Create audioset.py

audioset.py

@@ -0,0 +1,181 @@
+# coding=utf-8
+
+"""AudioSet sound event classification dataset."""
+
+
+import os
+import json
+import gzip
+import joblib
+import shutil
+import pathlib
+import logging
+import zipfile
+import textwrap
+import datasets
+import requests
+import itertools
+import typing as tp
+import pandas as pd
+from pathlib import Path
+from copy import deepcopy
+from tqdm.auto import tqdm
+from rich.logging import RichHandler
+
+logger = logging.getLogger(__name__)
+logger.addHandler(RichHandler())
+logger.setLevel(logging.INFO)
+
+
+DATA_DIR_STRUCTURE = """
+audios/
+├── balanced_train_segments [20550 entries]
+├── eval_segments [18887 entries]
+└── unbalanced_train_segments
+    ├── unbalanced_train_segments_part00 [46940 entries]
+    ...
+    └── unbalanced_train_segments_part40 [9844 entries]
+"""
+
+
+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),
+                    "sound": datasets.Sequence(datasets.Value("string")), 
+                    "label": datasets.Sequence(datasets.features.ClassLabel(names=CLASSES)), 
+                }
+            ),
+            name="balanced", 
+            description="",
+        ), 
+        AudioSetConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "sound": datasets.Sequence(datasets.Value("string")), 
+                    "label": datasets.Sequence(datasets.features.ClassLabel(names=CLASSES)), 
+                }
+            ),
+            name="unbalanced", 
+            description="",
+        ), 
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="",
+            features=self.config.features,
+            supervised_keys=None,
+            homepage="",
+            citation="",
+            task_templates=None,
+        )
+
+    @property
+    def manual_download_instructions(self):
+        return (
+            "To use AudioSet you have to download it manually. "
+            "Please download the dataset from https://huggingface.co/datasets/confit/audioset-full \n"
+            "Then extract all files in one folder called `audios` and load the dataset with: "
+            "`datasets.load_dataset('confit/audioset', 'balanced', data_dir='path/to/folder')`\n"
+            "The tree structure of the downloaded data looks like: \n"
+            f"{DATA_DIR_STRUCTURE}"
+        )
+
+    def _split_generators(self, dl_manager):
+
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                f"{data_dir} does not exist. Make sure you insert a manual dir via "
+                f"`datasets.load_dataset('confit/audioset', 'balanced', data_dir=...)` that includes files unzipped from all the zip files. "
+                f"Manual download instructions: {self.manual_download_instructions}"
+            )
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split": "train", "data_dir": data_dir}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split": "test", "data_dir": data_dir}),
+        ]
+
+    def _generate_examples(self, split, data_dir):
+        """Generate examples from AudioSet"""
+        # Iterating the contents of the data to extract the relevant information
+        extensions = ['.wav']
+
+        if split == 'train':
+            if self.config.name == 'balanced':
+                archive_path = os.path.join(data_dir, 'audios', 'balanced_train_segments')
+                metadata_url = 'https://huggingface.co/datasets/confit/audioset/resolve/main/metadata/audioset-20k.jsonl'
+            elif self.config.name == 'unbalanced':
+                archive_path = os.path.join(data_dir, 'audios', 'unbalanced_train_segments')
+                metadata_url = 'https://huggingface.co/datasets/confit/audioset/resolve/main/metadata/audioset-2m.jsonl'
+        elif split == 'test':
+            archive_path = os.path.join(data_dir, 'audios', 'eval_segments')
+            metadata_url = 'https://huggingface.co/datasets/confit/audioset/resolve/main/metadata/audioset-eval.jsonl'
+
+        response = requests.get(url)
+        if response.status_code == 200:
+            # Split the content by lines and parse each line as JSON
+            # Each line is like {"filename":"YN6UbMsh-q1c.wav","label":["Vehicle","Car"]}
+            data_list = [json.loads(line) for line in response.text.splitlines()]
+            fileid2labels = {item['filename']:item['label'] for item in data_list}
+        else:
+            logger.info(f"Failed to retrieve data: Status code {response.status_code}")
+        
+        _, wav_paths = fast_scandir(archive_path, extensions, recursive=True)
+
+        for guid, wav_path in enumerate(wav_paths):
+            fileid = Path(wav_path).name
+            sound = fileid2labels.get(fileid)
+            try:
+                yield guid, {
+                    "id": str(guid),
+                    "file": wav_path, 
+                    "audio": wav_path, 
+                    "sound": sound, 
+                    "label": sound, 
+                }
+            except:
+                continue
+
+
+def fast_scandir(path: str, extensions: 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 extensions:
+                        files.append(f.path)
+            except Exception:
+                pass
+    except Exception:
+        pass
+
+    if recursive:
+        for path in list(subfolders):
+            sf, f = fast_scandir(path, extensions, recursive=recursive)
+            subfolders.extend(sf)
+            files.extend(f)  # type: ignore
+
+    return subfolders, files