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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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| | | 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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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| | | |
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| | | class DiarEENDOLAModel(AbsESPnetModel): |
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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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| | | 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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| | | |
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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.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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| | | |
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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.encoder(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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| | | 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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| | | |
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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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| | | |
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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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| | | |
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| | | # for data-parallel |
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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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| | | 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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | # 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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| | | |
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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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| | | 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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| | | [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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| | | |
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| | | return ys, emb, attractors, raw_n_speakers |
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| | | |
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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.reporter.inv_mapping_func(i, self.mapping_dict) 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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