| import torch
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|
|
| import utils
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| from .diff.diffusion import GaussianDiffusion
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| from .diff.net import DiffNet
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| from tasks.tts.fs2 import FastSpeech2Task
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| from utils.hparams import hparams
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|
|
|
|
| DIFF_DECODERS = {
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| 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
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| }
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|
|
|
|
| class DiffFsTask(FastSpeech2Task):
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| def build_tts_model(self):
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| mel_bins = hparams['audio_num_mel_bins']
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| self.model = GaussianDiffusion(
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| phone_encoder=self.phone_encoder,
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| out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
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| timesteps=hparams['timesteps'],
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| loss_type=hparams['diff_loss_type'],
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| spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
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| )
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|
|
| def run_model(self, model, sample, return_output=False, infer=False):
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| txt_tokens = sample['txt_tokens']
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| target = sample['mels']
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| mel2ph = sample['mel2ph']
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| f0 = sample['f0']
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| uv = sample['uv']
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| energy = sample['energy']
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| spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
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| if hparams['pitch_type'] == 'cwt':
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| cwt_spec = sample[f'cwt_spec']
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| f0_mean = sample['f0_mean']
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| f0_std = sample['f0_std']
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| sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)
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|
|
| output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
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| ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer)
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|
|
| losses = {}
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| if 'diff_loss' in output:
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| losses['mel'] = output['diff_loss']
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| self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses)
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| if hparams['use_pitch_embed']:
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| self.add_pitch_loss(output, sample, losses)
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| if hparams['use_energy_embed']:
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| self.add_energy_loss(output['energy_pred'], energy, losses)
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| if not return_output:
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| return losses
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| else:
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| return losses, output
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|
|
| def _training_step(self, sample, batch_idx, _):
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| log_outputs = self.run_model(self.model, sample)
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| total_loss = sum([v for v in log_outputs.values() if isinstance(v, torch.Tensor) and v.requires_grad])
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| log_outputs['batch_size'] = sample['txt_tokens'].size()[0]
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| log_outputs['lr'] = self.scheduler.get_lr()[0]
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| return total_loss, log_outputs
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|
|
| def validation_step(self, sample, batch_idx):
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| outputs = {}
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| outputs['losses'] = {}
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| outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
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| outputs['total_loss'] = sum(outputs['losses'].values())
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| outputs['nsamples'] = sample['nsamples']
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| outputs = utils.tensors_to_scalars(outputs)
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| if batch_idx < hparams['num_valid_plots']:
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| _, model_out = self.run_model(self.model, sample, return_output=True, infer=True)
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| self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'])
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| return outputs
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|
|
| def build_scheduler(self, optimizer):
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| return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5)
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|
|
| def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx):
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| if optimizer is None:
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| return
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| optimizer.step()
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| optimizer.zero_grad()
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| if self.scheduler is not None:
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| self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
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|
|
