File size: 22,732 Bytes
b93970c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import torch

import utils
from utils.hparams import hparams
from .diff.net import DiffNet
from .diff.shallow_diffusion_tts import GaussianDiffusion, OfflineGaussianDiffusion
from .diffspeech_task import DiffSpeechTask
from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder
from modules.fastspeech.pe import PitchExtractor
from modules.fastspeech.fs2 import FastSpeech2
from modules.diffsinger_midi.fs2 import FastSpeech2MIDI
from modules.fastspeech.tts_modules import mel2ph_to_dur

from usr.diff.candidate_decoder import FFT
from utils.pitch_utils import denorm_f0
from tasks.tts.fs2_utils import FastSpeechDataset
from tasks.tts.fs2 import FastSpeech2Task

import numpy as np
import os
import torch.nn.functional as F

DIFF_DECODERS = {
    'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
    'fft': lambda hp: FFT(
        hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
}


class DiffSingerTask(DiffSpeechTask):
    def __init__(self):
        super(DiffSingerTask, self).__init__()
        self.dataset_cls = FastSpeechDataset
        self.vocoder: BaseVocoder = get_vocoder_cls(hparams)()
        if hparams.get('pe_enable') is not None and hparams['pe_enable']:
            self.pe = PitchExtractor().cuda()
            utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True)
            self.pe.eval()

    def build_tts_model(self):
        # import torch
        # from tqdm import tqdm
        # v_min = torch.ones([80]) * 100
        # v_max = torch.ones([80]) * -100
        # for i, ds in enumerate(tqdm(self.dataset_cls('train'))):
        #     v_max = torch.max(torch.max(ds['mel'].reshape(-1, 80), 0)[0], v_max)
        #     v_min = torch.min(torch.min(ds['mel'].reshape(-1, 80), 0)[0], v_min)
        #     if i % 100 == 0:
        #         print(i, v_min, v_max)
        # print('final', v_min, v_max)
        mel_bins = hparams['audio_num_mel_bins']
        self.model = GaussianDiffusion(
            phone_encoder=self.phone_encoder,
            out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
            timesteps=hparams['timesteps'],
            K_step=hparams['K_step'],
            loss_type=hparams['diff_loss_type'],
            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
        )
        if hparams['fs2_ckpt'] != '':
            utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True)
            # self.model.fs2.decoder = None
            for k, v in self.model.fs2.named_parameters():
                v.requires_grad = False

    def validation_step(self, sample, batch_idx):
        outputs = {}
        txt_tokens = sample['txt_tokens']  # [B, T_t]

        target = sample['mels']  # [B, T_s, 80]
        energy = sample['energy']
        # fs2_mel = sample['fs2_mels']
        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
        mel2ph = sample['mel2ph']
        f0 = sample['f0']
        uv = sample['uv']

        outputs['losses'] = {}

        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)


        outputs['total_loss'] = sum(outputs['losses'].values())
        outputs['nsamples'] = sample['nsamples']
        outputs = utils.tensors_to_scalars(outputs)
        if batch_idx < hparams['num_valid_plots']:
            model_out = self.model(
                txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True)

            if hparams.get('pe_enable') is not None and hparams['pe_enable']:
                gt_f0 = self.pe(sample['mels'])['f0_denorm_pred']  # pe predict from GT mel
                pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred']  # pe predict from Pred mel
            else:
                gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
                pred_f0 = model_out.get('f0_denorm')
            self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
            self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}')
            self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}')
        return outputs


class ShallowDiffusionOfflineDataset(FastSpeechDataset):
    def __getitem__(self, index):
        sample = super(ShallowDiffusionOfflineDataset, self).__getitem__(index)
        item = self._get_item(index)

        if self.prefix != 'train' and hparams['fs2_ckpt'] != '':
            fs2_ckpt = os.path.dirname(hparams['fs2_ckpt'])
            item_name = item['item_name']
            fs2_mel = torch.Tensor(np.load(f'{fs2_ckpt}/P_mels_npy/{item_name}.npy'))  # ~M generated by FFT-singer.
            sample['fs2_mel'] = fs2_mel
        return sample

    def collater(self, samples):
        batch = super(ShallowDiffusionOfflineDataset, self).collater(samples)
        if self.prefix != 'train' and hparams['fs2_ckpt'] != '':
            batch['fs2_mels'] = utils.collate_2d([s['fs2_mel'] for s in samples], 0.0)
        return batch


class DiffSingerOfflineTask(DiffSingerTask):
    def __init__(self):
        super(DiffSingerOfflineTask, self).__init__()
        self.dataset_cls = ShallowDiffusionOfflineDataset

    def build_tts_model(self):
        mel_bins = hparams['audio_num_mel_bins']
        self.model = OfflineGaussianDiffusion(
            phone_encoder=self.phone_encoder,
            out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
            timesteps=hparams['timesteps'],
            K_step=hparams['K_step'],
            loss_type=hparams['diff_loss_type'],
            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
        )
        # if hparams['fs2_ckpt'] != '':
        #     utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True)
        #     self.model.fs2.decoder = None

    def run_model(self, model, sample, return_output=False, infer=False):
        txt_tokens = sample['txt_tokens']  # [B, T_t]
        target = sample['mels']  # [B, T_s, 80]
        mel2ph = sample['mel2ph']  # [B, T_s]
        f0 = sample['f0']
        uv = sample['uv']
        energy = sample['energy']
        fs2_mel = None #sample['fs2_mels']
        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
        if hparams['pitch_type'] == 'cwt':
            cwt_spec = sample[f'cwt_spec']
            f0_mean = sample['f0_mean']
            f0_std = sample['f0_std']
            sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)

        output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
                       ref_mels=[target, fs2_mel], f0=f0, uv=uv, energy=energy, infer=infer)

        losses = {}
        if 'diff_loss' in output:
            losses['mel'] = output['diff_loss']
        # self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses)
        # if hparams['use_pitch_embed']:
        #     self.add_pitch_loss(output, sample, losses)
        if hparams['use_energy_embed']:
            self.add_energy_loss(output['energy_pred'], energy, losses)

        if not return_output:
            return losses
        else:
            return losses, output

    def validation_step(self, sample, batch_idx):
        outputs = {}
        txt_tokens = sample['txt_tokens']  # [B, T_t]

        target = sample['mels']  # [B, T_s, 80]
        energy = sample['energy']
        # fs2_mel = sample['fs2_mels']
        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
        mel2ph = sample['mel2ph']
        f0 = sample['f0']
        uv = sample['uv']

        outputs['losses'] = {}

        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)


        outputs['total_loss'] = sum(outputs['losses'].values())
        outputs['nsamples'] = sample['nsamples']
        outputs = utils.tensors_to_scalars(outputs)
        if batch_idx < hparams['num_valid_plots']:
            fs2_mel = sample['fs2_mels']
            model_out = self.model(
                txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy,
                ref_mels=[None, fs2_mel], infer=True)
            if hparams.get('pe_enable') is not None and hparams['pe_enable']:
                gt_f0 = self.pe(sample['mels'])['f0_denorm_pred']  # pe predict from GT mel
                pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred']  # pe predict from Pred mel
            else:
                gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
                pred_f0 = model_out.get('f0_denorm')
            self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
            self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}')
            self.plot_mel(batch_idx, sample['mels'], fs2_mel, name=f'fs2mel_{batch_idx}')
        return outputs

    def test_step(self, sample, batch_idx):
        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
        txt_tokens = sample['txt_tokens']
        energy = sample['energy']
        if hparams['profile_infer']:
            pass
        else:
            mel2ph, uv, f0 = None, None, None
            if hparams['use_gt_dur']:
                mel2ph = sample['mel2ph']
            if hparams['use_gt_f0']:
                f0 = sample['f0']
                uv = sample['uv']
            fs2_mel = sample['fs2_mels']
            outputs = self.model(
                txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=[None, fs2_mel], energy=energy,
                infer=True)
            sample['outputs'] = self.model.out2mel(outputs['mel_out'])
            sample['mel2ph_pred'] = outputs['mel2ph']

            if hparams.get('pe_enable') is not None and hparams['pe_enable']:
                sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred']  # pe predict from GT mel
                sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred']  # pe predict from Pred mel
            else:
                sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams)
                sample['f0_pred'] = outputs.get('f0_denorm')
            return self.after_infer(sample)


class MIDIDataset(FastSpeechDataset):
    def __getitem__(self, index):
        sample = super(MIDIDataset, self).__getitem__(index)
        item = self._get_item(index)
        sample['f0_midi'] = torch.FloatTensor(item['f0_midi'])
        sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']]

        return sample

    def collater(self, samples):
        batch = super(MIDIDataset, self).collater(samples)
        batch['f0_midi'] = utils.collate_1d([s['f0_midi'] for s in samples], 0.0)
        batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0)
        # print((batch['pitch_midi'] == f0_to_coarse(batch['f0_midi'])).all())
        return batch


class OpencpopDataset(FastSpeechDataset):
    def __getitem__(self, index):
        sample = super(OpencpopDataset, self).__getitem__(index)
        item = self._get_item(index)
        sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']]
        sample['midi_dur'] = torch.FloatTensor(item['midi_dur'])[:hparams['max_frames']]
        sample['is_slur'] = torch.LongTensor(item['is_slur'])[:hparams['max_frames']]
        sample['word_boundary'] = torch.LongTensor(item['word_boundary'])[:hparams['max_frames']]
        return sample

    def collater(self, samples):
        batch = super(OpencpopDataset, self).collater(samples)
        batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0)
        batch['midi_dur'] = utils.collate_1d([s['midi_dur'] for s in samples], 0)
        batch['is_slur'] = utils.collate_1d([s['is_slur'] for s in samples], 0)
        batch['word_boundary'] = utils.collate_1d([s['word_boundary'] for s in samples], 0)
        return batch


class DiffSingerMIDITask(DiffSingerTask):
    def __init__(self):
        super(DiffSingerMIDITask, self).__init__()
        # self.dataset_cls = MIDIDataset
        self.dataset_cls = OpencpopDataset

    def run_model(self, model, sample, return_output=False, infer=False):
        txt_tokens = sample['txt_tokens']  # [B, T_t]
        target = sample['mels']  # [B, T_s, 80]
        # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s]
        mel2ph = sample['mel2ph']
        if hparams.get('switch_midi2f0_step') is not None and self.global_step > hparams['switch_midi2f0_step']:
            f0 = None
            uv = None
        else:
            f0 = sample['f0']
            uv = sample['uv']
        energy = sample['energy']

        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
        if hparams['pitch_type'] == 'cwt':
            cwt_spec = sample[f'cwt_spec']
            f0_mean = sample['f0_mean']
            f0_std = sample['f0_std']
            sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)

        output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
                       ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer, pitch_midi=sample['pitch_midi'],
                       midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur'))

        losses = {}
        if 'diff_loss' in output:
            losses['mel'] = output['diff_loss']
        self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses)
        if hparams['use_pitch_embed']:
            self.add_pitch_loss(output, sample, losses)
        if hparams['use_energy_embed']:
            self.add_energy_loss(output['energy_pred'], energy, losses)
        if not return_output:
            return losses
        else:
            return losses, output

    def validation_step(self, sample, batch_idx):
        outputs = {}
        txt_tokens = sample['txt_tokens']  # [B, T_t]

        target = sample['mels']  # [B, T_s, 80]
        energy = sample['energy']
        # fs2_mel = sample['fs2_mels']
        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
        mel2ph = sample['mel2ph']

        outputs['losses'] = {}

        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)

        outputs['total_loss'] = sum(outputs['losses'].values())
        outputs['nsamples'] = sample['nsamples']
        outputs = utils.tensors_to_scalars(outputs)
        if batch_idx < hparams['num_valid_plots']:
            model_out = self.model(
                txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=None, uv=None, energy=energy, ref_mels=None, infer=True,
                pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur'))

            if hparams.get('pe_enable') is not None and hparams['pe_enable']:
                gt_f0 = self.pe(sample['mels'])['f0_denorm_pred']  # pe predict from GT mel
                pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred']  # pe predict from Pred mel
            else:
                gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
                pred_f0 = model_out.get('f0_denorm')
            self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
            self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}')
            self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}')
            if hparams['use_pitch_embed']:
                self.plot_pitch(batch_idx, sample, model_out)
        return outputs

    def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None):
        """
        :param dur_pred: [B, T], float, log scale
        :param mel2ph: [B, T]
        :param txt_tokens: [B, T]
        :param losses:
        :return:
        """
        B, T = txt_tokens.shape
        nonpadding = (txt_tokens != 0).float()
        dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding
        is_sil = torch.zeros_like(txt_tokens).bool()
        for p in self.sil_ph:
            is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0])
        is_sil = is_sil.float()  # [B, T_txt]

        # phone duration loss
        if hparams['dur_loss'] == 'mse':
            losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none')
            losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum()
            dur_pred = (dur_pred.exp() - 1).clamp(min=0)
        else:
            raise NotImplementedError

        # use linear scale for sent and word duration
        if hparams['lambda_word_dur'] > 0:
            idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1]
            # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur)  # midi_dur can be implied by add gt-ph_dur
            word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred)
            word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt)
            wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none')
            word_nonpadding = (word_dur_g > 0).float()
            wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum()
            losses['wdur'] = wdur_loss * hparams['lambda_word_dur']
        if hparams['lambda_sent_dur'] > 0:
            sent_dur_p = dur_pred.sum(-1)
            sent_dur_g = dur_gt.sum(-1)
            sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean')
            losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur']


class AuxDecoderMIDITask(FastSpeech2Task):
    def __init__(self):
        super().__init__()
        # self.dataset_cls = MIDIDataset
        self.dataset_cls = OpencpopDataset

    def build_tts_model(self):
        if hparams.get('use_midi') is not None and hparams['use_midi']:
            self.model = FastSpeech2MIDI(self.phone_encoder)
        else:
            self.model = FastSpeech2(self.phone_encoder)

    def run_model(self, model, sample, return_output=False):
        txt_tokens = sample['txt_tokens']  # [B, T_t]
        target = sample['mels']  # [B, T_s, 80]
        mel2ph = sample['mel2ph']  # [B, T_s]
        f0 = sample['f0']
        uv = sample['uv']
        energy = sample['energy']

        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
        if hparams['pitch_type'] == 'cwt':
            cwt_spec = sample[f'cwt_spec']
            f0_mean = sample['f0_mean']
            f0_std = sample['f0_std']
            sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)

        output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
                       ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False, pitch_midi=sample['pitch_midi'],
                       midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur'))

        losses = {}
        self.add_mel_loss(output['mel_out'], target, losses)
        self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses)
        if hparams['use_pitch_embed']:
            self.add_pitch_loss(output, sample, losses)
        if hparams['use_energy_embed']:
            self.add_energy_loss(output['energy_pred'], energy, losses)
        if not return_output:
            return losses
        else:
            return losses, output

    def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None):
        """
        :param dur_pred: [B, T], float, log scale
        :param mel2ph: [B, T]
        :param txt_tokens: [B, T]
        :param losses:
        :return:
        """
        B, T = txt_tokens.shape
        nonpadding = (txt_tokens != 0).float()
        dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding
        is_sil = torch.zeros_like(txt_tokens).bool()
        for p in self.sil_ph:
            is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0])
        is_sil = is_sil.float()  # [B, T_txt]

        # phone duration loss
        if hparams['dur_loss'] == 'mse':
            losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none')
            losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum()
            dur_pred = (dur_pred.exp() - 1).clamp(min=0)
        else:
            raise NotImplementedError

        # use linear scale for sent and word duration
        if hparams['lambda_word_dur'] > 0:
            idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1]
            # word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur)  # midi_dur can be implied by add gt-ph_dur
            word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred)
            word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt)
            wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none')
            word_nonpadding = (word_dur_g > 0).float()
            wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum()
            losses['wdur'] = wdur_loss * hparams['lambda_word_dur']
        if hparams['lambda_sent_dur'] > 0:
            sent_dur_p = dur_pred.sum(-1)
            sent_dur_g = dur_gt.sum(-1)
            sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean')
            losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur']

    def validation_step(self, sample, batch_idx):
        outputs = {}
        outputs['losses'] = {}
        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True)
        outputs['total_loss'] = sum(outputs['losses'].values())
        outputs['nsamples'] = sample['nsamples']
        mel_out = self.model.out2mel(model_out['mel_out'])
        outputs = utils.tensors_to_scalars(outputs)
        # if sample['mels'].shape[0] == 1:
        #     self.add_laplace_var(mel_out, sample['mels'], outputs)
        if batch_idx < hparams['num_valid_plots']:
            self.plot_mel(batch_idx, sample['mels'], mel_out)
            self.plot_dur(batch_idx, sample, model_out)
            if hparams['use_pitch_embed']:
                self.plot_pitch(batch_idx, sample, model_out)
        return outputs