| import torch |
|
|
| import utils |
| from .diff.diffusion import GaussianDiffusion |
| from .diff.net import DiffNet |
| from tasks.tts.fs2 import FastSpeech2Task |
| from utils.hparams import hparams |
|
|
|
|
| DIFF_DECODERS = { |
| 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), |
| } |
|
|
|
|
| class DiffFsTask(FastSpeech2Task): |
| def build_tts_model(self): |
| 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'], |
| loss_type=hparams['diff_loss_type'], |
| spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], |
| ) |
|
|
| def run_model(self, model, sample, return_output=False, infer=False): |
| txt_tokens = sample['txt_tokens'] |
| target = sample['mels'] |
| mel2ph = sample['mel2ph'] |
| 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) |
|
|
| 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 _training_step(self, sample, batch_idx, _): |
| log_outputs = self.run_model(self.model, sample) |
| total_loss = sum([v for v in log_outputs.values() if isinstance(v, torch.Tensor) and v.requires_grad]) |
| log_outputs['batch_size'] = sample['txt_tokens'].size()[0] |
| log_outputs['lr'] = self.scheduler.get_lr()[0] |
| return total_loss, log_outputs |
|
|
| def validation_step(self, sample, batch_idx): |
| outputs = {} |
| 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.run_model(self.model, sample, return_output=True, infer=True) |
| self.plot_mel(batch_idx, sample['mels'], model_out['mel_out']) |
| return outputs |
|
|
