Spaces:
Running
Running
File size: 10,211 Bytes
0883aa1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 |
# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
from typing import Optional
import torch
from torch import nn
from modules.wenet_extractor.utils.mask import make_pad_mask
class Predictor(nn.Module):
def __init__(
self,
idim,
l_order,
r_order,
threshold=1.0,
dropout=0.1,
smooth_factor=1.0,
noise_threshold=0,
tail_threshold=0.45,
):
super().__init__()
self.pad = nn.ConstantPad1d((l_order, r_order), 0.0)
self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
self.cif_output = nn.Linear(idim, 1)
self.dropout = torch.nn.Dropout(p=dropout)
self.threshold = threshold
self.smooth_factor = smooth_factor
self.noise_threshold = noise_threshold
self.tail_threshold = tail_threshold
def forward(
self,
hidden,
target_label: Optional[torch.Tensor] = None,
mask: torch.Tensor = torch.tensor(0),
ignore_id: int = -1,
mask_chunk_predictor: Optional[torch.Tensor] = None,
target_label_length: Optional[torch.Tensor] = None,
):
h = hidden
context = h.transpose(1, 2)
queries = self.pad(context)
memory = self.cif_conv1d(queries)
output = memory + context
output = self.dropout(output)
output = output.transpose(1, 2)
output = torch.relu(output)
output = self.cif_output(output)
alphas = torch.sigmoid(output)
alphas = torch.nn.functional.relu(
alphas * self.smooth_factor - self.noise_threshold
)
if mask is not None:
mask = mask.transpose(-1, -2).float()
alphas = alphas * mask
if mask_chunk_predictor is not None:
alphas = alphas * mask_chunk_predictor
alphas = alphas.squeeze(-1)
mask = mask.squeeze(-1)
if target_label_length is not None:
target_length = target_label_length
elif target_label is not None:
target_length = (target_label != ignore_id).float().sum(-1)
else:
target_length = None
token_num = alphas.sum(-1)
if target_length is not None:
alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
elif self.tail_threshold > 0.0:
hidden, alphas, token_num = self.tail_process_fn(
hidden, alphas, token_num, mask=mask
)
acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
if target_length is None and self.tail_threshold > 0.0:
token_num_int = torch.max(token_num).type(torch.int32).item()
acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
return acoustic_embeds, token_num, alphas, cif_peak
def tail_process_fn(
self,
hidden,
alphas,
token_num: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = None,
):
b, t, d = hidden.size()
tail_threshold = self.tail_threshold
if mask is not None:
zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
ones_t = torch.ones_like(zeros_t)
mask_1 = torch.cat([mask, zeros_t], dim=1)
mask_2 = torch.cat([ones_t, mask], dim=1)
mask = mask_2 - mask_1
tail_threshold = mask * tail_threshold
alphas = torch.cat([alphas, zeros_t], dim=1)
alphas = torch.add(alphas, tail_threshold)
else:
tail_threshold_tensor = torch.tensor(
[tail_threshold], dtype=alphas.dtype
).to(alphas.device)
tail_threshold_tensor = torch.reshape(tail_threshold_tensor, (1, 1))
alphas = torch.cat([alphas, tail_threshold_tensor], dim=1)
zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
hidden = torch.cat([hidden, zeros], dim=1)
token_num = alphas.sum(dim=-1)
token_num_floor = torch.floor(token_num)
return hidden, alphas, token_num_floor
def gen_frame_alignments(
self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None
):
batch_size, maximum_length = alphas.size()
int_type = torch.int32
is_training = self.training
if is_training:
token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
else:
token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
max_token_num = torch.max(token_num).item()
alphas_cumsum = torch.cumsum(alphas, dim=1)
alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
index = torch.ones([batch_size, max_token_num], dtype=int_type)
index = torch.cumsum(index, dim=1)
index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
index_div_bool_zeros = index_div.eq(0)
index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
index_div_bool_zeros_count = torch.clamp(
index_div_bool_zeros_count, 0, encoder_sequence_length.max()
)
token_num_mask = (~make_pad_mask(token_num, max_len=max_token_num)).to(
token_num.device
)
index_div_bool_zeros_count *= token_num_mask
index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(
1, 1, maximum_length
)
ones = torch.ones_like(index_div_bool_zeros_count_tile)
zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
ones = torch.cumsum(ones, dim=2)
cond = index_div_bool_zeros_count_tile == ones
index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(
torch.bool
)
index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(
int_type
)
index_div_bool_zeros_count_tile_out = torch.sum(
index_div_bool_zeros_count_tile, dim=1
)
index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(
int_type
)
predictor_mask = (
(
~make_pad_mask(
encoder_sequence_length, max_len=encoder_sequence_length.max()
)
)
.type(int_type)
.to(encoder_sequence_length.device)
)
index_div_bool_zeros_count_tile_out = (
index_div_bool_zeros_count_tile_out * predictor_mask
)
predictor_alignments = index_div_bool_zeros_count_tile_out
predictor_alignments_length = predictor_alignments.sum(-1).type(
encoder_sequence_length.dtype
)
return predictor_alignments.detach(), predictor_alignments_length.detach()
class MAELoss(nn.Module):
def __init__(self, normalize_length=False):
super(MAELoss, self).__init__()
self.normalize_length = normalize_length
self.criterion = torch.nn.L1Loss(reduction="sum")
def forward(self, token_length, pre_token_length):
loss_token_normalizer = token_length.size(0)
if self.normalize_length:
loss_token_normalizer = token_length.sum().type(torch.float32)
loss = self.criterion(token_length, pre_token_length)
loss = loss / loss_token_normalizer
return loss
def cif(hidden: torch.Tensor, alphas: torch.Tensor, threshold: float):
batch_size, len_time, hidden_size = hidden.size()
# loop varss
integrate = torch.zeros([batch_size], device=hidden.device)
frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
# intermediate vars along time
list_fires = []
list_frames = []
for t in range(len_time):
alpha = alphas[:, t]
distribution_completion = (
torch.ones([batch_size], device=hidden.device) - integrate
)
integrate += alpha
list_fires.append(integrate)
fire_place = integrate >= threshold
integrate = torch.where(
fire_place,
integrate - torch.ones([batch_size], device=hidden.device),
integrate,
)
cur = torch.where(fire_place, distribution_completion, alpha)
remainds = alpha - cur
frame += cur[:, None] * hidden[:, t, :]
list_frames.append(frame)
frame = torch.where(
fire_place[:, None].repeat(1, hidden_size),
remainds[:, None] * hidden[:, t, :],
frame,
)
fires = torch.stack(list_fires, 1)
frames = torch.stack(list_frames, 1)
list_ls = []
len_labels = torch.round(alphas.sum(-1)).int()
max_label_len = len_labels.max()
for b in range(batch_size):
fire = fires[b, :]
l = torch.index_select(
frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze()
)
pad_l = torch.zeros(
[int(max_label_len - l.size(0)), hidden_size], device=hidden.device
)
list_ls.append(torch.cat([l, pad_l], 0))
return torch.stack(list_ls, 0), fires
|