|
|
@@ -0,0 +1,175 @@
|
|
|
+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 numpy as np
|
|
|
+from typeguard import check_argument_types
|
|
|
+
|
|
|
+from funasr.models.encoder.abs_encoder import AbsEncoder
|
|
|
+from funasr.models.frontend.abs_frontend import AbsFrontend
|
|
|
+from funasr.models.predictor.cif import mae_loss
|
|
|
+from funasr.modules.add_sos_eos import add_sos_eos
|
|
|
+from funasr.modules.nets_utils import make_pad_mask, pad_list
|
|
|
+from funasr.torch_utils.device_funcs import force_gatherable
|
|
|
+from funasr.train.abs_espnet_model import AbsESPnetModel
|
|
|
+from funasr.models.predictor.cif import CifPredictorV3
|
|
|
+
|
|
|
+
|
|
|
+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 TimestampPredictor(AbsESPnetModel):
|
|
|
+ """
|
|
|
+ Author: Speech Lab, Alibaba Group, China
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ frontend: Optional[AbsFrontend],
|
|
|
+ encoder: AbsEncoder,
|
|
|
+ predictor: CifPredictorV3,
|
|
|
+ predictor_bias: int = 0,
|
|
|
+ token_list=None,
|
|
|
+ ):
|
|
|
+ assert check_argument_types()
|
|
|
+
|
|
|
+ super().__init__()
|
|
|
+ # note that eos is the same as sos (equivalent ID)
|
|
|
+
|
|
|
+ self.frontend = frontend
|
|
|
+ self.encoder = encoder
|
|
|
+ self.encoder.interctc_use_conditioning = False
|
|
|
+
|
|
|
+ self.predictor = predictor
|
|
|
+ self.predictor_bias = predictor_bias
|
|
|
+ self.criterion_pre = mae_loss()
|
|
|
+ self.token_list = token_list
|
|
|
+
|
|
|
+ def forward(
|
|
|
+ self,
|
|
|
+ speech: torch.Tensor,
|
|
|
+ speech_lengths: torch.Tensor,
|
|
|
+ text: torch.Tensor,
|
|
|
+ text_lengths: torch.Tensor,
|
|
|
+ ) -> 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,)
|
|
|
+ """
|
|
|
+ assert text_lengths.dim() == 1, text_lengths.shape
|
|
|
+ # Check that batch_size is unified
|
|
|
+ assert (
|
|
|
+ speech.shape[0]
|
|
|
+ == speech_lengths.shape[0]
|
|
|
+ == text.shape[0]
|
|
|
+ == text_lengths.shape[0]
|
|
|
+ ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
|
|
|
+ batch_size = speech.shape[0]
|
|
|
+ # for data-parallel
|
|
|
+ text = text[:, : text_lengths.max()]
|
|
|
+ speech = speech[:, :speech_lengths.max()]
|
|
|
+
|
|
|
+ # 1. Encoder
|
|
|
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
+
|
|
|
+ encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
|
|
+ encoder_out.device)
|
|
|
+ if self.predictor_bias == 1:
|
|
|
+ _, text = add_sos_eos(text, 1, 2, -1)
|
|
|
+ text_lengths = text_lengths + self.predictor_bias
|
|
|
+ _, _, _, _, pre_token_length2 = self.predictor(encoder_out, text, encoder_out_mask, ignore_id=-1)
|
|
|
+
|
|
|
+ # loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
|
|
|
+ loss_pre = self.criterion_pre(text_lengths.type_as(pre_token_length2), pre_token_length2)
|
|
|
+
|
|
|
+ loss = loss_pre
|
|
|
+ stats = dict()
|
|
|
+
|
|
|
+ # Collect Attn branch stats
|
|
|
+ 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 encode(
|
|
|
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor
|
|
|
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
|
+ """Frontend + Encoder. Note that this method is used by asr_inference.py
|
|
|
+
|
|
|
+ Args:
|
|
|
+ speech: (Batch, Length, ...)
|
|
|
+ speech_lengths: (Batch, )
|
|
|
+ """
|
|
|
+ with autocast(False):
|
|
|
+ # 1. Extract feats
|
|
|
+ feats, feats_lengths = self._extract_feats(speech, speech_lengths)
|
|
|
+
|
|
|
+ # 4. Forward encoder
|
|
|
+ # feats: (Batch, Length, Dim)
|
|
|
+ # -> encoder_out: (Batch, Length2, Dim2)
|
|
|
+ encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
|
|
|
+
|
|
|
+ return encoder_out, encoder_out_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 calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
|
|
|
+ encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
|
|
+ encoder_out.device)
|
|
|
+ ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
|
|
|
+ encoder_out_mask,
|
|
|
+ token_num)
|
|
|
+ return ds_alphas, ds_cif_peak, us_alphas, us_peaks
|
|
|
+
|
|
|
+ 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}
|
zhifu gao