Create crema-d-script.py

crema-d-script.py

@@ -0,0 +1,128 @@
+# coding=utf-8
+
+"""CREMA-D 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
+
+SAMPLE_RATE = 16_000
+
+_COMPRESSED_FILENAME = 'crema-d.zip'
+
+CREMAD_EMOTIONS_MAPPING = {
+    'ANG': 'anger', 
+    'DIS': 'disgust', 
+    'FEA': 'fear', 
+    'HAP': 'happy', 
+    'NEU': 'neutral', 
+    'SAD': 'sad', 
+}
+CLASSES = list(sorted(CREMAD_EMOTIONS_MAPPING.values))
+
+
+class CremaDConfig(datasets.BuilderConfig):
+    """BuilderConfig for CREMA-D."""
+    
+    def __init__(self, features, **kwargs):
+        super(CremaDConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
+        self.features = features
+
+
+class CREMAD(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        GtzanConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "emotion": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=CLASSES),
+                }
+            ),
+            name="crema-d", 
+            description='',
+        ), 
+    ]
+
+    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)
+        extensions = ['.au']
+        _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+
+        train_walker, val_test_walker = train_test_split(
+            _walker, test_size=0.3, random_state=914, stratify=[default_find_classes(f) for f in _walker]
+        )
+        val_walker, test_walker = train_test_split(
+            val_test_walker, test_size=0.5, random_state=914, stratify=[default_find_classes(f) for f in val_test_walker]
+        )
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"audio_paths": train_walker, "split": "train"}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"audio_paths": val_walker, "split": "validation"}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"audio_paths": test_walker, "split": "test"}
+            ), 
+        ]
+
+    def _generate_examples(self, audio_paths, split=None):
+        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 CREMAD_EMOTIONS_MAPPING.get(Path(audio_path).name.split('_')[2])
+
+
+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