Rename mswc.py to emodb.py

emodb.py

@@ -0,0 +1,131 @@
+# coding=utf-8
+
+"""EmoDB paralinguistics dataset."""
+
+
+import os
+import textwrap
+import datasets
+import itertools
+import typing as tp
+from pathlib import Path
+
+from ._emodb import OFFICIAL_TRAIN, OFFICIAL_TEST
+
+
+SAMPLE_RATE = 16_000
+
+_COMPRESSED_FILENAME = 'emo-db.tar.gz'
+
+EMOTIONS_MAPPING = {
+    'A': 'anxiety', 
+    'E': 'disgust', 
+    'F': 'happiness', 
+    'L': 'boredom', 
+    'N': 'neutral', 
+    'T': 'sadness', 
+    'W': 'anger', 
+}
+
+EMOTIONS = [
+    'anxiety', 'disgust', 'happiness', 'boredom', 'neutral', 'sadness', 'anger'
+]
+
+
+class EmodbConfig(datasets.BuilderConfig):
+    """BuilderConfig for EmoDB."""
+    
+    def __init__(self, features, **kwargs):
+        super(EmodbConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
+        self.features = features
+
+
+class EmoDB(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        EmodbConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "emotion": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=EMOTIONS),
+                }
+            ),
+            name="gtzan", 
+            description=textwrap.dedent(
+                """\
+                Paralinguistics classifies each audio for its emotion as a multi-class
+                classification, where emotions are in the same pre-defined set for both training and testing. 
+                The evaluation metric is accuracy (ACC).
+                """
+            ),
+        ), 
+    ]
+
+    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.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)
+
+        if split == 'train':
+            _walker = [fileid for fileid in _walker if Path(fileid).stem in OFFICIAL_TRAIN]
+        elif split == 'test':
+            _walker = [fileid for fileid in _walker if Path(fileid).stem in OFFICIAL_TEST]
+
+        for guid, audio_path in enumerate(_walker):
+            yield guid, {
+                "id": str(guid),
+                "file": audio_path, 
+                "audio": audio_path, 
+                "emotion": EMOTIONS_MAPPING.get(Path(audio_path).stem[-2]), 
+                "label": EMOTIONS_MAPPING.get(Path(audio_path).stem[-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

mswc.py

@@ -1,30 +0,0 @@
-# coding=utf-8
-
-"""EmoDB paralinguistics dataset."""
-
-
-import os
-import textwrap
-import datasets
-import itertools
-import typing as tp
-from pathlib import Path
-
-from ._mswc import (
-    TRAIN_ENG, VALIDATION_ENG, TEST_ENG, 
-    TRAIN_SPA, VALIDATION_SPA, TEST_SPA, 
-    TRAIN_IND, VALIDATION_IND, TEST_IND, 
-)
-
-
-SAMPLE_RATE = 16_000
-
-_COMPRESSED_FILENAME = 'gtzan.tar.gz'
-
-
-class EmodbConfig(datasets.BuilderConfig):
-    """BuilderConfig for EmoDB."""
-    
-    def __init__(self, features, **kwargs):
-        super(GtzanConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
-        self.features = features