705 lines
26 KiB
Python
705 lines
26 KiB
Python
|
import logging
|
||
|
|
from typing import Union, Dict, List, Tuple, Optional
|
||
|
|
|
||
|
|
import time
|
||
|
|
import torch
|
||
|
|
import numpy as np
|
||
|
|
import torch.nn as nn
|
||
|
|
from torch.cuda.amp import autocast
|
||
|
|
|
||
|
|
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
|
||
|
|
from funasr.models.ctc.ctc import CTC
|
||
|
|
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
|
||
|
|
from funasr.metrics.compute_acc import th_accuracy
|
||
|
|
from funasr.train_utils.device_funcs import force_gatherable
|
||
|
|
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
||
|
|
from funasr.utils import postprocess_utils
|
||
|
|
from funasr.utils.datadir_writer import DatadirWriter
|
||
|
|
from funasr.register import tables
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("model_classes", "OpenAIWhisperModel")
|
||
|
|
class OpenAIWhisperModel(nn.Module):
|
||
|
|
"""CTC-attention hybrid Encoder-Decoder model"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
specaug: str = None,
|
||
|
|
specaug_conf: dict = None,
|
||
|
|
normalize: str = None,
|
||
|
|
normalize_conf: dict = None,
|
||
|
|
encoder: str = None,
|
||
|
|
encoder_conf: dict = None,
|
||
|
|
decoder: str = None,
|
||
|
|
decoder_conf: dict = None,
|
||
|
|
ctc: str = None,
|
||
|
|
ctc_conf: 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,
|
||
|
|
share_embedding: bool = False,
|
||
|
|
# preencoder: Optional[AbsPreEncoder] = None,
|
||
|
|
# postencoder: Optional[AbsPostEncoder] = None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
if specaug is not None:
|
||
|
|
specaug_class = tables.specaug_classes.get(specaug)
|
||
|
|
specaug = specaug_class(**specaug_conf)
|
||
|
|
if normalize is not None:
|
||
|
|
normalize_class = tables.normalize_classes.get(normalize)
|
||
|
|
normalize = normalize_class(**normalize_conf)
|
||
|
|
encoder_class = tables.encoder_classes.get(encoder)
|
||
|
|
encoder = encoder_class(input_size=input_size, **encoder_conf)
|
||
|
|
encoder_output_size = encoder.output_size()
|
||
|
|
if decoder is not None:
|
||
|
|
decoder_class = tables.decoder_classes.get(decoder)
|
||
|
|
decoder = decoder_class(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)
|
||
|
|
|
||
|
|
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.specaug = specaug
|
||
|
|
self.normalize = normalize
|
||
|
|
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.interctc_weight = interctc_weight
|
||
|
|
|
||
|
|
# 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
|
||
|
|
# )
|
||
|
|
#
|
||
|
|
self.error_calculator = None
|
||
|
|
if ctc_weight == 0.0:
|
||
|
|
self.ctc = None
|
||
|
|
else:
|
||
|
|
self.ctc = ctc
|
||
|
|
|
||
|
|
self.share_embedding = share_embedding
|
||
|
|
if self.share_embedding:
|
||
|
|
self.decoder.embed = None
|
||
|
|
|
||
|
|
self.length_normalized_loss = length_normalized_loss
|
||
|
|
self.beam_search = None
|
||
|
|
|
||
|
|
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]:
|
||
|
|
"""Encoder + Decoder + Calc loss
|
||
|
|
Args:
|
||
|
|
speech: (Batch, Length, ...)
|
||
|
|
speech_lengths: (Batch, )
|
||
|
|
text: (Batch, Length)
|
||
|
|
text_lengths: (Batch,)
|
||
|
|
"""
|
||
|
|
# 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]
|
||
|
|
|
||
|
|
# 1. Encoder
|
||
|
|
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, acc_att, cer_att, wer_att = None, None, None, None
|
||
|
|
loss_ctc, cer_ctc = None, None
|
||
|
|
stats = dict()
|
||
|
|
|
||
|
|
# decoder: 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
|
||
|
|
|
||
|
|
# decoder: Attention decoder branch
|
||
|
|
loss_att, acc_att, cer_att, wer_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
|
||
|
|
elif self.ctc_weight == 1.0:
|
||
|
|
loss = loss_ctc
|
||
|
|
else:
|
||
|
|
loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
|
||
|
|
|
||
|
|
# Collect Attn branch stats
|
||
|
|
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
|
||
|
|
stats["acc"] = acc_att
|
||
|
|
stats["cer"] = cer_att
|
||
|
|
stats["wer"] = wer_att
|
||
|
|
|
||
|
|
# Collect total loss stats
|
||
|
|
stats["loss"] = torch.clone(loss.detach())
|
||
|
|
|
||
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
||
|
|
if self.length_normalized_loss:
|
||
|
|
batch_size = int((text_lengths + 1).sum())
|
||
|
|
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,
|
||
|
|
**kwargs,
|
||
|
|
) -> 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):
|
||
|
|
|
||
|
|
# Data augmentation
|
||
|
|
if self.specaug is not None and self.training:
|
||
|
|
speech, speech_lengths = self.specaug(speech, speech_lengths)
|
||
|
|
|
||
|
|
# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
|
||
|
|
if self.normalize is not None:
|
||
|
|
speech, speech_lengths = self.normalize(speech, speech_lengths)
|
||
|
|
|
||
|
|
# Forward encoder
|
||
|
|
# feats: (Batch, Length, Dim)
|
||
|
|
# -> encoder_out: (Batch, Length2, Dim2)
|
||
|
|
if self.encoder.interctc_use_conditioning:
|
||
|
|
encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths, ctc=self.ctc)
|
||
|
|
else:
|
||
|
|
encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
|
||
|
|
intermediate_outs = None
|
||
|
|
if isinstance(encoder_out, tuple):
|
||
|
|
intermediate_outs = encoder_out[1]
|
||
|
|
encoder_out = encoder_out[0]
|
||
|
|
|
||
|
|
if intermediate_outs is not None:
|
||
|
|
return (encoder_out, intermediate_outs), encoder_out_lens
|
||
|
|
|
||
|
|
return encoder_out, encoder_out_lens
|
||
|
|
|
||
|
|
def _calc_att_loss(
|
||
|
|
self,
|
||
|
|
encoder_out: torch.Tensor,
|
||
|
|
encoder_out_lens: torch.Tensor,
|
||
|
|
ys_pad: torch.Tensor,
|
||
|
|
ys_pad_lens: torch.Tensor,
|
||
|
|
):
|
||
|
|
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)
|
||
|
|
|
||
|
|
# 2. Compute attention loss
|
||
|
|
loss_att = self.criterion_att(decoder_out, ys_out_pad)
|
||
|
|
acc_att = th_accuracy(
|
||
|
|
decoder_out.view(-1, self.vocab_size),
|
||
|
|
ys_out_pad,
|
||
|
|
ignore_label=self.ignore_id,
|
||
|
|
)
|
||
|
|
|
||
|
|
# 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.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
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
def init_beam_search(
|
||
|
|
self,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
from funasr.models.transformer.search import BeamSearch
|
||
|
|
from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
|
||
|
|
from funasr.models.transformer.scorers.length_bonus import LengthBonus
|
||
|
|
|
||
|
|
# 1. Build ASR model
|
||
|
|
scorers = {}
|
||
|
|
|
||
|
|
if self.ctc != None:
|
||
|
|
ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
|
||
|
|
scorers.update(ctc=ctc)
|
||
|
|
token_list = kwargs.get("token_list")
|
||
|
|
scorers.update(
|
||
|
|
decoder=self.decoder,
|
||
|
|
length_bonus=LengthBonus(len(token_list)),
|
||
|
|
)
|
||
|
|
|
||
|
|
# 3. Build ngram model
|
||
|
|
# ngram is not supported now
|
||
|
|
ngram = None
|
||
|
|
scorers["ngram"] = ngram
|
||
|
|
|
||
|
|
weights = dict(
|
||
|
|
decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.5),
|
||
|
|
ctc=kwargs.get("decoding_ctc_weight", 0.5),
|
||
|
|
lm=kwargs.get("lm_weight", 0.0),
|
||
|
|
ngram=kwargs.get("ngram_weight", 0.0),
|
||
|
|
length_bonus=kwargs.get("penalty", 0.0),
|
||
|
|
)
|
||
|
|
beam_search = BeamSearch(
|
||
|
|
beam_size=kwargs.get("beam_size", 10),
|
||
|
|
weights=weights,
|
||
|
|
scorers=scorers,
|
||
|
|
sos=self.sos,
|
||
|
|
eos=self.eos,
|
||
|
|
vocab_size=len(token_list),
|
||
|
|
token_list=token_list,
|
||
|
|
pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
|
||
|
|
)
|
||
|
|
|
||
|
|
self.beam_search = beam_search
|
||
|
|
|
||
|
|
def inference(
|
||
|
|
self,
|
||
|
|
data_in,
|
||
|
|
data_lengths=None,
|
||
|
|
key: list = None,
|
||
|
|
tokenizer=None,
|
||
|
|
frontend=None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
|
||
|
|
if kwargs.get("batch_size", 1) > 1:
|
||
|
|
raise NotImplementedError("batch decoding is not implemented")
|
||
|
|
|
||
|
|
# init beamsearch
|
||
|
|
if self.beam_search is None:
|
||
|
|
logging.info("enable beam_search")
|
||
|
|
self.init_beam_search(**kwargs)
|
||
|
|
self.nbest = kwargs.get("nbest", 1)
|
||
|
|
|
||
|
|
meta_data = {}
|
||
|
|
if (
|
||
|
|
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
|
||
|
|
): # fbank
|
||
|
|
speech, speech_lengths = data_in, data_lengths
|
||
|
|
if len(speech.shape) < 3:
|
||
|
|
speech = speech[None, :, :]
|
||
|
|
if speech_lengths is None:
|
||
|
|
speech_lengths = speech.shape[1]
|
||
|
|
else:
|
||
|
|
# extract fbank feats
|
||
|
|
time1 = time.perf_counter()
|
||
|
|
audio_sample_list = load_audio_text_image_video(
|
||
|
|
data_in,
|
||
|
|
fs=frontend.fs,
|
||
|
|
audio_fs=kwargs.get("fs", 16000),
|
||
|
|
data_type=kwargs.get("data_type", "sound"),
|
||
|
|
tokenizer=tokenizer,
|
||
|
|
)
|
||
|
|
time2 = time.perf_counter()
|
||
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
||
|
|
speech, speech_lengths = extract_fbank(
|
||
|
|
audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
|
||
|
|
)
|
||
|
|
time3 = time.perf_counter()
|
||
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||
|
|
meta_data["batch_data_time"] = (
|
||
|
|
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
||
|
|
)
|
||
|
|
|
||
|
|
speech = speech.to(device=kwargs["device"])
|
||
|
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
||
|
|
# Encoder
|
||
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
||
|
|
if isinstance(encoder_out, tuple):
|
||
|
|
encoder_out = encoder_out[0]
|
||
|
|
|
||
|
|
# c. Passed the encoder result and the beam search
|
||
|
|
nbest_hyps = self.beam_search(
|
||
|
|
x=encoder_out[0],
|
||
|
|
maxlenratio=kwargs.get("maxlenratio", 0.0),
|
||
|
|
minlenratio=kwargs.get("minlenratio", 0.0),
|
||
|
|
)
|
||
|
|
|
||
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
||
|
|
|
||
|
|
results = []
|
||
|
|
b, n, d = encoder_out.size()
|
||
|
|
for i in range(b):
|
||
|
|
|
||
|
|
for nbest_idx, hyp in enumerate(nbest_hyps):
|
||
|
|
ibest_writer = None
|
||
|
|
if kwargs.get("output_dir") is not None:
|
||
|
|
if not hasattr(self, "writer"):
|
||
|
|
self.writer = DatadirWriter(kwargs.get("output_dir"))
|
||
|
|
ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
|
||
|
|
|
||
|
|
# remove sos/eos and get results
|
||
|
|
last_pos = -1
|
||
|
|
if isinstance(hyp.yseq, list):
|
||
|
|
token_int = hyp.yseq[1:last_pos]
|
||
|
|
else:
|
||
|
|
token_int = hyp.yseq[1:last_pos].tolist()
|
||
|
|
|
||
|
|
# remove blank symbol id, which is assumed to be 0
|
||
|
|
token_int = list(
|
||
|
|
filter(
|
||
|
|
lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
# Change integer-ids to tokens
|
||
|
|
token = tokenizer.ids2tokens(token_int)
|
||
|
|
text = tokenizer.tokens2text(token)
|
||
|
|
|
||
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
||
|
|
result_i = {"key": key[i], "token": token, "text": text_postprocessed}
|
||
|
|
results.append(result_i)
|
||
|
|
|
||
|
|
if ibest_writer is not None:
|
||
|
|
ibest_writer["token"][key[i]] = " ".join(token)
|
||
|
|
ibest_writer["text"][key[i]] = text_postprocessed
|
||
|
|
|
||
|
|
return results, meta_data
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("model_classes", "OpenAIWhisperLIDModel")
|
||
|
|
class OpenAIWhisperLIDModel(nn.Module):
|
||
|
|
"""WhisperEncoder and EResNet based LID Model"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_size: int,
|
||
|
|
specaug: str = None,
|
||
|
|
specaug_conf: dict = None,
|
||
|
|
encoder: str = None,
|
||
|
|
encoder_conf: dict = None,
|
||
|
|
lid_predictor: str = None,
|
||
|
|
lid_predictor_conf: dict = None,
|
||
|
|
proj_dim: int = None,
|
||
|
|
clip_frames: int = None,
|
||
|
|
random_clip: bool = False,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
if specaug is not None:
|
||
|
|
specaug_class = tables.specaug_classes.get(specaug)
|
||
|
|
specaug = specaug_class(**specaug_conf)
|
||
|
|
encoder_class = tables.encoder_classes.get(encoder)
|
||
|
|
encoder = encoder_class(**encoder_conf)
|
||
|
|
lid_predictor_class = tables.lid_predictor_classes.get(lid_predictor)
|
||
|
|
lid_predictor = lid_predictor_class(**lid_predictor_conf)
|
||
|
|
if encoder.output_size() != proj_dim:
|
||
|
|
self.proj_layer = torch.nn.Linear(encoder.output_size(), proj_dim)
|
||
|
|
else:
|
||
|
|
self.proj_layer = None
|
||
|
|
self.output_layer = torch.nn.Linear(lid_predictor.output_size(), vocab_size)
|
||
|
|
self.criterion_lid = LabelSmoothingLoss(
|
||
|
|
size=vocab_size,
|
||
|
|
padding_idx=-1,
|
||
|
|
smoothing=0.0,
|
||
|
|
normalize_length=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
self.specaug = specaug
|
||
|
|
self.encoder = encoder
|
||
|
|
self.lid_predictor = lid_predictor
|
||
|
|
self.clip_frames = clip_frames
|
||
|
|
self.random_clip = random_clip
|
||
|
|
self.normalize = None
|
||
|
|
self.beam_search = None
|
||
|
|
if not hasattr(self.encoder, "interctc_use_conditioning"):
|
||
|
|
self.encoder.interctc_use_conditioning = False
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
speech: torch.Tensor, # may be padding
|
||
|
|
speech_lengths: torch.Tensor, # actual length
|
||
|
|
lid: torch.Tensor, # lid label, (batch_size, 1)
|
||
|
|
lid_lengths: torch.Tensor,
|
||
|
|
):
|
||
|
|
assert lid.shape[1] == 1
|
||
|
|
batch_size = speech.shape[0]
|
||
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
||
|
|
|
||
|
|
# re-generate encoder_out
|
||
|
|
if self.clip_frames is None:
|
||
|
|
reduced_encoder_out = (
|
||
|
|
torch.zeros(batch_size, encoder_out_lens.max(), encoder_out.shape[-1])
|
||
|
|
.to(encoder_out.dtype)
|
||
|
|
.to(encoder_out.device)
|
||
|
|
)
|
||
|
|
for i, enc_length in enumerate(encoder_out_lens):
|
||
|
|
reduced_encoder_out[i, :enc_length] = encoder_out[i, :enc_length]
|
||
|
|
else:
|
||
|
|
reduced_encoder_out = (
|
||
|
|
torch.zeros(batch_size, self.clip_frames, encoder_out.shape[-1])
|
||
|
|
.to(encoder_out.dtype)
|
||
|
|
.to(encoder_out.device)
|
||
|
|
)
|
||
|
|
if self.random_clip:
|
||
|
|
for i, enc_length in enumerate(encoder_out_lens):
|
||
|
|
if enc_length <= self.clip_frames:
|
||
|
|
reduced_encoder_out[i, :enc_length] = encoder_out[i, :enc_length]
|
||
|
|
encoder_out_lens[i] = enc_length
|
||
|
|
else:
|
||
|
|
max_start_index = enc_length.item() - self.clip_frames
|
||
|
|
start_index = np.random.randint(0, max_start_index + 1)
|
||
|
|
reduced_encoder_out[i, : self.clip_frames] = encoder_out[
|
||
|
|
i, start_index : start_index + self.clip_frames
|
||
|
|
]
|
||
|
|
encoder_out_lens[i] = self.clip_frames
|
||
|
|
else:
|
||
|
|
for i, enc_length in enumerate(encoder_out_lens):
|
||
|
|
enc_length = self.clip_frames if enc_length >= self.clip_frames else enc_length
|
||
|
|
reduced_encoder_out[i, :enc_length] = encoder_out[i, :enc_length]
|
||
|
|
encoder_out_lens[i] = enc_length
|
||
|
|
if self.proj_layer is not None:
|
||
|
|
reduced_encoder_out = self.proj_layer(reduced_encoder_out)
|
||
|
|
lid_output = self.lid_predictor(reduced_encoder_out, encoder_out_lens) # (B, D)
|
||
|
|
lid_logits = self.output_layer(lid_output) # (B, num_classes)
|
||
|
|
loss = self.criterion_lid(lid_logits[:, None, :], lid)
|
||
|
|
with torch.no_grad():
|
||
|
|
_, predicted_lid = torch.max(lid_logits, 1)
|
||
|
|
correct = (predicted_lid == lid[:, 0]).sum().item()
|
||
|
|
lid_acc = correct * 1.0 / lid_logits.shape[0]
|
||
|
|
stats = dict()
|
||
|
|
stats["batch_size"] = batch_size
|
||
|
|
stats["loss"] = torch.clone(loss.detach())
|
||
|
|
stats["acc"] = lid_acc
|
||
|
|
stats["token_length"] = speech_lengths.max()
|
||
|
|
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):
|
||
|
|
|
||
|
|
# Data augmentation
|
||
|
|
if self.specaug is not None and self.training:
|
||
|
|
speech = speech.permute(0, 2, 1)
|
||
|
|
# suit for whisper padding
|
||
|
|
padded_speech_lengths = torch.ones_like(speech_lengths) * speech.shape[1]
|
||
|
|
speech, padded_speech_lengths = self.specaug(speech, padded_speech_lengths)
|
||
|
|
speech = speech.permute(0, 2, 1)
|
||
|
|
|
||
|
|
# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
|
||
|
|
if self.normalize is not None:
|
||
|
|
speech, speech_lengths = self.normalize(speech, speech_lengths)
|
||
|
|
|
||
|
|
# Forward encoder
|
||
|
|
# feats: (Batch, Length, Dim)
|
||
|
|
# -> encoder_out: (Batch, Length2, Dim2)
|
||
|
|
if self.encoder.interctc_use_conditioning:
|
||
|
|
encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths, ctc=self.ctc)
|
||
|
|
else:
|
||
|
|
encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
|
||
|
|
intermediate_outs = None
|
||
|
|
if isinstance(encoder_out, tuple):
|
||
|
|
intermediate_outs = encoder_out[1]
|
||
|
|
encoder_out = encoder_out[0]
|
||
|
|
|
||
|
|
if intermediate_outs is not None:
|
||
|
|
return (encoder_out, intermediate_outs), encoder_out_lens
|
||
|
|
|
||
|
|
return encoder_out, encoder_out_lens
|
||
|
|
|
||
|
|
def inference(
|
||
|
|
self,
|
||
|
|
data_in,
|
||
|
|
data_lengths=None,
|
||
|
|
key: list = None,
|
||
|
|
tokenizer=None,
|
||
|
|
frontend=None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
|
||
|
|
if kwargs.get("batch_size", 1) > 1:
|
||
|
|
raise NotImplementedError("batch decoding is not implemented")
|
||
|
|
|
||
|
|
meta_data = {}
|
||
|
|
if (
|
||
|
|
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
|
||
|
|
): # fbank
|
||
|
|
speech, speech_lengths = data_in, data_lengths
|
||
|
|
if len(speech.shape) < 3:
|
||
|
|
speech = speech[None, :, :]
|
||
|
|
if speech_lengths is None:
|
||
|
|
speech_lengths = speech.shape[1]
|
||
|
|
else:
|
||
|
|
# extract fbank feats
|
||
|
|
time1 = time.perf_counter()
|
||
|
|
audio_sample_list = load_audio_text_image_video(
|
||
|
|
data_in,
|
||
|
|
fs=frontend.fs,
|
||
|
|
audio_fs=kwargs.get("fs", 16000),
|
||
|
|
data_type=kwargs.get("data_type", "sound"),
|
||
|
|
tokenizer=tokenizer,
|
||
|
|
)
|
||
|
|
time2 = time.perf_counter()
|
||
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
||
|
|
speech, speech_lengths = extract_fbank(
|
||
|
|
audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
|
||
|
|
)
|
||
|
|
time3 = time.perf_counter()
|
||
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||
|
|
meta_data["batch_data_time"] = (
|
||
|
|
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
||
|
|
)
|
||
|
|
|
||
|
|
speech = speech.to(device=kwargs["device"])
|
||
|
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
||
|
|
# Encoder
|
||
|
|
enc, enc_out_lens = self.encode(speech, speech_lengths)
|
||
|
|
|
||
|
|
inference_clip_length = kwargs.get("inference_clip_length", None)
|
||
|
|
if self.clip_frames is not None:
|
||
|
|
if inference_clip_length is None:
|
||
|
|
reduced_enc = (
|
||
|
|
torch.zeros(enc.shape[0], self.clip_frames, enc.shape[-1])
|
||
|
|
.to(enc.dtype)
|
||
|
|
.to(enc.device)
|
||
|
|
)
|
||
|
|
for i, enc_length in enumerate(enc_out_lens):
|
||
|
|
enc_length = self.clip_frames if enc_length >= self.clip_frames else enc_length
|
||
|
|
reduced_enc[i, :enc_length] = enc[i, :enc_length]
|
||
|
|
enc_out_lens[i] = enc_length
|
||
|
|
else:
|
||
|
|
assert inference_clip_length > 0, "inference_clip_length must be larger than 0"
|
||
|
|
reduced_enc = (
|
||
|
|
torch.zeros(enc.shape[0], inference_clip_length, enc.shape[-1])
|
||
|
|
.to(enc.dtype)
|
||
|
|
.to(enc.device)
|
||
|
|
)
|
||
|
|
for i, enc_length in enumerate(enc_out_lens):
|
||
|
|
enc_length = (
|
||
|
|
inference_clip_length if enc_length >= inference_clip_length else enc_length
|
||
|
|
)
|
||
|
|
reduced_enc[i, :enc_length] = enc[i, :enc_length]
|
||
|
|
enc_out_lens[i] = enc_length
|
||
|
|
else:
|
||
|
|
reduced_enc = (
|
||
|
|
torch.zeros(enc.shape[0], enc_out_lens.max(), enc.shape[-1])
|
||
|
|
.to(enc.dtype)
|
||
|
|
.to(enc.device)
|
||
|
|
)
|
||
|
|
for i, enc_length in enumerate(enc_out_lens):
|
||
|
|
reduced_enc[i, :enc_length] = enc[i, :enc_length]
|
||
|
|
|
||
|
|
if self.proj_layer is not None:
|
||
|
|
reduced_enc = self.proj_layer(reduced_enc)
|
||
|
|
lid_output = self.lid_predictor(reduced_enc, enc_out_lens) # (B, D)
|
||
|
|
lid_logits = self.output_layer(lid_output) # (B, num_classes)
|
||
|
|
|
||
|
|
_, predicted_lid_index = torch.max(lid_logits, 1)
|
||
|
|
predicted_lid = tokenizer.ids2tokens([predicted_lid_index[0].cpu()])[0]
|
||
|
|
|
||
|
|
if kwargs.get("output_dir") is not None:
|
||
|
|
if not hasattr(self, "writer"):
|
||
|
|
self.writer = DatadirWriter(kwargs.get("output_dir"))
|
||
|
|
lid_writer = self.writer["lid"]
|
||
|
|
lid_writer[key[0]] = predicted_lid
|
||
|
|
|
||
|
|
results = [{"key": key[0], "lid": predicted_lid}]
|
||
|
|
|
||
|
|
return results, meta_data
|