech_lengths = torch.tensor([len(sph) for sph in speech]).to(torch.int64)
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speaker_labels_lengths = torch.tensor([spk.shape[-1] for spk in speaker_labels]).to(torch.int64)
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batch_size = len(speech)
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# Encoder
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encoder_out = self.forward_encoder(speech, speech_lengths)
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# Encoder-decoder attractor
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attractor_loss, attractors = self.encoder_decoder_attractor([e[order] for e, order in zip(encoder_out, orders)],
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speaker_labels_lengths)
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speaker_logits = [torch.matmul(e, att.permute(1, 0)) for e, att in zip(encoder_out, attractors)]
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# pit loss
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pit_speaker_labels = fast_batch_pit_n_speaker_loss(speaker_logits, speaker_labels)
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pit_loss = standard_loss(speaker_logits, pit_speaker_labels)
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# pse loss
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with torch.no_grad():
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power_ts = [create_powerlabel(label.cpu().numpy(), self.mapping_dict, self.max_n_speaker).
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to(encoder_out[0].device, non_blocking=True) for label in pit_speaker_labels]
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pad_attractors = [pad_attractor(att, self.max_n_speaker) for att in attractors]
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pse_speaker_logits = [torch.matmul(e, pad_att.permute(1, 0)) for e, pad_att in zip(encoder_out, pad_attractors)]
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pse_speaker_logits = self.forward_post_net(pse_speaker_logits, speech_lengths)
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pse_loss = cal_power_loss(pse_speaker_logits, power_ts)
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loss = pse_loss + pit_loss + self.attractor_loss_weight * attractor_loss
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stats = dict()
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stats["pse_loss"] = pse_loss.detach()
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stats["pit_loss"] = pit_loss.detach()
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stats["attractor_loss"] = attractor_loss.detach()
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stats["batch_size"] = batch_size
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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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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_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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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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[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_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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