chore: import upstream snapshot with attribution

2026-07-13 12:35:17 +08:00
commit 344816a5d8
136 changed files with 25044 additions and 0 deletions
@@ -0,0 +1,95 @@
+from __future__ import annotations
+
+import torch
+
+import modules.compat as compat
+from modules.core.ddpm import MultiVarianceDiffusion
+from utils import filter_kwargs
+from utils.hparams import hparams
+
+VARIANCE_CHECKLIST = ['energy', 'breathiness', 'voicing', 'tension']
+
+
+class ParameterAdaptorModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.variance_prediction_list = []
+        self.predict_energy = hparams.get('predict_energy', False)
+        self.predict_breathiness = hparams.get('predict_breathiness', False)
+        self.predict_voicing = hparams.get('predict_voicing', False)
+        self.predict_tension = hparams.get('predict_tension', False)
+        if self.predict_energy:
+            self.variance_prediction_list.append('energy')
+        if self.predict_breathiness:
+            self.variance_prediction_list.append('breathiness')
+        if self.predict_voicing:
+            self.variance_prediction_list.append('voicing')
+        if self.predict_tension:
+            self.variance_prediction_list.append('tension')
+        self.predict_variances = len(self.variance_prediction_list) > 0
+
+    def build_adaptor(self, cls=MultiVarianceDiffusion):
+        ranges = []
+        clamps = []
+
+        if self.predict_energy:
+            ranges.append((
+                hparams['energy_db_min'],
+                hparams['energy_db_max']
+            ))
+            clamps.append((hparams['energy_db_min'], 0.))
+
+        if self.predict_breathiness:
+            ranges.append((
+                hparams['breathiness_db_min'],
+                hparams['breathiness_db_max']
+            ))
+            clamps.append((hparams['breathiness_db_min'], 0.))
+
+        if self.predict_voicing:
+            ranges.append((
+                hparams['voicing_db_min'],
+                hparams['voicing_db_max']
+            ))
+            clamps.append((hparams['voicing_db_min'], 0.))
+
+        if self.predict_tension:
+            ranges.append((
+                hparams['tension_logit_min'],
+                hparams['tension_logit_max']
+            ))
+            clamps.append((
+                hparams['tension_logit_min'],
+                hparams['tension_logit_max']
+            ))
+
+        variances_hparams = hparams['variances_prediction_args']
+        total_repeat_bins = variances_hparams['total_repeat_bins']
+        assert total_repeat_bins % len(self.variance_prediction_list) == 0, \
+            f'Total number of repeat bins must be divisible by number of ' \
+            f'variance parameters ({len(self.variance_prediction_list)}).'
+        repeat_bins = total_repeat_bins // len(self.variance_prediction_list)
+        backbone_type = compat.get_backbone_type(hparams, nested_config=variances_hparams)
+        backbone_args = compat.get_backbone_args(variances_hparams, backbone_type=backbone_type)
+        kwargs = filter_kwargs(
+            {
+                'ranges': ranges,
+                'clamps': clamps,
+                'repeat_bins': repeat_bins,
+                'timesteps': hparams.get('timesteps'),
+                'time_scale_factor': hparams.get('time_scale_factor'),
+                'backbone_type': backbone_type,
+                'backbone_args': backbone_args
+            },
+            cls
+        )
+        return cls(**kwargs)
+
+    def collect_variance_inputs(self, **kwargs) -> list:
+        return [kwargs.get(name) for name in self.variance_prediction_list]
+
+    def collect_variance_outputs(self, variances: list | tuple) -> dict:
+        return {
+            name: pred
+            for name, pred in zip(self.variance_prediction_list, variances)
+        }