forked from NVIDIA/radtts
-
Notifications
You must be signed in to change notification settings - Fork 0
/
data.py
534 lines (458 loc) · 21.4 KB
/
data.py
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
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
# Based on https://github.com/NVIDIA/flowtron/blob/master/data.py
# Original license text:
###############################################################################
#
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
###############################################################################
import os
import argparse
import json
import numpy as np
import lmdb
import pickle as pkl
import torch
import torch.utils.data
from scipy.io.wavfile import read
from audio_processing import TacotronSTFT
from tts_text_processing.text_processing import TextProcessing
from scipy.stats import betabinom
from librosa import pyin
from common import update_params
from scipy.ndimage import distance_transform_edt as distance_transform
def beta_binomial_prior_distribution(phoneme_count, mel_count,
scaling_factor=0.05):
P = phoneme_count
M = mel_count
x = np.arange(0, P)
mel_text_probs = []
for i in range(1, M+1):
a, b = scaling_factor*i, scaling_factor*(M+1-i)
rv = betabinom(P - 1, a, b)
mel_i_prob = rv.pmf(x)
mel_text_probs.append(mel_i_prob)
return torch.tensor(np.array(mel_text_probs))
def load_wav_to_torch(full_path):
""" Loads wavdata into torch array """
sampling_rate, data = read(full_path)
return torch.from_numpy(np.array(data)).float(), sampling_rate
class Data(torch.utils.data.Dataset):
def __init__(self, datasets, filter_length, hop_length, win_length,
sampling_rate, n_mel_channels, mel_fmin, mel_fmax, f0_min,
f0_max, max_wav_value, use_f0, use_energy_avg, use_log_f0,
use_scaled_energy, symbol_set, cleaner_names, heteronyms_path,
phoneme_dict_path, p_phoneme, handle_phoneme='word',
handle_phoneme_ambiguous='ignore', speaker_ids=None,
include_speakers=None, n_frames=-1,
use_attn_prior_masking=True, prepend_space_to_text=True,
append_space_to_text=True, add_bos_eos_to_text=False,
betabinom_cache_path="", betabinom_scaling_factor=0.05,
lmdb_cache_path="", dur_min=None, dur_max=None,
combine_speaker_and_emotion=False, **kwargs):
self.combine_speaker_and_emotion = combine_speaker_and_emotion
self.max_wav_value = max_wav_value
self.audio_lmdb_dict = {} # dictionary of lmdbs for audio data
self.data = self.load_data(datasets)
self.distance_tx_unvoiced = False
if 'distance_tx_unvoiced' in kwargs.keys():
self.distance_tx_unvoiced = kwargs['distance_tx_unvoiced']
self.stft = TacotronSTFT(filter_length=filter_length,
hop_length=hop_length,
win_length=win_length,
sampling_rate=sampling_rate,
n_mel_channels=n_mel_channels,
mel_fmin=mel_fmin, mel_fmax=mel_fmax)
self.do_mel_scaling = kwargs.get('do_mel_scaling', True)
self.mel_noise_scale = kwargs.get('mel_noise_scale', 0.0)
self.filter_length = filter_length
self.hop_length = hop_length
self.win_length = win_length
self.mel_fmin = mel_fmin
self.mel_fmax = mel_fmax
self.f0_min = f0_min
self.f0_max = f0_max
self.use_f0 = use_f0
self.use_log_f0 = use_log_f0
self.use_energy_avg = use_energy_avg
self.use_scaled_energy = use_scaled_energy
self.sampling_rate = sampling_rate
self.tp = TextProcessing(
symbol_set, cleaner_names, heteronyms_path, phoneme_dict_path,
p_phoneme=p_phoneme, handle_phoneme=handle_phoneme,
handle_phoneme_ambiguous=handle_phoneme_ambiguous,
prepend_space_to_text=prepend_space_to_text,
append_space_to_text=append_space_to_text,
add_bos_eos_to_text=add_bos_eos_to_text)
self.dur_min = dur_min
self.dur_max = dur_max
if speaker_ids is None or speaker_ids == '':
self.speaker_ids = self.create_speaker_lookup_table(self.data)
else:
self.speaker_ids = speaker_ids
print("Number of files", len(self.data))
if include_speakers is not None:
for (speaker_set, include) in include_speakers:
self.filter_by_speakers_(speaker_set, include)
print("Number of files after speaker filtering", len(self.data))
if dur_min is not None and dur_max is not None:
self.filter_by_duration_(dur_min, dur_max)
print("Number of files after duration filtering", len(self.data))
self.use_attn_prior_masking = bool(use_attn_prior_masking)
self.prepend_space_to_text = bool(prepend_space_to_text)
self.append_space_to_text = bool(append_space_to_text)
self.betabinom_cache_path = betabinom_cache_path
self.betabinom_scaling_factor = betabinom_scaling_factor
self.lmdb_cache_path = lmdb_cache_path
if self.lmdb_cache_path != "":
self.cache_data_lmdb = lmdb.open(
self.lmdb_cache_path, readonly=True, max_readers=1024,
lock=False).begin()
# make sure caching path exists
if not os.path.exists(self.betabinom_cache_path):
os.makedirs(self.betabinom_cache_path)
print("Dataloader initialized with no augmentations")
self.speaker_map = None
if 'speaker_map' in kwargs:
self.speaker_map = kwargs['speaker_map']
def load_data(self, datasets, split='|'):
dataset = []
for dset_name, dset_dict in datasets.items():
folder_path = dset_dict['basedir']
audiodir = dset_dict['audiodir']
filename = dset_dict['filelist']
audio_lmdb_key = None
if 'lmdbpath' in dset_dict.keys() and len(dset_dict['lmdbpath']) > 0:
self.audio_lmdb_dict[dset_name] = lmdb.open(
dset_dict['lmdbpath'], readonly=True, max_readers=256,
lock=False).begin()
audio_lmdb_key = dset_name
wav_folder_prefix = os.path.join(folder_path, audiodir)
filelist_path = os.path.join(folder_path, filename)
with open(filelist_path, encoding='utf-8') as f:
data = [line.strip().split(split) for line in f]
for d in data:
emotion = 'other' if len(d) == 3 else d[3]
duration = -1 if len(d) == 3 else d[4]
dataset.append(
{'audiopath': os.path.join(wav_folder_prefix, d[0]),
'text': d[1],
'speaker': d[2] + '-' + emotion if self.combine_speaker_and_emotion else d[2],
'emotion': emotion,
'duration': float(duration),
'lmdb_key': audio_lmdb_key
})
return dataset
def filter_by_speakers_(self, speakers, include=True):
print("Include spaker {}: {}".format(speakers, include))
if include:
self.data = [x for x in self.data if x['speaker'] in speakers]
else:
self.data = [x for x in self.data if x['speaker'] not in speakers]
def filter_by_duration_(self, dur_min, dur_max):
self.data = [
x for x in self.data
if x['duration'] == -1 or (
x['duration'] >= dur_min and x['duration'] <= dur_max)]
def create_speaker_lookup_table(self, data):
speaker_ids = np.sort(np.unique([x['speaker'] for x in data]))
d = {speaker_ids[i]: i for i in range(len(speaker_ids))}
print("Number of speakers:", len(d))
print("Speaker IDS", d)
return d
def f0_normalize(self, x):
if self.use_log_f0:
mask = x >= self.f0_min
x[mask] = torch.log(x[mask])
x[~mask] = 0.0
return x
def f0_denormalize(self, x):
if self.use_log_f0:
log_f0_min = np.log(self.f0_min)
mask = x >= log_f0_min
x[mask] = torch.exp(x[mask])
x[~mask] = 0.0
x[x <= 0.0] = 0.0
return x
def energy_avg_normalize(self, x):
if self.use_scaled_energy:
x = (x + 20.0) / 20.0
return x
def energy_avg_denormalize(self, x):
if self.use_scaled_energy:
x = x * 20.0 - 20.0
return x
def get_f0_pvoiced(self, audio, sampling_rate=22050, frame_length=1024,
hop_length=256, f0_min=100, f0_max=300):
audio_norm = audio / self.max_wav_value
f0, voiced_mask, p_voiced = pyin(
audio_norm, f0_min, f0_max, sampling_rate,
frame_length=frame_length, win_length=frame_length // 2,
hop_length=hop_length)
f0[~voiced_mask] = 0.0
f0 = torch.FloatTensor(f0)
p_voiced = torch.FloatTensor(p_voiced)
voiced_mask = torch.FloatTensor(voiced_mask)
return f0, voiced_mask, p_voiced
def get_energy_average(self, mel):
energy_avg = mel.mean(0)
energy_avg = self.energy_avg_normalize(energy_avg)
return energy_avg
def get_mel(self, audio):
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
if self.do_mel_scaling:
melspec = (melspec + 5.5) / 2
if self.mel_noise_scale > 0:
melspec += torch.randn_like(melspec) * self.mel_noise_scale
return melspec
def get_speaker_id(self, speaker):
if self.speaker_map is not None and speaker in self.speaker_map:
speaker = self.speaker_map[speaker]
return torch.LongTensor([self.speaker_ids[speaker]])
def get_text(self, text):
text = self.tp.encode_text(text)
text = torch.LongTensor(text)
return text
def get_attention_prior(self, n_tokens, n_frames):
# cache the entire attn_prior by filename
if self.use_attn_prior_masking:
filename = "{}_{}".format(n_tokens, n_frames)
prior_path = os.path.join(self.betabinom_cache_path, filename)
prior_path += "_prior.pth"
if self.lmdb_cache_path != "":
attn_prior = pkl.loads(
self.cache_data_lmdb.get(prior_path.encode('ascii')))
elif os.path.exists(prior_path):
attn_prior = torch.load(prior_path)
else:
attn_prior = beta_binomial_prior_distribution(
n_tokens, n_frames, self.betabinom_scaling_factor)
torch.save(attn_prior, prior_path)
else:
attn_prior = torch.ones(n_frames, n_tokens) # all ones baseline
return attn_prior
def __getitem__(self, index):
data = self.data[index]
audiopath, text = data['audiopath'], data['text']
speaker_id = data['speaker']
if data['lmdb_key'] is not None:
data_dict = pkl.loads(
self.audio_lmdb_dict[data['lmdb_key']].get(
audiopath.encode('ascii')))
audio = data_dict['audio']
sampling_rate = data_dict['sampling_rate']
else:
audio, sampling_rate = load_wav_to_torch(audiopath)
if sampling_rate != self.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate))
mel = self.get_mel(audio)
f0 = None
p_voiced = None
voiced_mask = None
if self.use_f0:
filename = '_'.join(audiopath.split('/')[-3:])
f0_path = os.path.join(self.betabinom_cache_path, filename)
f0_path += "_f0_sr{}_fl{}_hl{}_f0min{}_f0max{}_log{}.pt".format(
self.sampling_rate, self.filter_length, self.hop_length,
self.f0_min, self.f0_max, self.use_log_f0)
dikt = None
if len(self.lmdb_cache_path) > 0:
dikt = pkl.loads(
self.cache_data_lmdb.get(f0_path.encode('ascii')))
f0 = dikt['f0']
p_voiced = dikt['p_voiced']
voiced_mask = dikt['voiced_mask']
elif os.path.exists(f0_path):
try:
dikt = torch.load(f0_path)
except:
print(f"f0 loading from {f0_path} is broken, recomputing.")
if dikt is not None:
f0 = dikt['f0']
p_voiced = dikt['p_voiced']
voiced_mask = dikt['voiced_mask']
else:
f0, voiced_mask, p_voiced = self.get_f0_pvoiced(
audio.cpu().numpy(), self.sampling_rate,
self.filter_length, self.hop_length, self.f0_min,
self.f0_max)
print("saving f0 to {}".format(f0_path))
torch.save({'f0': f0,
'voiced_mask': voiced_mask,
'p_voiced': p_voiced}, f0_path)
if f0 is None:
raise Exception("STOP, BROKEN F0 {}".format(audiopath))
f0 = self.f0_normalize(f0)
if self.distance_tx_unvoiced:
mask = f0 <= 0.0
distance_map = np.log(distance_transform(mask))
distance_map[distance_map <= 0] = 0.0
f0 = f0 - distance_map
energy_avg = None
if self.use_energy_avg:
energy_avg = self.get_energy_average(mel)
if self.use_scaled_energy and energy_avg.min() < 0.0:
print(audiopath, "has scaled energy avg smaller than 0")
speaker_id = self.get_speaker_id(speaker_id)
text_encoded = self.get_text(text)
attn_prior = self.get_attention_prior(
text_encoded.shape[0], mel.shape[1])
if not self.use_attn_prior_masking:
attn_prior = None
return {'mel': mel,
'speaker_id': speaker_id,
'text_encoded': text_encoded,
'audiopath': audiopath,
'attn_prior': attn_prior,
'f0': f0,
'p_voiced': p_voiced,
'voiced_mask': voiced_mask,
'energy_avg': energy_avg,
}
def __len__(self):
return len(self.data)
class DataCollate():
""" Zero-pads model inputs and targets given number of steps """
def __init__(self, n_frames_per_step=1):
self.n_frames_per_step = n_frames_per_step
def __call__(self, batch):
"""Collate from normalized data """
# Right zero-pad all one-hot text sequences to max input length
input_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([len(x['text_encoded']) for x in batch]),
dim=0, descending=True)
max_input_len = input_lengths[0]
text_padded = torch.LongTensor(len(batch), max_input_len)
text_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
text = batch[ids_sorted_decreasing[i]]['text_encoded']
text_padded[i, :text.size(0)] = text
# Right zero-pad mel-spec
num_mel_channels = batch[0]['mel'].size(0)
max_target_len = max([x['mel'].size(1) for x in batch])
# include mel padded, gate padded and speaker ids
mel_padded = torch.FloatTensor(len(batch), num_mel_channels, max_target_len)
mel_padded.zero_()
f0_padded = None
p_voiced_padded = None
voiced_mask_padded = None
energy_avg_padded = None
if batch[0]['f0'] is not None:
f0_padded = torch.FloatTensor(len(batch), max_target_len)
f0_padded.zero_()
if batch[0]['p_voiced'] is not None:
p_voiced_padded = torch.FloatTensor(len(batch), max_target_len)
p_voiced_padded.zero_()
if batch[0]['voiced_mask'] is not None:
voiced_mask_padded = torch.FloatTensor(len(batch), max_target_len)
voiced_mask_padded.zero_()
if batch[0]['energy_avg'] is not None:
energy_avg_padded = torch.FloatTensor(len(batch), max_target_len)
energy_avg_padded.zero_()
attn_prior_padded = torch.FloatTensor(len(batch), max_target_len, max_input_len)
attn_prior_padded.zero_()
output_lengths = torch.LongTensor(len(batch))
speaker_ids = torch.LongTensor(len(batch))
audiopaths = []
for i in range(len(ids_sorted_decreasing)):
mel = batch[ids_sorted_decreasing[i]]['mel']
mel_padded[i, :, :mel.size(1)] = mel
if batch[ids_sorted_decreasing[i]]['f0'] is not None:
f0 = batch[ids_sorted_decreasing[i]]['f0']
f0_padded[i, :len(f0)] = f0
if batch[ids_sorted_decreasing[i]]['voiced_mask'] is not None:
voiced_mask = batch[ids_sorted_decreasing[i]]['voiced_mask']
voiced_mask_padded[i, :len(f0)] = voiced_mask
if batch[ids_sorted_decreasing[i]]['p_voiced'] is not None:
p_voiced = batch[ids_sorted_decreasing[i]]['p_voiced']
p_voiced_padded[i, :len(f0)] = p_voiced
if batch[ids_sorted_decreasing[i]]['energy_avg'] is not None:
energy_avg = batch[ids_sorted_decreasing[i]]['energy_avg']
energy_avg_padded[i, :len(energy_avg)] = energy_avg
output_lengths[i] = mel.size(1)
speaker_ids[i] = batch[ids_sorted_decreasing[i]]['speaker_id']
audiopath = batch[ids_sorted_decreasing[i]]['audiopath']
audiopaths.append(audiopath)
cur_attn_prior = batch[ids_sorted_decreasing[i]]['attn_prior']
if cur_attn_prior is None:
attn_prior_padded = None
else:
attn_prior_padded[i, :cur_attn_prior.size(0), :cur_attn_prior.size(1)] = cur_attn_prior
return {'mel': mel_padded,
'speaker_ids': speaker_ids,
'text': text_padded,
'input_lengths': input_lengths,
'output_lengths': output_lengths,
'audiopaths': audiopaths,
'attn_prior': attn_prior_padded,
'f0': f0_padded,
'p_voiced': p_voiced_padded,
'voiced_mask': voiced_mask_padded,
'energy_avg': energy_avg_padded
}
# ===================================================================
# Takes directory of clean audio and makes directory of spectrograms
# Useful for making test sets
# ===================================================================
if __name__ == "__main__":
# Get defaults so it can work with no Sacred
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str,
help='JSON file for configuration')
parser.add_argument('-p', '--params', nargs='+', default=[])
args = parser.parse_args()
args.rank = 0
# Parse configs. Globals nicer in this case
with open(args.config) as f:
data = f.read()
config = json.loads(data)
update_params(config, args.params)
print(config)
data_config = config["data_config"]
ignore_keys = ['training_files', 'validation_files']
trainset = Data(data_config['training_files'],
**dict((k, v) for k, v in data_config.items()
if k not in ignore_keys))
valset = Data(data_config['validation_files'],
**dict((k, v) for k, v in data_config.items()
if k not in ignore_keys), speaker_ids=trainset.speaker_ids)
collate_fn = DataCollate()
for dataset in (trainset, valset):
for i, batch in enumerate(dataset):
out = batch
print("{}/{}".format(i, len(dataset)))