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| | | # 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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| | | |
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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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| | | from typing import Dict, List, Tuple, Optional |
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| | | |
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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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| | | |
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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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| | | from funasr.models.base_model import FunASRModel |
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| | | |
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| | | if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): |
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| | | pass |
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| | | return att |
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| | | |
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| | | |
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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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| | | |
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| | | |
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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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| | | |
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| | | |
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| | | class DiarEENDOLAModel(FunASRModel): |
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| | | """EEND-OLA diarization model""" |
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| | | |
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| | | def __init__( |
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| | | self, |
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| | | frontend: WavFrontendMel23, |
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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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| | |
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| | | mapping_dict=None, |
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| | | **kwargs, |
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| | | ): |
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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.eda = encoder_decoder_attractor |
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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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| | |
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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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| | | 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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| | | |
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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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| | | 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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| | | """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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| | | |
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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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| | | 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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| | | |
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| | | # for data-parallel |
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| | | text = text[:, : text_lengths.max()] |
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| | | # Encoder |
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| | | encoder_out = self.forward_encoder(speech, speech_lengths) |
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| | | |
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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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| | | # 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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| | | |
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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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| | | # 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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| | | |
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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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| | | |
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| | | loss = pse_loss + pit_loss + self.attractor_loss_weight * attractor_loss |
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| | | |
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| | | stats = dict() |
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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | 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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| | | |
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| | | # Collect total loss stats |
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| | | stats["loss"] = torch.clone(loss.detach()) |
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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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| | | 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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| | | 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.eda.estimate(emb) |
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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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