Add dataset loading script.

AudioSet.py

@@ -0,0 +1,157 @@
+# Copyright (C) 2024 Aaron Keesing
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# “Software”), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from itertools import chain
+import json
+import os
+import tarfile
+
+import pandas as pd
+import datasets
+
+
+_CITATION = """\
+@inproceedings{45857,
+    title = {Audio Set: An ontology and human-labeled dataset for audio events},
+    author = {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
+    year = {2017},
+    booktitle = {Proc. IEEE ICASSP 2017},
+    address	= {New Orleans, LA}
+}
+"""
+
+_DESCRIPTION = """\
+This repository contains the balanced training set and evaluation set of the AudioSet
+data, described here: https://research.google.com/audioset/dataset/index.html. The
+YouTube videos were downloaded in March 2023, and so not all of the original audios are
+available.
+"""
+
+_HOMEPAGE = "https://research.google.com/audioset/dataset/index.html"
+
+_LICENSE = "cc-by-4.0"
+
+_URL_PREFIX = "https://huggingface.co/datasets/agkphysics/AudioSet/resolve/main"
+
+
+def _iter_tar(path):
+    """Iterate through the tar archive, but without skipping some files, which the HF
+    DL does.
+    """
+    with open(path, "rb") as fid:
+        stream = tarfile.open(fileobj=fid, mode="r|*")
+        for tarinfo in stream:
+            file_obj = stream.extractfile(tarinfo)
+            yield tarinfo.name, file_obj
+            stream.members = []
+        del stream
+
+
+class AudioSetDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            features=datasets.Features(
+                {
+                    "video_id": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=None, mono=True, decode=True),
+                    "labels": datasets.Sequence(datasets.Value("string")),
+                    "human_labels": datasets.Sequence(datasets.Value("string")),
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        if self.config.data_dir:
+            prefix = self.config.data_dir
+        else:
+            prefix = _URL_PREFIX
+
+        _LABEL_URLS = {
+            "bal_train": f"{prefix}/balanced_train_segments.csv",
+            "eval": f"{prefix}/eval_segments.csv",
+            "ontology": f"{prefix}/ontology.json",
+        }
+
+        _DATA_URLS = {
+            "bal_train": [f"{prefix}/bal_train0{i}.tar" for i in range(10)],
+            "eval": [f"{prefix}/eval0{i}.tar" for i in range(9)],
+        }
+
+        tar_files = dl_manager.download(_DATA_URLS)
+        label_files = dl_manager.download(_LABEL_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "labels": label_files["bal_train"],
+                    "ontology": label_files["ontology"],
+                    "audio_files": chain.from_iterable(
+                        _iter_tar(x) for x in tar_files["bal_train"]
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "labels": label_files["eval"],
+                    "ontology": label_files["ontology"],
+                    "audio_files": chain.from_iterable(
+                        _iter_tar(x) for x in tar_files["eval"]
+                    ),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, labels, ontology, audio_files):
+        labels_df = pd.read_csv(
+            labels,
+            skiprows=3,
+            header=None,
+            skipinitialspace=True,
+            names=["vid_id", "start", "end", "labels"],
+        )
+        with open(ontology) as fid:
+            ontology_data = json.load(fid)
+        id_to_name = {x["id"]: x["name"] for x in ontology_data}
+
+        examples = {}
+        for _, row in labels_df.iterrows():
+            label_ids = row["labels"].split(",")
+            human_labels = [id_to_name[x] for x in label_ids]
+            examples[row["vid_id"]] = {
+                "video_id": row["vid_id"],
+                "labels": label_ids,
+                "human_labels": human_labels,
+            }
+
+        for path, fid in audio_files:
+            vid_id = os.path.splitext(os.path.basename(path))[0]
+            if vid_id in examples:
+                audio = {"path": path, "bytes": fid.read()}
+                examples[vid_id]["audio"] = audio
+                yield vid_id, examples[vid_id]

README.md

@@ -1,9 +1,28 @@
 ---
 license: cc-by-4.0
-tags:
-- audio
 task_categories:
 - audio-classification
+tags:
+- audio
+dataset_info:
+  features:
+  - name: video_id
+    dtype: string
+  - name: audio
+    dtype: audio
+  - name: labels
+    sequence: string
+  - name: human_labels
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 26016210987
+    num_examples: 18685
+  - name: test
+    num_bytes: 23763682278
+    num_examples: 17142
+  download_size: 49805654900
+  dataset_size: 49779893265
 ---
 
 # AudioSet data
@@ -23,7 +42,7 @@ Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at
 
 ## Citation
 ```bibtex
-@inproceedings{45857,
+@inproceedings{jort_audioset_2017,
 	title	= {Audio Set: An ontology and human-labeled dataset for audio events},
 	author	= {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
 	year	= {2017},