refactor: move shared function into dataset.py

TTS/tts/datasets/dataset.py

@@ -63,6 +63,31 @@ def get_audio_size(audiopath: Union[str, os.PathLike[Any]]) -> int:
         raise RuntimeError(msg) from e
 
 
+def get_attribute_balancer_weights(items: list, attr_name: str, multi_dict: Optional[dict] = None):
+    """Create inverse frequency weights for balancing the dataset.
+
+    Use `multi_dict` to scale relative weights."""
+    attr_names_samples = np.array([item[attr_name] for item in items])
+    unique_attr_names = np.unique(attr_names_samples).tolist()
+    attr_idx = [unique_attr_names.index(l) for l in attr_names_samples]
+    attr_count = np.array([len(np.where(attr_names_samples == l)[0]) for l in unique_attr_names])
+    weight_attr = 1.0 / attr_count
+    dataset_samples_weight = np.array([weight_attr[l] for l in attr_idx])
+    dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight)
+    if multi_dict is not None:
+        # check if all keys are in the multi_dict
+        for k in multi_dict:
+            assert k in unique_attr_names, f"{k} not in {unique_attr_names}"
+        # scale weights
+        multiplier_samples = np.array([multi_dict.get(item[attr_name], 1.0) for item in items])
+        dataset_samples_weight *= multiplier_samples
+    return (
+        torch.from_numpy(dataset_samples_weight).float(),
+        unique_attr_names,
+        np.unique(dataset_samples_weight).tolist(),
+    )
+
+
 class TTSDataset(Dataset):
     def __init__(
         self,

TTS/tts/models/delightful_tts.py

@@ -17,7 +17,7 @@ from trainer.io import load_fsspec
 from trainer.torch import DistributedSampler, DistributedSamplerWrapper
 from trainer.trainer_utils import get_optimizer, get_scheduler
 
-from TTS.tts.datasets.dataset import F0Dataset, TTSDataset, _parse_sample
+from TTS.tts.datasets.dataset import F0Dataset, TTSDataset, _parse_sample, get_attribute_balancer_weights
 from TTS.tts.layers.delightful_tts.acoustic_model import AcousticModel
 from TTS.tts.layers.losses import (
     ForwardSumLoss,
@@ -194,25 +194,6 @@ def wav_to_mel(y, n_fft, num_mels, sample_rate, hop_length, win_length, fmin, fm
 ##############################
 
 
-def get_attribute_balancer_weights(items: list, attr_name: str, multi_dict: dict = None):
-    """Create balancer weight for torch WeightedSampler"""
-    attr_names_samples = np.array([item[attr_name] for item in items])
-    unique_attr_names = np.unique(attr_names_samples).tolist()
-    attr_idx = [unique_attr_names.index(l) for l in attr_names_samples]
-    attr_count = np.array([len(np.where(attr_names_samples == l)[0]) for l in unique_attr_names])
-    weight_attr = 1.0 / attr_count
-    dataset_samples_weight = np.array([weight_attr[l] for l in attr_idx])
-    dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight)
-    if multi_dict is not None:
-        multiplier_samples = np.array([multi_dict.get(item[attr_name], 1.0) for item in items])
-        dataset_samples_weight *= multiplier_samples
-    return (
-        torch.from_numpy(dataset_samples_weight).float(),
-        unique_attr_names,
-        np.unique(dataset_samples_weight).tolist(),
-    )
-
-
 class ForwardTTSE2eF0Dataset(F0Dataset):
     """Override F0Dataset to avoid slow computing of pitches"""
 

TTS/tts/models/vits.py

@@ -21,7 +21,7 @@ from trainer.torch import DistributedSampler, DistributedSamplerWrapper
 from trainer.trainer_utils import get_optimizer, get_scheduler
 
 from TTS.tts.configs.shared_configs import CharactersConfig
-from TTS.tts.datasets.dataset import TTSDataset, _parse_sample
+from TTS.tts.datasets.dataset import TTSDataset, _parse_sample, get_attribute_balancer_weights
 from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
 from TTS.tts.layers.vits.discriminator import VitsDiscriminator
 from TTS.tts.layers.vits.networks import PosteriorEncoder, ResidualCouplingBlocks, TextEncoder
@@ -219,30 +219,6 @@ class VitsAudioConfig(Coqpit):
 ##############################
 
 
-def get_attribute_balancer_weights(items: list, attr_name: str, multi_dict: dict = None):
-    """Create inverse frequency weights for balancing the dataset.
-    Use `multi_dict` to scale relative weights."""
-    attr_names_samples = np.array([item[attr_name] for item in items])
-    unique_attr_names = np.unique(attr_names_samples).tolist()
-    attr_idx = [unique_attr_names.index(l) for l in attr_names_samples]
-    attr_count = np.array([len(np.where(attr_names_samples == l)[0]) for l in unique_attr_names])
-    weight_attr = 1.0 / attr_count
-    dataset_samples_weight = np.array([weight_attr[l] for l in attr_idx])
-    dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight)
-    if multi_dict is not None:
-        # check if all keys are in the multi_dict
-        for k in multi_dict:
-            assert k in unique_attr_names, f"{k} not in {unique_attr_names}"
-        # scale weights
-        multiplier_samples = np.array([multi_dict.get(item[attr_name], 1.0) for item in items])
-        dataset_samples_weight *= multiplier_samples
-    return (
-        torch.from_numpy(dataset_samples_weight).float(),
-        unique_attr_names,
-        np.unique(dataset_samples_weight).tolist(),
-    )
-
-
 class VitsDataset(TTSDataset):
     def __init__(self, model_args, *args, **kwargs):
         super().__init__(*args, **kwargs)