mirror of
https://github.com/modelscope/FunASR
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396 lines
14 KiB
Python
396 lines
14 KiB
Python
# 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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import logging
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import torch
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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from funasr.layers.abs_normalize import AbsNormalize
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from funasr.losses.label_smoothing_loss import (
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LabelSmoothingLoss, # noqa: H301
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)
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from funasr.models.ctc import CTC
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from funasr.models.decoder.abs_decoder import AbsDecoder
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from funasr.models.encoder.abs_encoder import AbsEncoder
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from funasr.models.frontend.abs_frontend import AbsFrontend
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from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
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from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
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from funasr.models.specaug.abs_specaug import AbsSpecAug
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from funasr.modules.add_sos_eos import add_sos_eos
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from funasr.modules.e2e_asr_common import ErrorCalculator
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from funasr.modules.eend_ola.encoder import TransformerEncoder
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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.modules.nets_utils import th_accuracy
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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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from typeguard import check_argument_types
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from typing import Dict
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from typing import List
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from typing import Optional
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from typing import Tuple
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from typing import Union
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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from torch.cuda.amp import autocast
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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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class DiarEENDOLAModel(AbsESPnetModel):
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"""CTC-attention hybrid Encoder-Decoder model"""
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def __init__(
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self,
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encoder: TransformerEncoder,
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eda: EncoderDecoderAttractor,
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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.encoder = encoder
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self.eda = eda
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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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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.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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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 collect_feats(
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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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) -> Dict[str, torch.Tensor]:
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if self.extract_feats_in_collect_stats:
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feats, feats_lengths = self._extract_feats(speech, speech_lengths)
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else:
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# Generate dummy stats if extract_feats_in_collect_stats is False
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logging.warning(
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"Generating dummy stats for feats and feats_lengths, "
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"because encoder_conf.extract_feats_in_collect_stats is "
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f"{self.extract_feats_in_collect_stats}"
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)
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feats, feats_lengths = speech, speech_lengths
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return {"feats": feats, "feats_lengths": feats_lengths}
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def encode(
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self, speech: torch.Tensor, speech_lengths: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Frontend + Encoder. Note that this method is used by asr_inference.py
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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"""
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with autocast(False):
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# 1. Extract feats
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feats, feats_lengths = self._extract_feats(speech, speech_lengths)
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# 2. Data augmentation
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if self.specaug is not None and self.training:
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feats, feats_lengths = self.specaug(feats, feats_lengths)
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# 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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if self.normalize is not None:
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feats, feats_lengths = self.normalize(feats, feats_lengths)
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# Pre-encoder, e.g. used for raw input data
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if self.preencoder is not None:
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feats, feats_lengths = self.preencoder(feats, feats_lengths)
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# 4. Forward encoder
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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
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if self.encoder.interctc_use_conditioning:
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encoder_out, encoder_out_lens, _ = self.encoder(
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feats, feats_lengths, ctc=self.ctc
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)
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else:
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encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_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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# Post-encoder, e.g. NLU
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if self.postencoder is not None:
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encoder_out, encoder_out_lens = self.postencoder(
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encoder_out, encoder_out_lens
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)
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assert encoder_out.size(0) == speech.size(0), (
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encoder_out.size(),
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speech.size(0),
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)
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assert encoder_out.size(1) <= encoder_out_lens.max(), (
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encoder_out.size(),
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encoder_out_lens.max(),
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)
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if intermediate_outs is not None:
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return (encoder_out, intermediate_outs), encoder_out_lens
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return encoder_out, encoder_out_lens
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def _extract_feats(
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self, speech: torch.Tensor, speech_lengths: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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assert speech_lengths.dim() == 1, speech_lengths.shape
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# for data-parallel
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speech = speech[:, : speech_lengths.max()]
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if self.frontend is not None:
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# Frontend
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# e.g. STFT and Feature extract
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# data_loader may send time-domain signal in this case
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# speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
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feats, feats_lengths = self.frontend(speech, speech_lengths)
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else:
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# No frontend and no feature extract
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feats, feats_lengths = speech, speech_lengths
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return feats, feats_lengths
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def nll(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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) -> torch.Tensor:
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"""Compute negative log likelihood(nll) from transformer-decoder
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Normally, this function is called in batchify_nll.
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Args:
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encoder_out: (Batch, Length, Dim)
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encoder_out_lens: (Batch,)
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ys_pad: (Batch, Length)
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ys_pad_lens: (Batch,)
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"""
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ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
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ys_in_lens = ys_pad_lens + 1
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# 1. Forward decoder
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decoder_out, _ = self.decoder(
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encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
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) # [batch, seqlen, dim]
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batch_size = decoder_out.size(0)
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decoder_num_class = decoder_out.size(2)
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# nll: negative log-likelihood
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nll = torch.nn.functional.cross_entropy(
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decoder_out.view(-1, decoder_num_class),
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ys_out_pad.view(-1),
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ignore_index=self.ignore_id,
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reduction="none",
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)
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nll = nll.view(batch_size, -1)
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nll = nll.sum(dim=1)
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assert nll.size(0) == batch_size
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return nll
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def batchify_nll(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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batch_size: int = 100,
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):
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"""Compute negative log likelihood(nll) from transformer-decoder
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To avoid OOM, this fuction seperate the input into batches.
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Then call nll for each batch and combine and return results.
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Args:
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encoder_out: (Batch, Length, Dim)
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encoder_out_lens: (Batch,)
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ys_pad: (Batch, Length)
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ys_pad_lens: (Batch,)
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batch_size: int, samples each batch contain when computing nll,
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you may change this to avoid OOM or increase
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GPU memory usage
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"""
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total_num = encoder_out.size(0)
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if total_num <= batch_size:
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nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
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else:
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nll = []
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start_idx = 0
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while True:
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end_idx = min(start_idx + batch_size, total_num)
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batch_encoder_out = encoder_out[start_idx:end_idx, :, :]
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batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx]
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batch_ys_pad = ys_pad[start_idx:end_idx, :]
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batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx]
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batch_nll = self.nll(
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batch_encoder_out,
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batch_encoder_out_lens,
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batch_ys_pad,
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batch_ys_pad_lens,
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)
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nll.append(batch_nll)
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start_idx = end_idx
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if start_idx == total_num:
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break
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nll = torch.cat(nll)
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assert nll.size(0) == total_num
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return nll
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def _calc_att_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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):
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ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
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ys_in_lens = ys_pad_lens + 1
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# 1. Forward decoder
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decoder_out, _ = self.decoder(
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encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
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)
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# 2. Compute attention loss
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loss_att = self.criterion_att(decoder_out, ys_out_pad)
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acc_att = th_accuracy(
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decoder_out.view(-1, self.vocab_size),
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ys_out_pad,
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ignore_label=self.ignore_id,
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)
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# Compute cer/wer using attention-decoder
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if self.training or self.error_calculator is None:
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cer_att, wer_att = None, None
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else:
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ys_hat = decoder_out.argmax(dim=-1)
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cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
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return loss_att, acc_att, cer_att, wer_att
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def _calc_ctc_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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):
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# Calc CTC loss
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loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
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# Calc CER using CTC
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cer_ctc = None
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if not self.training and self.error_calculator is not None:
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ys_hat = self.ctc.argmax(encoder_out).data
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cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
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return loss_ctc, cer_ctc
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