264 lines
9.5 KiB
Python
264 lines
9.5 KiB
Python
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"""
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Author: Speech Lab, Alibaba Group, China
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"""
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import logging
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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 torch
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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.frontends.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.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.metrics import ErrorCalculator
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.models.base_model import FunASRModel
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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 ESPnetSVModel(FunASRModel):
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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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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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postencoder: Optional[AbsPostEncoder],
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pooling_layer: torch.nn.Module,
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decoder: AbsDecoder,
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):
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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.vocab_size = vocab_size
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self.token_list = token_list.copy()
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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.postencoder = postencoder
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self.encoder = encoder
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self.pooling_layer = pooling_layer
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self.decoder = decoder
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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] == speech_lengths.shape[0] == text.shape[0] == 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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loss_transducer, cer_transducer, wer_transducer = None, 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 = (1 - self.interctc_weight) * loss_ctc + self.interctc_weight * loss_interctc
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if self.use_transducer_decoder:
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# 2a. Transducer decoder branch
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(
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loss_transducer,
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cer_transducer,
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wer_transducer,
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) = self._calc_transducer_loss(
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encoder_out,
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encoder_out_lens,
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text,
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)
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if loss_ctc is not None:
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loss = loss_transducer + (self.ctc_weight * loss_ctc)
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else:
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loss = loss_transducer
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# Collect Transducer branch stats
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stats["loss_transducer"] = (
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loss_transducer.detach() if loss_transducer is not None else None
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)
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stats["cer_transducer"] = cer_transducer
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stats["wer_transducer"] = wer_transducer
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else:
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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) -> (Batch, Channel, Length2, Dim2)
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encoder_out, encoder_out_lens = self.encoder(feats, feats_lengths)
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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(encoder_out, 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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