Spaces:
Runtime error
Runtime error
File size: 12,423 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 |
import copy
from typing import Optional
from typing import Tuple
from typing import Union
import logging
import humanfriendly
import numpy as np
import torch
import torch.nn as nn
try:
from torch_complex.tensor import ComplexTensor
except:
print("Please install torch_complex firstly")
from funasr_detach.frontends.utils.log_mel import LogMel
from funasr_detach.frontends.utils.stft import Stft
from funasr_detach.frontends.utils.frontend import Frontend
from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask
class DefaultFrontend(nn.Module):
"""Conventional frontend structure for ASR.
Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN
"""
def __init__(
self,
fs: Union[int, str] = 16000,
n_fft: int = 512,
win_length: int = None,
hop_length: int = 128,
window: Optional[str] = "hann",
center: bool = True,
normalized: bool = False,
onesided: bool = True,
n_mels: int = 80,
fmin: int = None,
fmax: int = None,
htk: bool = False,
frontend_conf: Optional[dict] = None,
apply_stft: bool = True,
use_channel: int = None,
):
super().__init__()
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)
# Deepcopy (In general, dict shouldn't be used as default arg)
frontend_conf = copy.deepcopy(frontend_conf)
self.hop_length = hop_length
if apply_stft:
self.stft = Stft(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
center=center,
window=window,
normalized=normalized,
onesided=onesided,
)
else:
self.stft = None
self.apply_stft = apply_stft
if frontend_conf is not None:
self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf)
else:
self.frontend = None
self.logmel = LogMel(
fs=fs,
n_fft=n_fft,
n_mels=n_mels,
fmin=fmin,
fmax=fmax,
htk=htk,
)
self.n_mels = n_mels
self.use_channel = use_channel
self.frontend_type = "default"
def output_size(self) -> int:
return self.n_mels
def forward(
self, input: torch.Tensor, input_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# 1. Domain-conversion: e.g. Stft: time -> time-freq
if self.stft is not None:
input_stft, feats_lens = self._compute_stft(input, input_lengths)
else:
input_stft = ComplexTensor(input[..., 0], input[..., 1])
feats_lens = input_lengths
# 2. [Option] Speech enhancement
if self.frontend is not None:
assert isinstance(input_stft, ComplexTensor), type(input_stft)
# input_stft: (Batch, Length, [Channel], Freq)
input_stft, _, mask = self.frontend(input_stft, feats_lens)
# 3. [Multi channel case]: Select a channel
if input_stft.dim() == 4:
# h: (B, T, C, F) -> h: (B, T, F)
if self.training:
if self.use_channel is not None:
input_stft = input_stft[:, :, self.use_channel, :]
else:
# Select 1ch randomly
ch = np.random.randint(input_stft.size(2))
input_stft = input_stft[:, :, ch, :]
else:
# Use the first channel
input_stft = input_stft[:, :, 0, :]
# 4. STFT -> Power spectrum
# h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F)
input_power = input_stft.real**2 + input_stft.imag**2
# 5. Feature transform e.g. Stft -> Log-Mel-Fbank
# input_power: (Batch, [Channel,] Length, Freq)
# -> input_feats: (Batch, Length, Dim)
input_feats, _ = self.logmel(input_power, feats_lens)
return input_feats, feats_lens
def _compute_stft(
self, input: torch.Tensor, input_lengths: torch.Tensor
) -> torch.Tensor:
input_stft, feats_lens = self.stft(input, input_lengths)
assert input_stft.dim() >= 4, input_stft.shape
# "2" refers to the real/imag parts of Complex
assert input_stft.shape[-1] == 2, input_stft.shape
# Change torch.Tensor to ComplexTensor
# input_stft: (..., F, 2) -> (..., F)
input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1])
return input_stft, feats_lens
class MultiChannelFrontend(nn.Module):
"""Conventional frontend structure for ASR.
Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN
"""
def __init__(
self,
fs: Union[int, str] = 16000,
n_fft: int = 512,
win_length: int = None,
hop_length: int = None,
frame_length: int = None,
frame_shift: int = None,
window: Optional[str] = "hann",
center: bool = True,
normalized: bool = False,
onesided: bool = True,
n_mels: int = 80,
fmin: int = None,
fmax: int = None,
htk: bool = False,
frontend_conf: Optional[dict] = None,
apply_stft: bool = True,
use_channel: int = None,
lfr_m: int = 1,
lfr_n: int = 1,
cmvn_file: str = None,
mc: bool = True,
):
super().__init__()
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)
# Deepcopy (In general, dict shouldn't be used as default arg)
frontend_conf = copy.deepcopy(frontend_conf)
if win_length is None and hop_length is None:
self.win_length = frame_length * 16
self.hop_length = frame_shift * 16
elif frame_length is None and frame_shift is None:
self.win_length = self.win_length
self.hop_length = self.hop_length
else:
logging.error(
"Only one of (win_length, hop_length) and (frame_length, frame_shift)"
"can be set."
)
exit(1)
if apply_stft:
self.stft = Stft(
n_fft=n_fft,
win_length=self.win_length,
hop_length=self.hop_length,
center=center,
window=window,
normalized=normalized,
onesided=onesided,
)
else:
self.stft = None
self.apply_stft = apply_stft
if frontend_conf is not None:
self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf)
else:
self.frontend = None
self.logmel = LogMel(
fs=fs,
n_fft=n_fft,
n_mels=n_mels,
fmin=fmin,
fmax=fmax,
htk=htk,
)
self.n_mels = n_mels
self.use_channel = use_channel
self.mc = mc
if not self.mc:
if self.use_channel is not None:
logging.info("use the channel %d" % (self.use_channel))
else:
logging.info("random select channel")
self.cmvn_file = cmvn_file
if self.cmvn_file is not None:
mean, std = self._load_cmvn(self.cmvn_file)
self.register_buffer("mean", torch.from_numpy(mean))
self.register_buffer("std", torch.from_numpy(std))
self.frontend_type = "multichannelfrontend"
def output_size(self) -> int:
return self.n_mels
def forward(
self, input: torch.Tensor, input_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# 1. Domain-conversion: e.g. Stft: time -> time-freq
# import pdb;pdb.set_trace()
if self.stft is not None:
input_stft, feats_lens = self._compute_stft(input, input_lengths)
else:
input_stft = ComplexTensor(input[..., 0], input[..., 1])
feats_lens = input_lengths
# 2. [Option] Speech enhancement
if self.frontend is not None:
assert isinstance(input_stft, ComplexTensor), type(input_stft)
# input_stft: (Batch, Length, [Channel], Freq)
input_stft, _, mask = self.frontend(input_stft, feats_lens)
# 3. [Multi channel case]: Select a channel(sa_asr)
if input_stft.dim() == 4 and not self.mc:
# h: (B, T, C, F) -> h: (B, T, F)
if self.training:
if self.use_channel is not None:
input_stft = input_stft[:, :, self.use_channel, :]
else:
# Select 1ch randomly
ch = np.random.randint(input_stft.size(2))
input_stft = input_stft[:, :, ch, :]
else:
# Use the first channel
input_stft = input_stft[:, :, 0, :]
# 4. STFT -> Power spectrum
# h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F)
input_power = input_stft.real**2 + input_stft.imag**2
# 5. Feature transform e.g. Stft -> Log-Mel-Fbank
# input_power: (Batch, [Channel,] Length, Freq)
# -> input_feats: (Batch, Length, Dim)
input_feats, _ = self.logmel(input_power, feats_lens)
if self.mc:
# MFCCA
if input_feats.dim() == 4:
bt = input_feats.size(0)
channel_size = input_feats.size(2)
input_feats = (
input_feats.transpose(1, 2)
.reshape(bt * channel_size, -1, 80)
.contiguous()
)
feats_lens = feats_lens.repeat(1, channel_size).squeeze()
else:
channel_size = 1
return input_feats, feats_lens, channel_size
else:
# 6. Apply CMVN
if self.cmvn_file is not None:
if feats_lens is None:
feats_lens = input_feats.new_full(
[input_feats.size(0)], input_feats.size(1)
)
self.mean = self.mean.to(input_feats.device, input_feats.dtype)
self.std = self.std.to(input_feats.device, input_feats.dtype)
mask = make_pad_mask(feats_lens, input_feats, 1)
if input_feats.requires_grad:
input_feats = input_feats + self.mean
else:
input_feats += self.mean
if input_feats.requires_grad:
input_feats = input_feats.masked_fill(mask, 0.0)
else:
input_feats.masked_fill_(mask, 0.0)
input_feats *= self.std
return input_feats, feats_lens
def _compute_stft(
self, input: torch.Tensor, input_lengths: torch.Tensor
) -> torch.Tensor:
input_stft, feats_lens = self.stft(input, input_lengths)
assert input_stft.dim() >= 4, input_stft.shape
# "2" refers to the real/imag parts of Complex
assert input_stft.shape[-1] == 2, input_stft.shape
# Change torch.Tensor to ComplexTensor
# input_stft: (..., F, 2) -> (..., F)
input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1])
return input_stft, feats_lens
def _load_cmvn(self, cmvn_file):
with open(cmvn_file, "r", encoding="utf-8") as f:
lines = f.readlines()
means_list = []
vars_list = []
for i in range(len(lines)):
line_item = lines[i].split()
if line_item[0] == "<AddShift>":
line_item = lines[i + 1].split()
if line_item[0] == "<LearnRateCoef>":
add_shift_line = line_item[3 : (len(line_item) - 1)]
means_list = list(add_shift_line)
continue
elif line_item[0] == "<Rescale>":
line_item = lines[i + 1].split()
if line_item[0] == "<LearnRateCoef>":
rescale_line = line_item[3 : (len(line_item) - 1)]
vars_list = list(rescale_line)
continue
means = np.array(means_list).astype(np.float)
vars = np.array(vars_list).astype(np.float)
return means, vars
|