Create ravdess-script.py

ravdess-script.py

@@ -0,0 +1,176 @@
+# coding=utf-8
+
+"""RAVDESS paralinguistics classification dataset."""
+
+
+import os
+import textwrap
+import datasets
+import itertools
+import typing as tp
+from pathlib import Path
+from sklearn.model_selection import train_test_split
+
+_COMPRESSED_FILENAME = 'ravdess.zip'
+
+SAMPLE_RATE = 48_000
+
+RAVDESS_EMOTIONS_MAPPING = {
+    '01': 'neutral', 
+    '02': 'calm', 
+    '03': 'happy', 
+    '04': 'sad', 
+    '05': 'angry', 
+    '06': 'fearful', 
+    '07': 'disgust', 
+    '08': 'surprised', 
+}
+RAVDESS_ACTOR_FOLD_MAPPING = {
+    '01': '4', 
+    '02': '0', 
+    '03': '1', 
+    '04': '4', 
+    '05': '0', 
+    '06': '1', 
+    '07': '1', 
+    '08': '3', 
+    '09': '4', 
+    '10': '2', 
+    '11': '2', 
+    '12': '2', 
+    '13': '1', 
+    '14': '0', 
+    '15': '0', 
+    '16': '0', 
+    '17': '3',  
+    '18': '1',  
+    '19': '2', 
+    '20': '2', 
+    '21': '3', 
+    '22': '4', 
+    '23': '3', 
+    '24': '3', 
+}
+CLASSES = list(RAVDESS_EMOTIONS_MAPPING.values())
+
+
+class RavdessConfig(datasets.BuilderConfig):
+    """BuilderConfig for RAVDESS."""
+    
+    def __init__(self, features, **kwargs):
+        super(RavdessConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
+        self.features = features
+
+
+class RAVDESS(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        RavdessConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "emotion": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=CLASSES),
+                }
+            ),
+            name=f"fold{f}", 
+            description='',
+        ) for f in range(1, 6)
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="",
+            features=self.config.features,
+            supervised_keys=None,
+            homepage="",
+            citation="",
+            task_templates=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        archive_path = dl_manager.extract(_COMPRESSED_FILENAME)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split": "train"}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path, "split": "validation"}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
+            ), 
+        ]
+
+    def _generate_examples(self, archive_path, split=None):
+        extensions = ['.wav']
+        _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+        _walker_with_fold = [(fileid, default_find_fold(fileid)) for fileid in _walker]
+
+        if self.config.name == 'fold1':
+            train_fold = ['2', '3', '4', '5']
+            test_fold = ['1']
+        elif self.config.name == 'fold2':
+            train_fold = ['1', '3', '4', '5']
+            test_fold = ['2']
+        elif self.config.name == 'fold3':
+            train_fold = ['1', '2', '4', '5']
+            test_fold = ['3']
+        elif self.config.name == 'fold4':
+            train_fold = ['1', '2', '3', '5']
+            test_fold = ['4']
+        elif self.config.name == 'fold5':
+            train_fold = ['1', '2', '3', '4']
+            test_fold = ['5']
+
+        if split == 'train':
+            audio_paths = [fileid for fileid, fold in _walker_with_fold if fold in train_fold]
+        elif split == 'test':
+            audio_paths = [fileid for fileid, fold in _walker_with_fold if fold in train_fold]
+
+        for guid, audio_path in enumerate(audio_paths):
+            yield guid, {
+                "id": str(guid), 
+                "file": audio_path, 
+                "audio": audio_path, 
+                "emotion": default_find_classes(audio_path), 
+                "label": default_find_classes(audio_path), 
+            }
+
+
+def default_find_classes(audio_path):
+    return RAVDESS_EMOTIONS_MAPPING.get(Path(audio_path).name.split('-')[2])
+
+
+def default_find_fold(audio_path):
+    actor_id = Path(audio_path).parent.stem.split('_')[1]
+    return RAVDESS_ACTOR_FOLD_MAPPING.get(actor_id)
+
+
+def fast_scandir(path: str, exts: tp.List[str], recursive: bool = False):
+    # Scan files recursively faster than glob
+    # From github.com/drscotthawley/aeiou/blob/main/aeiou/core.py
+    subfolders, files = [], []
+
+    try:  # hope to avoid 'permission denied' by this try
+        for f in os.scandir(path):
+            try:  # 'hope to avoid too many levels of symbolic links' error
+                if f.is_dir():
+                    subfolders.append(f.path)
+                elif f.is_file():
+                    if os.path.splitext(f.name)[1].lower() in exts:
+                        files.append(f.path)
+            except Exception:
+                pass
+    except Exception:
+        pass
+
+    if recursive:
+        for path in list(subfolders):
+            sf, f = fast_scandir(path, exts, recursive=recursive)
+            subfolders.extend(sf)
+            files.extend(f)  # type: ignore
+
+    return subfolders, files