Convert balanced data to Parquet format

AudioSet.py

@@ -1,183 +0,0 @@
-# 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"
-
-_N_BAL_TRAIN_TARS = 10
-_N_UNBAL_TRAIN_TARS = 870
-_N_EVAL_TARS = 9
-
-
-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")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="balanced",
-            version=VERSION,
-            description="Balanced training and balanced evaluation set.",
-        ),
-        datasets.BuilderConfig(
-            name="unbalanced",
-            version=VERSION,
-            description="Full unbalanced training set and balanced evaluation set.",
-        ),
-    ]
-    DEFAULT_CONFIG_NAME = "balanced"
-
-    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
-        prefix = prefix + "/data"
-
-        _LABEL_URLS = {
-            "bal_train": (
-                f"{prefix}/balanced_train_segments.csv"
-                if self.config.name == "balanced"
-                else f"{prefix}/unbalanced_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(_N_BAL_TRAIN_TARS)]
-                if self.config.name == "balanced"
-                else [
-                    f"{prefix}/unbal_train{i:03d}.tar"
-                    for i in range(_N_UNBAL_TRAIN_TARS)
-                ]
-            ),
-            "eval": [f"{prefix}/eval0{i}.tar" for i in range(_N_EVAL_TARS)],
-        }
-
-        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):
-        with open(ontology) as fid:
-            ontology_data = json.load(fid)
-        id_to_name = {x["id"]: x["name"] for x in ontology_data}
-
-        labels_df = pd.read_csv(
-            labels,
-            skiprows=3,
-            header=None,
-            skipinitialspace=True,
-            names=["vid_id", "start", "end", "labels"],
-            index_col="vid_id",
-        )
-
-        for path, fid in audio_files:
-            vid_id = os.path.splitext(os.path.basename(path))[0]
-            label_ids = labels_df.loc[vid_id, "labels"].split(",")
-            human_labels = [id_to_name[x] for x in label_ids]
-            example = {
-                "video_id": vid_id,
-                "labels": label_ids,
-                "human_labels": human_labels,
-                "audio": {"path": path, "bytes": fid.read()},
-            }
-            yield vid_id, example

README.md

@@ -16,45 +16,14 @@ config_names:
 - unbalanced
 tags:
 - audio
-dataset_info:
+configs:
 - config_name: balanced
-  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
-- config_name: unbalanced
-  features:
-  - name: video_id
-    dtype: string
-  - name: audio
-    dtype: audio
-  - name: labels
-    sequence: string
-  - name: human_labels
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 2408656417541
-    num_examples: 1738788
-  - name: test
-    num_bytes: 23763682278
-    num_examples: 17142
-  download_size: 2433673104977
-  dataset_size: 2432420099819
+  default: true
+  data_files:
+  - split: train
+    path: data/bal_train/*.parquet
+  - split: test
+    path: data/eval/*.parquet
 ---
 
 # Dataset Card for AudioSet

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