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funasr_local/models/e2e_asr_mfcca.py
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326
funasr_local/models/e2e_asr_mfcca.py
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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 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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import logging
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import torch
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from typeguard import check_argument_types
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from funasr_local.modules.e2e_asr_common import ErrorCalculator
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from funasr_local.modules.nets_utils import th_accuracy
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from funasr_local.modules.add_sos_eos import add_sos_eos
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from funasr_local.losses.label_smoothing_loss import (
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LabelSmoothingLoss, # noqa: H301
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)
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from funasr_local.models.ctc import CTC
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from funasr_local.models.decoder.abs_decoder import AbsDecoder
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from funasr_local.models.encoder.abs_encoder import AbsEncoder
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from funasr_local.models.frontend.abs_frontend import AbsFrontend
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from funasr_local.models.preencoder.abs_preencoder import AbsPreEncoder
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from funasr_local.models.specaug.abs_specaug import AbsSpecAug
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from funasr_local.layers.abs_normalize import AbsNormalize
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from funasr_local.torch_utils.device_funcs import force_gatherable
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from funasr_local.train.abs_espnet_model import AbsESPnetModel
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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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import pdb
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import random
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import math
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class MFCCA(AbsESPnetModel):
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"""
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Author: Audio, Speech and Language Processing Group (ASLP@NPU), Northwestern Polytechnical University
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MFCCA:Multi-Frame Cross-Channel attention for multi-speaker ASR in Multi-party meeting scenario
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https://arxiv.org/abs/2210.05265
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"""
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def __init__(
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self,
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vocab_size: int,
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token_list: Union[Tuple[str, ...], List[str]],
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frontend: Optional[AbsFrontend],
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specaug: Optional[AbsSpecAug],
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normalize: Optional[AbsNormalize],
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preencoder: Optional[AbsPreEncoder],
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encoder: AbsEncoder,
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decoder: AbsDecoder,
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ctc: CTC,
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rnnt_decoder: None,
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ctc_weight: float = 0.5,
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ignore_id: int = -1,
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lsm_weight: float = 0.0,
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mask_ratio: float = 0.0,
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length_normalized_loss: bool = False,
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report_cer: bool = True,
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report_wer: bool = True,
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sym_space: str = "<space>",
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sym_blank: str = "<blank>",
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):
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assert check_argument_types()
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assert 0.0 <= ctc_weight <= 1.0, ctc_weight
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assert rnnt_decoder is None, "Not implemented"
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super().__init__()
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# note that eos is the same as sos (equivalent ID)
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self.sos = vocab_size - 1
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self.eos = vocab_size - 1
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self.vocab_size = vocab_size
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self.ignore_id = ignore_id
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self.ctc_weight = ctc_weight
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self.token_list = token_list.copy()
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self.mask_ratio = mask_ratio
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self.frontend = frontend
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self.specaug = specaug
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self.normalize = normalize
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self.preencoder = preencoder
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self.encoder = encoder
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# we set self.decoder = None in the CTC mode since
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# self.decoder parameters were never used and PyTorch complained
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# and threw an Exception in the multi-GPU experiment.
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# thanks Jeff Farris for pointing out the issue.
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if ctc_weight == 1.0:
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self.decoder = None
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else:
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self.decoder = decoder
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if ctc_weight == 0.0:
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self.ctc = None
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else:
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self.ctc = ctc
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self.rnnt_decoder = rnnt_decoder
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self.criterion_att = LabelSmoothingLoss(
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size=vocab_size,
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padding_idx=ignore_id,
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smoothing=lsm_weight,
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normalize_length=length_normalized_loss,
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)
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if report_cer or report_wer:
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self.error_calculator = ErrorCalculator(
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token_list, sym_space, sym_blank, report_cer, report_wer
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)
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else:
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self.error_calculator = None
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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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#pdb.set_trace()
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if(speech.dim()==3 and speech.size(2)==8 and self.mask_ratio !=0):
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rate_num = random.random()
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#rate_num = 0.1
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if(rate_num<=self.mask_ratio):
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retain_channel = math.ceil(random.random() *8)
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if(retain_channel>1):
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speech = speech[:,:,torch.randperm(8)[0:retain_channel].sort().values]
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else:
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speech = speech[:,:,torch.randperm(8)[0]]
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#pdb.set_trace()
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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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# 2a. 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 = None, None, None, None
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else:
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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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# 2b. CTC branch
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if self.ctc_weight == 0.0:
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loss_ctc, cer_ctc = None, None
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else:
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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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# 2c. RNN-T branch
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if self.rnnt_decoder is not None:
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_ = self._calc_rnnt_loss(encoder_out, encoder_out_lens, text, text_lengths)
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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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stats = dict(
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loss=loss.detach(),
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loss_att=loss_att.detach() if loss_att is not None else None,
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loss_ctc=loss_ctc.detach() if loss_ctc is not None else None,
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acc=acc_att,
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cer=cer_att,
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wer=wer_att,
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cer_ctc=cer_ctc,
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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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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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feats, feats_lengths, channel_size = self._extract_feats(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, channel_size = 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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#pdb.set_trace()
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encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, channel_size)
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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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if(encoder_out.dim()==4):
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assert encoder_out.size(2) <= 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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else:
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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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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, channel_size = 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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channel_size = 1
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return feats, feats_lengths, channel_size
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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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if(encoder_out.dim()==4):
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encoder_out = encoder_out.mean(1)
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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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def _calc_rnnt_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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raise NotImplementedError
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