From c0008fd46134d60a3a41b022bf9156cea5b145e5 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 11 十二月 2023 10:10:40 +0800
Subject: [PATCH] Merge branch 'dev_gzf_funasr2' into main
---
funasr/cli/models/paraformer.py | 652 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 652 insertions(+), 0 deletions(-)
diff --git a/funasr/cli/models/paraformer.py b/funasr/cli/models/paraformer.py
new file mode 100644
index 0000000..ee8c0b4
--- /dev/null
+++ b/funasr/cli/models/paraformer.py
@@ -0,0 +1,652 @@
+import logging
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+from typing import Dict
+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+import torch.nn as nn
+import random
+import numpy as np
+
+# from funasr.layers.abs_normalize import AbsNormalize
+from funasr.losses.label_smoothing_loss import (
+ LabelSmoothingLoss, # noqa: H301
+)
+# from funasr.models.ctc import CTC
+# from funasr.models.decoder.abs_decoder import AbsDecoder
+# from funasr.models.e2e_asr_common import ErrorCalculator
+# from funasr.models.encoder.abs_encoder import AbsEncoder
+# from funasr.models.frontend.abs_frontend import AbsFrontend
+# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
+from funasr.models.predictor.cif import mae_loss
+# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
+# from funasr.models.specaug.abs_specaug import AbsSpecAug
+from funasr.modules.add_sos_eos import add_sos_eos
+from funasr.modules.nets_utils import make_pad_mask, pad_list
+from funasr.modules.nets_utils import th_accuracy
+from funasr.torch_utils.device_funcs import force_gatherable
+# from funasr.models.base_model import FunASRModel
+# from funasr.models.predictor.cif import CifPredictorV3
+
+from funasr.cli.model_class_factory import *
+
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+ from torch.cuda.amp import autocast
+else:
+ # Nothing to do if torch<1.6.0
+ @contextmanager
+ def autocast(enabled=True):
+ yield
+
+
+class Paraformer(nn.Module):
+ """
+ Author: Speech Lab of DAMO Academy, Alibaba Group
+ Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
+ https://arxiv.org/abs/2206.08317
+ """
+
+ def __init__(
+ self,
+ # token_list: Union[Tuple[str, ...], List[str]],
+ frontend: Optional[str] = None,
+ frontend_conf: Optional[Dict] = None,
+ specaug: Optional[str] = None,
+ specaug_conf: Optional[Dict] = None,
+ normalize: str = None,
+ normalize_conf: Optional[Dict] = None,
+ encoder: str = None,
+ encoder_conf: Optional[Dict] = None,
+ decoder: str = None,
+ decoder_conf: Optional[Dict] = None,
+ ctc: str = None,
+ ctc_conf: Optional[Dict] = None,
+ predictor: str = None,
+ predictor_conf: Optional[Dict] = None,
+ ctc_weight: float = 0.5,
+ interctc_weight: float = 0.0,
+ input_size: int = 80,
+ vocab_size: int = -1,
+ ignore_id: int = -1,
+ blank_id: int = 0,
+ sos: int = 1,
+ eos: int = 2,
+ lsm_weight: float = 0.0,
+ length_normalized_loss: bool = False,
+ # report_cer: bool = True,
+ # report_wer: bool = True,
+ # sym_space: str = "<space>",
+ # sym_blank: str = "<blank>",
+ # extract_feats_in_collect_stats: bool = True,
+ # predictor=None,
+ predictor_weight: float = 0.0,
+ predictor_bias: int = 0,
+ sampling_ratio: float = 0.2,
+ share_embedding: bool = False,
+ # preencoder: Optional[AbsPreEncoder] = None,
+ # postencoder: Optional[AbsPostEncoder] = None,
+ use_1st_decoder_loss: bool = False,
+ **kwargs,
+ ):
+ assert 0.0 <= ctc_weight <= 1.0, ctc_weight
+ assert 0.0 <= interctc_weight < 1.0, interctc_weight
+
+ super().__init__()
+
+ # import pdb;
+ # pdb.set_trace()
+
+ if frontend is not None:
+ frontend_class = frontend_choices.get_class(frontend)
+ frontend = frontend_class(**frontend_conf)
+ if specaug is not None:
+ specaug_class = specaug_choices.get_class(specaug)
+ specaug = specaug_class(**specaug_conf)
+ if normalize is not None:
+ normalize_class = normalize_choices.get_class(normalize)
+ normalize = normalize_class(**normalize_conf)
+ encoder_class = encoder_choices.get_class(encoder)
+ encoder = encoder_class(input_size=input_size, **encoder_conf)
+ encoder_output_size = encoder.output_size()
+ if decoder is not None:
+ decoder_class = decoder_choices.get_class(decoder)
+ decoder = decoder_class(
+ vocab_size=vocab_size,
+ encoder_output_size=encoder_output_size,
+ **decoder_conf,
+ )
+ if ctc_weight > 0.0:
+
+ if ctc_conf is None:
+ ctc_conf = {}
+
+ ctc = CTC(
+ odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf
+ )
+ if predictor is not None:
+ predictor_class = predictor_choices.get_class(predictor)
+ predictor = predictor_class(**predictor_conf)
+
+ # note that eos is the same as sos (equivalent ID)
+ self.blank_id = blank_id
+ self.sos = sos if sos is not None else vocab_size - 1
+ self.eos = eos if eos is not None else vocab_size - 1
+ self.vocab_size = vocab_size
+ self.ignore_id = ignore_id
+ self.ctc_weight = ctc_weight
+ self.interctc_weight = interctc_weight
+ # self.token_list = token_list.copy()
+ #
+ self.frontend = frontend
+ self.specaug = specaug
+ self.normalize = normalize
+ # self.preencoder = preencoder
+ # self.postencoder = postencoder
+ self.encoder = encoder
+ #
+ # if not hasattr(self.encoder, "interctc_use_conditioning"):
+ # self.encoder.interctc_use_conditioning = False
+ # if self.encoder.interctc_use_conditioning:
+ # self.encoder.conditioning_layer = torch.nn.Linear(
+ # vocab_size, self.encoder.output_size()
+ # )
+ #
+ # self.error_calculator = None
+ #
+ if ctc_weight == 1.0:
+ self.decoder = None
+ else:
+ self.decoder = decoder
+
+ self.criterion_att = LabelSmoothingLoss(
+ size=vocab_size,
+ padding_idx=ignore_id,
+ smoothing=lsm_weight,
+ normalize_length=length_normalized_loss,
+ )
+ #
+ # if report_cer or report_wer:
+ # self.error_calculator = ErrorCalculator(
+ # token_list, sym_space, sym_blank, report_cer, report_wer
+ # )
+ #
+ if ctc_weight == 0.0:
+ self.ctc = None
+ else:
+ self.ctc = ctc
+ #
+ # self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
+ self.predictor = predictor
+ self.predictor_weight = predictor_weight
+ self.predictor_bias = predictor_bias
+ self.sampling_ratio = sampling_ratio
+ self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
+ # self.step_cur = 0
+ #
+ self.share_embedding = share_embedding
+ if self.share_embedding:
+ self.decoder.embed = None
+
+ self.use_1st_decoder_loss = use_1st_decoder_loss
+
+ def forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ """Frontend + Encoder + Decoder + Calc loss
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ text: (Batch, Length)
+ text_lengths: (Batch,)
+ decoding_ind: int
+ """
+ decoding_ind = kwargs.get("kwargs", None)
+ # import pdb;
+ # pdb.set_trace()
+ if len(text_lengths.size()) > 1:
+ text_lengths = text_lengths[:, 0]
+ if len(speech_lengths.size()) > 1:
+ speech_lengths = speech_lengths[:, 0]
+
+ batch_size = speech.shape[0]
+
+ # # for data-parallel
+ # text = text[:, : text_lengths.max()]
+ # speech = speech[:, :speech_lengths.max()]
+
+ # 1. Encoder
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
+ else:
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ intermediate_outs = None
+ if isinstance(encoder_out, tuple):
+ intermediate_outs = encoder_out[1]
+ encoder_out = encoder_out[0]
+
+ loss_att, pre_loss_att, acc_att, cer_att, wer_att = None, None, None, None, None
+ loss_ctc, cer_ctc = None, None
+ loss_pre = None
+ stats = dict()
+
+ # 1. CTC branch
+ if self.ctc_weight != 0.0:
+ loss_ctc, cer_ctc = self._calc_ctc_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # Collect CTC branch stats
+ stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
+ stats["cer_ctc"] = cer_ctc
+
+ # Intermediate CTC (optional)
+ loss_interctc = 0.0
+ if self.interctc_weight != 0.0 and intermediate_outs is not None:
+ for layer_idx, intermediate_out in intermediate_outs:
+ # we assume intermediate_out has the same length & padding
+ # as those of encoder_out
+ loss_ic, cer_ic = self._calc_ctc_loss(
+ intermediate_out, encoder_out_lens, text, text_lengths
+ )
+ loss_interctc = loss_interctc + loss_ic
+
+ # Collect Intermedaite CTC stats
+ stats["loss_interctc_layer{}".format(layer_idx)] = (
+ loss_ic.detach() if loss_ic is not None else None
+ )
+ stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
+
+ loss_interctc = loss_interctc / len(intermediate_outs)
+
+ # calculate whole encoder loss
+ loss_ctc = (
+ 1 - self.interctc_weight
+ ) * loss_ctc + self.interctc_weight * loss_interctc
+
+ # 2b. Attention decoder branch
+ if self.ctc_weight != 1.0:
+ loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = self._calc_att_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # 3. CTC-Att loss definition
+ if self.ctc_weight == 0.0:
+ loss = loss_att + loss_pre * self.predictor_weight
+ elif self.ctc_weight == 1.0:
+ loss = loss_ctc
+ else:
+ loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
+
+ if self.use_1st_decoder_loss and pre_loss_att is not None:
+ loss = loss + (1 - self.ctc_weight) * pre_loss_att
+
+ # Collect Attn branch stats
+ stats["loss_att"] = loss_att.detach() if loss_att is not None else None
+ stats["pre_loss_att"] = pre_loss_att.detach() if pre_loss_att is not None else None
+ stats["acc"] = acc_att
+ stats["cer"] = cer_att
+ stats["wer"] = wer_att
+ stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
+
+ stats["loss"] = torch.clone(loss.detach())
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+ return loss, stats, weight
+
+ def collect_feats(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ ) -> Dict[str, torch.Tensor]:
+ if self.extract_feats_in_collect_stats:
+ feats, feats_lengths = self._extract_feats(speech, speech_lengths)
+ else:
+ # Generate dummy stats if extract_feats_in_collect_stats is False
+ logging.warning(
+ "Generating dummy stats for feats and feats_lengths, "
+ "because encoder_conf.extract_feats_in_collect_stats is "
+ f"{self.extract_feats_in_collect_stats}"
+ )
+ feats, feats_lengths = speech, speech_lengths
+ return {"feats": feats, "feats_lengths": feats_lengths}
+
+ def encode(
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor, ind: int = 0,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Frontend + Encoder. Note that this method is used by asr_inference.py
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ ind: int
+ """
+ with autocast(False):
+ # # 1. Extract feats
+ # feats, feats_lengths = self._extract_feats(speech, speech_lengths)
+
+ # 2. Data augmentation
+ if self.specaug is not None and self.training:
+ feats, feats_lengths = self.specaug(speech, speech_lengths)
+
+ # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
+ if self.normalize is not None:
+ feats, feats_lengths = self.normalize(feats, feats_lengths)
+
+ # # Pre-encoder, e.g. used for raw input data
+ # if self.preencoder is not None:
+ # feats, feats_lengths = self.preencoder(feats, feats_lengths)
+
+ # 4. Forward encoder
+ # feats: (Batch, Length, Dim)
+ # -> encoder_out: (Batch, Length2, Dim2)
+ if self.encoder.interctc_use_conditioning:
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ encoder_out, encoder_out_lens, _ = self.encoder(
+ feats, feats_lengths, ctc=self.ctc, ind=ind
+ )
+ encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
+ encoder_out_lens,
+ chunk_outs=None)
+ else:
+ encoder_out, encoder_out_lens, _ = self.encoder(
+ feats, feats_lengths, ctc=self.ctc
+ )
+ else:
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, ind=ind)
+ encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
+ encoder_out_lens,
+ chunk_outs=None)
+ else:
+ encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
+ intermediate_outs = None
+ if isinstance(encoder_out, tuple):
+ intermediate_outs = encoder_out[1]
+ encoder_out = encoder_out[0]
+
+ # # Post-encoder, e.g. NLU
+ # if self.postencoder is not None:
+ # encoder_out, encoder_out_lens = self.postencoder(
+ # encoder_out, encoder_out_lens
+ # )
+
+ assert encoder_out.size(0) == speech.size(0), (
+ encoder_out.size(),
+ speech.size(0),
+ )
+ assert encoder_out.size(1) <= encoder_out_lens.max(), (
+ encoder_out.size(),
+ encoder_out_lens.max(),
+ )
+
+ if intermediate_outs is not None:
+ return (encoder_out, intermediate_outs), encoder_out_lens
+
+ return encoder_out, encoder_out_lens
+
+ def calc_predictor(self, encoder_out, encoder_out_lens):
+
+ encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
+ encoder_out.device)
+ pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(encoder_out, None, encoder_out_mask,
+ ignore_id=self.ignore_id)
+ return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
+
+ def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
+
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
+ )
+ decoder_out = decoder_outs[0]
+ decoder_out = torch.log_softmax(decoder_out, dim=-1)
+ return decoder_out, ys_pad_lens
+
+ def _extract_feats(
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ assert speech_lengths.dim() == 1, speech_lengths.shape
+
+ # for data-parallel
+ speech = speech[:, : speech_lengths.max()]
+ if self.frontend is not None:
+ # Frontend
+ # e.g. STFT and Feature extract
+ # data_loader may send time-domain signal in this case
+ # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
+ feats, feats_lengths = self.frontend(speech, speech_lengths)
+ else:
+ # No frontend and no feature extract
+ feats, feats_lengths = speech, speech_lengths
+ return feats, feats_lengths
+
+ def nll(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ) -> torch.Tensor:
+ """Compute negative log likelihood(nll) from transformer-decoder
+ Normally, this function is called in batchify_nll.
+ Args:
+ encoder_out: (Batch, Length, Dim)
+ encoder_out_lens: (Batch,)
+ ys_pad: (Batch, Length)
+ ys_pad_lens: (Batch,)
+ """
+ ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+ ys_in_lens = ys_pad_lens + 1
+
+ # 1. Forward decoder
+ decoder_out, _ = self.decoder(
+ encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
+ ) # [batch, seqlen, dim]
+ batch_size = decoder_out.size(0)
+ decoder_num_class = decoder_out.size(2)
+ # nll: negative log-likelihood
+ nll = torch.nn.functional.cross_entropy(
+ decoder_out.view(-1, decoder_num_class),
+ ys_out_pad.view(-1),
+ ignore_index=self.ignore_id,
+ reduction="none",
+ )
+ nll = nll.view(batch_size, -1)
+ nll = nll.sum(dim=1)
+ assert nll.size(0) == batch_size
+ return nll
+
+ def batchify_nll(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ batch_size: int = 100,
+ ):
+ """Compute negative log likelihood(nll) from transformer-decoder
+ To avoid OOM, this fuction seperate the input into batches.
+ Then call nll for each batch and combine and return results.
+ Args:
+ encoder_out: (Batch, Length, Dim)
+ encoder_out_lens: (Batch,)
+ ys_pad: (Batch, Length)
+ ys_pad_lens: (Batch,)
+ batch_size: int, samples each batch contain when computing nll,
+ you may change this to avoid OOM or increase
+ GPU memory usage
+ """
+ total_num = encoder_out.size(0)
+ if total_num <= batch_size:
+ nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
+ else:
+ nll = []
+ start_idx = 0
+ while True:
+ end_idx = min(start_idx + batch_size, total_num)
+ batch_encoder_out = encoder_out[start_idx:end_idx, :, :]
+ batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx]
+ batch_ys_pad = ys_pad[start_idx:end_idx, :]
+ batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx]
+ batch_nll = self.nll(
+ batch_encoder_out,
+ batch_encoder_out_lens,
+ batch_ys_pad,
+ batch_ys_pad_lens,
+ )
+ nll.append(batch_nll)
+ start_idx = end_idx
+ if start_idx == total_num:
+ break
+ nll = torch.cat(nll)
+ assert nll.size(0) == total_num
+ return nll
+
+ def _calc_att_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
+ encoder_out.device)
+ if self.predictor_bias == 1:
+ _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+ ys_pad_lens = ys_pad_lens + self.predictor_bias
+ pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor(encoder_out, ys_pad, encoder_out_mask,
+ ignore_id=self.ignore_id)
+
+ # 0. sampler
+ decoder_out_1st = None
+ pre_loss_att = None
+ if self.sampling_ratio > 0.0:
+
+
+ if self.use_1st_decoder_loss:
+ sematic_embeds, decoder_out_1st, pre_loss_att = self.sampler_with_grad(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
+ pre_acoustic_embeds)
+ else:
+ sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
+ pre_acoustic_embeds)
+ else:
+ if self.step_cur < 2:
+ logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
+ sematic_embeds = pre_acoustic_embeds
+
+ # 1. Forward decoder
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
+ )
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+
+ if decoder_out_1st is None:
+ decoder_out_1st = decoder_out
+ # 2. Compute attention loss
+ loss_att = self.criterion_att(decoder_out, ys_pad)
+ acc_att = th_accuracy(
+ decoder_out_1st.view(-1, self.vocab_size),
+ ys_pad,
+ ignore_label=self.ignore_id,
+ )
+ loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
+
+ # Compute cer/wer using attention-decoder
+ if self.training or self.error_calculator is None:
+ cer_att, wer_att = None, None
+ else:
+ ys_hat = decoder_out_1st.argmax(dim=-1)
+ cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
+
+ return loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att
+
+ def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
+
+ tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
+ ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
+ if self.share_embedding:
+ ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
+ else:
+ ys_pad_embed = self.decoder.embed(ys_pad_masked)
+ with torch.no_grad():
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
+ )
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+ pred_tokens = decoder_out.argmax(-1)
+ nonpad_positions = ys_pad.ne(self.ignore_id)
+ seq_lens = (nonpad_positions).sum(1)
+ same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
+ input_mask = torch.ones_like(nonpad_positions)
+ bsz, seq_len = ys_pad.size()
+ for li in range(bsz):
+ target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
+ if target_num > 0:
+ input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0)
+ input_mask = input_mask.eq(1)
+ input_mask = input_mask.masked_fill(~nonpad_positions, False)
+ input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
+
+ sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
+ input_mask_expand_dim, 0)
+ return sematic_embeds * tgt_mask, decoder_out * tgt_mask
+
+ def sampler_with_grad(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
+ tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
+ ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
+ if self.share_embedding:
+ ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
+ else:
+ ys_pad_embed = self.decoder.embed(ys_pad_masked)
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
+ )
+ pre_loss_att = self.criterion_att(decoder_outs[0], ys_pad)
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+ pred_tokens = decoder_out.argmax(-1)
+ nonpad_positions = ys_pad.ne(self.ignore_id)
+ seq_lens = (nonpad_positions).sum(1)
+ same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
+ input_mask = torch.ones_like(nonpad_positions)
+ bsz, seq_len = ys_pad.size()
+ for li in range(bsz):
+ target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
+ if target_num > 0:
+ input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0)
+ input_mask = input_mask.eq(1)
+ input_mask = input_mask.masked_fill(~nonpad_positions, False)
+ input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
+
+ sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
+ input_mask_expand_dim, 0)
+
+ return sematic_embeds * tgt_mask, decoder_out * tgt_mask, pre_loss_att
+
+ def _calc_ctc_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ # Calc CTC loss
+ loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
+
+ # Calc CER using CTC
+ cer_ctc = None
+ if not self.training and self.error_calculator is not None:
+ ys_hat = self.ctc.argmax(encoder_out).data
+ cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
+ return loss_ctc, cer_ctc
--
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