Spaces:
Runtime error
Runtime error
File size: 9,072 Bytes
0102e16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 |
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
import time
import copy
import torch
from torch.cuda.amp import autocast
from typing import Union, Dict, List, Tuple, Optional
from funasr_detach.register import tables
from funasr_detach.models.ctc.ctc import CTC
from funasr_detach.utils import postprocess_utils
from funasr_detach.utils.datadir_writer import DatadirWriter
from funasr_detach.models.paraformer.cif_predictor import mae_loss
from funasr_detach.train_utils.device_funcs import force_gatherable
from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask
from funasr_detach.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank
@tables.register("model_classes", "MonotonicAligner")
class MonotonicAligner(torch.nn.Module):
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model
https://arxiv.org/abs/2301.12343
"""
def __init__(
self,
input_size: int = 80,
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,
predictor: str = None,
predictor_conf: Optional[Dict] = None,
predictor_bias: int = 0,
length_normalized_loss: bool = False,
**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()
predictor_class = tables.predictor_classes.get(predictor)
predictor = predictor_class(**predictor_conf)
self.specaug = specaug
self.normalize = normalize
self.encoder = encoder
self.predictor = predictor
self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
self.predictor_bias = predictor_bias
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 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 encode(
self,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""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
encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
return encoder_out, encoder_out_lens
def inference(
self,
data_in,
data_lengths=None,
key: list = None,
tokenizer=None,
frontend=None,
**kwargs,
):
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
audio_list, text_token_int_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_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)
# predictor
text_lengths = torch.tensor([len(i) + 1 for i in text_token_int_list]).to(
encoder_out.device
)
_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(
encoder_out, encoder_out_lens, token_num=text_lengths
)
results = []
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["tp_res"]
for i, (us_alpha, us_peak, token_int) in enumerate(
zip(us_alphas, us_peaks, text_token_int_list)
):
token = tokenizer.ids2tokens(token_int)
timestamp_str, timestamp = ts_prediction_lfr6_standard(
us_alpha[: encoder_out_lens[i] * 3],
us_peak[: encoder_out_lens[i] * 3],
copy.copy(token),
)
text_postprocessed, time_stamp_postprocessed, _ = (
postprocess_utils.sentence_postprocess(token, timestamp)
)
result_i = {
"key": key[i],
"text": text_postprocessed,
"timestamp": time_stamp_postprocessed,
}
results.append(result_i)
if ibest_writer:
# ibest_writer["token"][key[i]] = " ".join(token)
ibest_writer["timestamp_list"][key[i]] = time_stamp_postprocessed
ibest_writer["timestamp_str"][key[i]] = timestamp_str
return results, meta_data
|