add support for compose transform

pyannote · Jan 9, 2025 · 2a59bc7 · 2a59bc7
1 parent da3356b
commit 2a59bc7
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Showing 2 changed files with 38 additions and 19 deletions.
diff --git a/pyannote/audio/core/model.py b/pyannote/audio/core/model.py
@@ -40,6 +40,7 @@
 from pyannote.core import SlidingWindow
 from pytorch_lightning.utilities.model_summary.model_summary import ModelSummary
 from torch.utils.data import DataLoader
+from torch_audiomentations.core.composition import Compose
 
 from pyannote.audio import __version__
 from pyannote.audio.core.io import Audio
@@ -679,16 +680,27 @@ def default_map_location(storage, loc):
 
             TaskClass = getattr(task_module, task_class_name)
 
-            # instanciate task augmentation
-            augmentation = loaded_checkpoint["pyannote.audio"]["task"]["augmentation"]
-            if augmentation:
-                augmentation_module = import_module(augmentation["module"])
-                augmentation_class = augmentation["class"]
-                augmentation_kwargs = augmentation["kwargs"]
-                AugmentationClass = getattr(augmentation_module, augmentation_class)
-                augmentation = AugmentationClass(**augmentation_kwargs)
-
-            task_hparams["augmentation"] = augmentation
+            # instantiate task augmentation
+            def instantiate_transform(transform_data):
+                transform_module = import_module(transform_data["module"])
+                transform_class = transform_data["class"]
+                transform_kwargs = transform_data["kwargs"]
+                TransformClass = getattr(transform_module, transform_class)
+                return TransformClass(**transform_kwargs)
+
+            augmentation_data = loaded_checkpoint["pyannote.audio"]["task"]["augmentation"]
+            # BaseWaveformTransform case
+            if isinstance(augmentation_data, Dict):
+                task_hparams["augmentation"] = instantiate_transform(augmentation_data)
+
+            # Compose transform case
+            elif isinstance(augmentation_data , List):
+                transforms = []
+                for transform_data in augmentation_data:
+                    transform = instantiate_transform(transform_data)
+                    transforms.append(transform)
+
+                task_hparams["augmentation"] = Compose(transforms=transforms, output_type="dict")
 
             # instanciate task metrics
             metrics = loaded_checkpoint["pyannote.audio"]["task"]["metrics"]

diff --git a/pyannote/audio/core/task.py b/pyannote/audio/core/task.py
@@ -46,6 +46,7 @@
 from torch.utils.data import DataLoader, Dataset, IterableDataset
 from torch_audiomentations import Identity
 from torch_audiomentations.core.transforms_interface import BaseWaveformTransform
+from torch_audiomentations.core.composition import BaseCompose
 from torchmetrics import Metric, MetricCollection
 
 from pyannote.audio.utils.loss import binary_cross_entropy, nll_loss
@@ -915,18 +916,24 @@ def on_save_checkpoint(self, checkpoint):
         }
 
         # save augmentation:
-        # TODO: add support for compose augmentation
+        def serialize_augmentation(augmentation) -> Dict:
+            return {
+                "module": augmentation.__class__.__module__,
+                "class": augmentation.__class__.__name__,
+                "kwargs": {
+                    param: getattr(augmentation, param, None)
+                    for param in inspect.signature(augmentation.__init__).parameters
+                }
+            }
+
         if not self.augmentation:
             checkpoint["pyannote.audio"]["task"]["augmentation"] = None
         elif isinstance(self.augmentation, BaseWaveformTransform):
-            checkpoint["pyannote.audio"]["task"]["augmentation"] = [{
-                "module": self.augmentation.__class__.__module__,
-                "class": self.augmentation.__class__.__name__,
-                "kwargs": {
-                    param: getattr(self.augmentation, param, None)
-                    for param in inspect.signature(self.augmentation.__init__).parameters
-                }
-            }]
+            checkpoint["pyannote.audio"]["task"]["augmentation"] = serialize_augmentation(self.augmentation)
+        elif isinstance(self.augmentation, BaseCompose):
+            checkpoint["pyannote.audio"]["task"]["augmentation"] = []
+            for augmentation in self.augmentation.transforms:
+                checkpoint["pyannote.audio"]["task"]["augmentation"].append(serialize_augmentation(augmentation))
 
         # save metrics:
         if isinstance(self.metric, Metric):