chore: import upstream snapshot with attribution

2026-07-13 12:33:43 +08:00
commit a37eb772bb
131 changed files with 42973 additions and 0 deletions
@@ -0,0 +1,84 @@
+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']  # [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=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
+
+    def build_scheduler(self, optimizer):
+        return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5)
+
+    def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx):
+        if optimizer is None:
+            return
+        optimizer.step()
+        optimizer.zero_grad()
+        if self.scheduler is not None:
+            self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])