Create audioset.py

audioset.py

@@ -0,0 +1,154 @@
+# coding=utf-8
+
+"""AudioSet sound event classification dataset."""
+
+
+import os
+import json
+import textwrap
+import datasets
+import itertools
+import typing as tp
+import pandas as pd
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+
+
+SAMPLE_RATE = 22_050
+
+_BALANCED_TRAIN_FILENAME = 'balanced_train_segments.zip'
+_EVAL_FILENAME = 'eval_segments.zip'
+
+ID2LABEL = json.load(
+    open(hf_hub_download("huggingface/label-files", "audioset-id2label.json", repo_type="dataset"), "r")
+)
+LABEL2ID = {v:k for k, v in ID2LABEL.items()}
+CLASSES = list(set(LABEL2ID.keys()))
+
+
+class AudioSetConfig(datasets.BuilderConfig):
+    """BuilderConfig for AudioSet."""
+    
+    def __init__(self, features, **kwargs):
+        super(AudioSetConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
+        self.features = features
+
+
+class AudioSet(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        AudioSetConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "label": datasets.Sequence(datasets.ClassLabel(CLASSES)), 
+                }
+            ),
+            name="balanced", 
+            description="",
+        ), 
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="",
+            features=self.config.features,
+            supervised_keys=None,
+            homepage="",
+            citation="",
+            task_templates=None,
+        )
+
+    def _preprocess_metadata_csv(self, csv_file):
+        df = pd.read_csv(csv_file, skiprows=2, sep=', ', engine='python')
+        df.rename(columns={'positive_labels': 'ids'}, inplace=True)
+        df['ids'] = [label.strip('\"').split(',') for label in df['ids']]
+        df['filename'] = (
+            'Y' + df['# YTID'] + '.wav'
+        )
+        return df[['filename', 'ids']]
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        if self.config.name == 'balanced':
+            archive_path = dl_manager.extract(_BALANCED_TRAIN_FILENAME)
+        elif self.config.name == 'unbalanced':
+            archive_path = dl_manager.extract(_UNBALANCED_TRAIN_FILENAME)
+        test_archive_path = dl_manager.extract(_EVAL_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": test_archive_path, "split": "test"}
+            ), 
+        ]
+
+    def _generate_examples(self, archive_path, split=None):
+        extensions = ['.wav']
+
+        test_metadata_csv ＝ 'metadata/eval_segments.csv'
+        if self.config.name == 'balanced':
+            train_metadata_csv ＝ 'metadata/balanced_train_segments.csv'
+        elif self.config.name == 'unbalanced':
+            train_metadata_csv ＝ 'metadata/unbalanced_train_segments.csv'
+        train_metadata_df = self._preprocess_metadata_csv(train_metadata_csv) # ['filename', 'ids']
+        test_metadata_df = self._preprocess_metadata_csv(test_metadata_csv) # ['filename', 'ids']
+        
+        class_labels_indices_df = pd.read_csv(
+            'metadata/class_labels_indices.csv'
+        ) # ['index', 'mid', 'display_name']
+        mid2label = {
+            row['mid']:row['display_name'] for idx, row in class_labels_indices_df.iterrows()
+        }
+
+        def default_find_classes(audio_path):
+            fileid = Path(audio_path).name
+            ids = metadata_df.query(f'filename=="{fileid}"')['ids'].values.tolist()
+            ids = [
+                mid2label.get(mid, None) for mid in flatten(ids)
+            ]
+            return ids
+
+        _walker = fast_scandir(archive_path, extensions, recursive=True)
+
+        for guid, audio_path in enumerate(_walker):
+            yield guid, {
+                "id": str(guid),
+                "file": audio_path, 
+                "audio": audio_path, 
+                "label": default_find_classes(audio_path), 
+            }
+
+
+def flatten(list2d):
+    return list(itertools.chain.from_iterable(list2d))
+
+
+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