Lycoris53 commited on
Commit
b49a481
·
1 Parent(s): 5e4d148

add base model training version of finetune plachtaa based

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Files changed (1) hide show
  1. finetune_speaker_v2.py +339 -0
finetune_speaker_v2.py ADDED
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1
+ import os
2
+ import json
3
+ import argparse
4
+ import itertools
5
+ import math
6
+ import torch
7
+ from torch import nn, optim
8
+ from torch.nn import functional as F
9
+ from torch.utils.data import DataLoader
10
+ from torch.utils.tensorboard import SummaryWriter
11
+ import torch.multiprocessing as mp
12
+ import torch.distributed as dist
13
+ from torch.nn.parallel import DistributedDataParallel as DDP
14
+ from torch.cuda.amp import autocast, GradScaler
15
+ from tqdm import tqdm
16
+
17
+ import librosa
18
+ import logging
19
+
20
+ logging.getLogger('numba').setLevel(logging.WARNING)
21
+
22
+ import commons
23
+ import utils
24
+ from data_utils import (
25
+ TextAudioSpeakerLoader,
26
+ TextAudioSpeakerCollate,
27
+ DistributedBucketSampler
28
+ )
29
+ from models import (
30
+ SynthesizerTrn,
31
+ MultiPeriodDiscriminator,
32
+ )
33
+ from losses import (
34
+ generator_loss,
35
+ discriminator_loss,
36
+ feature_loss,
37
+ kl_loss
38
+ )
39
+ from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
40
+
41
+
42
+ torch.backends.cudnn.benchmark = True
43
+ global_step = 0
44
+
45
+
46
+ def main():
47
+ """Assume Single Node Multi GPUs Training Only"""
48
+ assert torch.cuda.is_available(), "CPU training is not allowed."
49
+
50
+ n_gpus = torch.cuda.device_count()
51
+ os.environ['MASTER_ADDR'] = 'localhost'
52
+ os.environ['MASTER_PORT'] = '8000'
53
+
54
+ hps = utils.get_hparams()
55
+ mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
56
+
57
+
58
+ def run(rank, n_gpus, hps):
59
+ global global_step
60
+ symbols = hps['symbols']
61
+ if rank == 0:
62
+ logger = utils.get_logger(hps.model_dir)
63
+ logger.info(hps)
64
+ utils.check_git_hash(hps.model_dir)
65
+ writer = SummaryWriter(log_dir=hps.model_dir)
66
+ writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
67
+
68
+ # Use gloo backend on Windows for Pytorch
69
+ dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
70
+ torch.manual_seed(hps.train.seed)
71
+ torch.cuda.set_device(rank)
72
+
73
+ train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data, symbols)
74
+ train_sampler = DistributedBucketSampler(
75
+ train_dataset,
76
+ hps.train.batch_size,
77
+ [32,300,400,500,600,700,800,900,1000],
78
+ num_replicas=n_gpus,
79
+ rank=rank,
80
+ shuffle=True)
81
+ collate_fn = TextAudioSpeakerCollate()
82
+ train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True,
83
+ collate_fn=collate_fn, batch_sampler=train_sampler)
84
+ # train_loader = DataLoader(train_dataset, batch_size=hps.train.batch_size, num_workers=2, shuffle=False, pin_memory=True,
85
+ # collate_fn=collate_fn)
86
+ if rank == 0:
87
+ eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, symbols)
88
+ eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
89
+ batch_size=hps.train.batch_size, pin_memory=True,
90
+ drop_last=False, collate_fn=collate_fn)
91
+
92
+ net_g = SynthesizerTrn(
93
+ len(symbols),
94
+ hps.data.filter_length // 2 + 1,
95
+ hps.train.segment_size // hps.data.hop_length,
96
+ n_speakers=hps.data.n_speakers,
97
+ **hps.model).cuda(rank)
98
+ net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
99
+
100
+ # load existing model
101
+ if hps.cont:
102
+ try:
103
+ _, _, _, epoch_str = utils.load_checkpoint("./OUTPUT_MODEL/G_latest.pth", net_g, None)
104
+ _, _, _, epoch_str = utils.load_checkpoint("./OUTPUT_MODEL/D_latest.pth", net_d, None)
105
+ global_step = epoch_str * hps.train.batch_size
106
+ except:
107
+ raise Exception("Failed to find latest checkpoint on cont=True flag")
108
+ # _, _, _, epoch_str = utils.load_checkpoint("./pretrained_models/G_0.pth", net_g, None)
109
+ # _, _, _, epoch_str = utils.load_checkpoint("./pretrained_models/D_0.pth", net_d, None)
110
+ # epoch_str = 1
111
+ # global_step = 0
112
+ else:
113
+ print("*** Start trainging from scratch ***")
114
+ #_, _, _, epoch_str = utils.load_checkpoint("./pretrained_models/G_0.pth", net_g, None)
115
+ #_, _, _, epoch_str = utils.load_checkpoint("./pretrained_models/D_0.pth", net_d, None)
116
+ epoch_str = 1
117
+ global_step = 0
118
+
119
+
120
+ # freeze all other layers except speaker embedding
121
+ #for p in net_g.parameters():
122
+ # p.requires_grad = True
123
+ #for p in net_d.parameters():
124
+ # p.requires_grad = True
125
+
126
+ # for p in net_d.parameters():
127
+ # p.requires_grad = False
128
+ # net_g.emb_g.weight.requires_grad = True
129
+
130
+ optim_g = torch.optim.AdamW(
131
+ net_g.parameters(),
132
+ hps.train.learning_rate,
133
+ betas=hps.train.betas,
134
+ eps=hps.train.eps)
135
+ optim_d = torch.optim.AdamW(
136
+ net_d.parameters(),
137
+ hps.train.learning_rate,
138
+ betas=hps.train.betas,
139
+ eps=hps.train.eps)
140
+ net_g = DDP(net_g, device_ids=[rank])
141
+ net_d = DDP(net_d, device_ids=[rank])
142
+
143
+ scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay)
144
+ scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay)
145
+
146
+ scaler = GradScaler(enabled=hps.train.fp16_run)
147
+
148
+ for epoch in range(epoch_str, hps.train.epochs + 1):
149
+ if rank==0:
150
+ train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
151
+ else:
152
+ train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
153
+ scheduler_g.step()
154
+ scheduler_d.step()
155
+
156
+
157
+ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
158
+ net_g, net_d = nets
159
+ optim_g, optim_d = optims
160
+ scheduler_g, scheduler_d = schedulers
161
+ train_loader, eval_loader = loaders
162
+ if writers is not None:
163
+ writer, writer_eval = writers
164
+
165
+ # train_loader.batch_sampler.set_epoch(epoch)
166
+ global global_step
167
+
168
+ net_g.train()
169
+ net_d.train()
170
+ for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(tqdm(train_loader)):
171
+ x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
172
+ spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
173
+ y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
174
+ speakers = speakers.cuda(rank, non_blocking=True)
175
+
176
+ with autocast(enabled=hps.train.fp16_run):
177
+ y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
178
+ (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers)
179
+
180
+ mel = spec_to_mel_torch(
181
+ spec,
182
+ hps.data.filter_length,
183
+ hps.data.n_mel_channels,
184
+ hps.data.sampling_rate,
185
+ hps.data.mel_fmin,
186
+ hps.data.mel_fmax)
187
+ y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
188
+ y_hat_mel = mel_spectrogram_torch(
189
+ y_hat.squeeze(1),
190
+ hps.data.filter_length,
191
+ hps.data.n_mel_channels,
192
+ hps.data.sampling_rate,
193
+ hps.data.hop_length,
194
+ hps.data.win_length,
195
+ hps.data.mel_fmin,
196
+ hps.data.mel_fmax
197
+ )
198
+
199
+ y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
200
+
201
+ # Discriminator
202
+ y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
203
+ with autocast(enabled=False):
204
+ loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
205
+ loss_disc_all = loss_disc
206
+ optim_d.zero_grad()
207
+ scaler.scale(loss_disc_all).backward()
208
+ scaler.unscale_(optim_d)
209
+ grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
210
+ scaler.step(optim_d)
211
+
212
+ with autocast(enabled=hps.train.fp16_run):
213
+ # Generator
214
+ y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
215
+ with autocast(enabled=False):
216
+ loss_dur = torch.sum(l_length.float())
217
+ loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
218
+ loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
219
+
220
+ loss_fm = feature_loss(fmap_r, fmap_g)
221
+ loss_gen, losses_gen = generator_loss(y_d_hat_g)
222
+ loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
223
+ optim_g.zero_grad()
224
+ scaler.scale(loss_gen_all).backward()
225
+ scaler.unscale_(optim_g)
226
+ grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
227
+ scaler.step(optim_g)
228
+ scaler.update()
229
+
230
+ if rank==0:
231
+ if global_step % hps.train.log_interval == 0:
232
+ lr = optim_g.param_groups[0]['lr']
233
+ losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
234
+ logger.info('Train Epoch: {} [{:.0f}%]'.format(
235
+ epoch,
236
+ 100. * batch_idx / len(train_loader)))
237
+ logger.info([x.item() for x in losses] + [global_step, lr])
238
+
239
+ scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_g": grad_norm_g}
240
+ scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
241
+
242
+ scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
243
+ scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
244
+ scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
245
+ image_dict = {
246
+ "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
247
+ "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
248
+ "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
249
+ "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
250
+ }
251
+ utils.summarize(
252
+ writer=writer,
253
+ global_step=global_step,
254
+ images=image_dict,
255
+ scalars=scalar_dict)
256
+
257
+ if global_step % hps.train.eval_interval == 0:
258
+ evaluate(hps, net_g, eval_loader, writer_eval)
259
+ utils.save_checkpoint(net_g, None, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
260
+ utils.save_checkpoint(net_g, None, hps.train.learning_rate, epoch,
261
+ os.path.join(hps.model_dir, "G_latest.pth".format(global_step)))
262
+ utils.save_checkpoint(net_d, None, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
263
+ utils.save_checkpoint(net_d, None, hps.train.learning_rate, epoch,
264
+ os.path.join(hps.model_dir, "D_latest.pth".format(global_step)))
265
+ #old_g=os.path.join(hps.model_dir, "G_{}.pth".format(global_step-4000))
266
+ #old_d=os.path.join(hps.model_dir, "D_{}.pth".format(global_step-4000))
267
+ #if os.path.exists(old_g):
268
+ # os.remove(old_g)
269
+ #if os.path.exists(old_d):
270
+ # os.remove(old_d)
271
+ global_step += 1
272
+ if epoch > hps.max_epochs:
273
+ print("Maximum epoch reached, closing training...")
274
+ exit()
275
+
276
+ if rank == 0:
277
+ logger.info('====> Epoch: {}'.format(epoch))
278
+
279
+
280
+ def evaluate(hps, generator, eval_loader, writer_eval):
281
+ generator.eval()
282
+ with torch.no_grad():
283
+ for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(eval_loader):
284
+ x, x_lengths = x.cuda(0), x_lengths.cuda(0)
285
+ spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
286
+ y, y_lengths = y.cuda(0), y_lengths.cuda(0)
287
+ speakers = speakers.cuda(0)
288
+
289
+ # remove else
290
+ x = x[:1]
291
+ x_lengths = x_lengths[:1]
292
+ spec = spec[:1]
293
+ spec_lengths = spec_lengths[:1]
294
+ y = y[:1]
295
+ y_lengths = y_lengths[:1]
296
+ speakers = speakers[:1]
297
+ break
298
+ y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, max_len=1000)
299
+ y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
300
+
301
+ mel = spec_to_mel_torch(
302
+ spec,
303
+ hps.data.filter_length,
304
+ hps.data.n_mel_channels,
305
+ hps.data.sampling_rate,
306
+ hps.data.mel_fmin,
307
+ hps.data.mel_fmax)
308
+ y_hat_mel = mel_spectrogram_torch(
309
+ y_hat.squeeze(1).float(),
310
+ hps.data.filter_length,
311
+ hps.data.n_mel_channels,
312
+ hps.data.sampling_rate,
313
+ hps.data.hop_length,
314
+ hps.data.win_length,
315
+ hps.data.mel_fmin,
316
+ hps.data.mel_fmax
317
+ )
318
+ image_dict = {
319
+ "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
320
+ }
321
+ audio_dict = {
322
+ "gen/audio": y_hat[0,:,:y_hat_lengths[0]]
323
+ }
324
+ if global_step == 0:
325
+ image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
326
+ audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
327
+
328
+ utils.summarize(
329
+ writer=writer_eval,
330
+ global_step=global_step,
331
+ images=image_dict,
332
+ audios=audio_dict,
333
+ audio_sampling_rate=hps.data.sampling_rate
334
+ )
335
+ generator.train()
336
+
337
+
338
+ if __name__ == "__main__":
339
+ main()