New Dataset Structure

dataset.py

@@ -1,14 +1,10 @@
 import datasets
 from pathlib import Path
-import random
 
-_DESCRIPTION = "Custom dataset with audio (.wav) and phoneme (.txt) pairs, split by speaker."
-
-# For reproducibility!
-_SEED = 42
+_DESCRIPTION = "Custom dataset with audio (.wav) and phoneme (.txt) pairs, sharded by split."
 
 class CustomAudioPhonemeDataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.5")
+    VERSION = datasets.Version("1.1.0")
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -22,59 +18,38 @@ class CustomAudioPhonemeDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        data_dir = dl_manager.manual_dir
-
-        # Get all speakers from 'wav'
-        wav_dir = Path(data_dir) / 'wav'
-        speakers = sorted([d.name for d in wav_dir.iterdir() if d.is_dir()])
-        random.seed(_SEED)
-        random.shuffle(speakers)
-
-        # 80/10/10 split
-        N = len(speakers)
-        n_train = int(0.8 * N)
-        n_val = int(0.1 * N)
-
-        train_speakers = set(speakers[:n_train])
-        val_speakers = set(speakers[n_train:n_train + n_val])
-        test_speakers = set(speakers[n_train + n_val:])
-
+        # Each split is a folder: train/, validation/, test/
+        data_dir = Path(dl_manager.manual_dir)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"dataset_folder": data_dir, "split_speakers": train_speakers},
+                gen_kwargs={"split_dir": data_dir / "train"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"dataset_folder": data_dir, "split_speakers": val_speakers},
+                gen_kwargs={"split_dir": data_dir / "validation"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"dataset_folder": data_dir, "split_speakers": test_speakers},
+                gen_kwargs={"split_dir": data_dir / "test"},
             ),
         ]
 
-    def _generate_examples(self, dataset_folder, split_speakers):
-        dataset_folder = Path(dataset_folder)
-        wav_dir = dataset_folder / 'wav'
-        phoneme_dir = dataset_folder / 'phonemized'
+    def _generate_examples(self, split_dir):
+        # Folders inside split_dir: wav/ and phonemized/
+        wav_dir = Path(split_dir) / "wav"
+        phoneme_dir = Path(split_dir) / "phonemized"
 
-        # Gather audio and phoneme files (still recursively)
-        audio_files = sorted([p for p in wav_dir.rglob('*.wav') if not p.name.startswith('._')])
-        phoneme_files = sorted([p for p in phoneme_dir.rglob('*.txt') if not p.name.startswith('._')])
+        audio_files = sorted([p for p in wav_dir.rglob("*.wav") if not p.name.startswith("._")])
+        phoneme_files = sorted([p for p in phoneme_dir.rglob("*.txt") if not p.name.startswith("._")])
 
-        # Map speaker from the relative path (assumes: wav/speaker/file.wav)
         def get_speaker(path):
-            # wav/speaker/utterance.wav → speaker
             return path.parent.name
 
-        # Match by base name
         audio_map = {(get_speaker(p), p.stem): p for p in audio_files}
         phoneme_map = {(get_speaker(p), p.stem): p for p in phoneme_files}
 
-        # Only use files where speaker is in split
         keys = set(audio_map.keys()) & set(phoneme_map.keys())
-        keys = [k for k in keys if k[0] in split_speakers]
 
         for idx, (speaker, stem) in enumerate(sorted(keys)):
             audio_path = str(audio_map[(speaker, stem)])