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 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.models.base_model import FunASRModel
|
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(FunASRModel):
|
"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
"""
|
|
def __init__(
|
self,
|
frontend: Optional[AbsFrontend],
|
encoder: AbsEncoder,
|
predictor: CifPredictorV3,
|
predictor_bias: int = 0,
|
token_list=None,
|
):
|
|
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}
|
