Convert to Parquet format (#5)

- Convert balanced data to Parquet format (6762f044d1c88619c7f2006486036192128fb07e)
- Remove balanced data tar files (dbc89ba34a8f107ff1bef215a13eab648b0c7da8)
- Convert unbalanced data to Parquet format (0049167e89f259a010c3f070fe3666d9e5242836)
- Remove unbalanced labels CSV and update README (cb06767e072e3bd3e4145834cbcb0837b9e0065a)
- Move ontology.json (ceb9eaaa7844c9ad7351e659c84a572e376ad06d)

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

@@ -16,71 +16,54 @@ 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
+  default: true
+  data_files:
+  - split: train
+    path: data/bal_train/*.parquet
+  - split: test
+    path: data/eval/*.parquet
 - 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
+  data_files:
+  - split: train
+    path: data/unbal_train/*.parquet
+  - split: test
+    path: data/eval/*.parquet
+- config_name: full
+  data_files:
+  - split: bal_train
+    path: data/bal_train/*.parquet
+  - split: unbal_train
+    path: data/unbal_train/*.parquet
+  - split: eval
+    path: data/eval/*.parquet
 ---
 
 # Dataset Card for AudioSet
 
 ## Dataset Description
+
 - **Homepage**: https://research.google.com/audioset/index.html
 - **Paper**: https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/45857.pdf
 - **Leaderboard**: https://paperswithcode.com/sota/audio-classification-on-audioset
 
 ### Dataset Summary
-[AudioSet](https://research.google.com/audioset/dataset/index.html) is a
-dataset of 10-second clips from YouTube, annotated into one or more
-sound categories, following the AudioSet ontology.
+
+[AudioSet](https://research.google.com/audioset/dataset/index.html) is a dataset of 10-second clips from YouTube, annotated into one or more sound categories, following the AudioSet ontology.
 
 ### Supported Tasks and Leaderboards
-- `audio-classification`: Classify audio clips into categories. The
-  leaderboard is available
-  [here](https://paperswithcode.com/sota/audio-classification-on-audioset)
+
+- `audio-classification`: Classify audio clips into categories. The leaderboard is available [here](https://paperswithcode.com/sota/audio-classification-on-audioset)
 
 ### Languages
-The class labels in the dataset are in English.
 
+The class labels in the dataset are in English.
 
 ## Dataset Structure
 
 ### Data Instances
+
 Example instance from the dataset:
 ```python
 {
@@ -97,6 +80,7 @@ Example instance from the dataset:
 ```
 
 ### Data Fields
+
 Instances have the following fields:
 - `video_id`: a `string` feature containing the original YouTube ID.
 - `audio`: an `Audio` feature containing the audio data and sample rate.
@@ -106,70 +90,80 @@ Instances have the following fields:
   human-readable forms of the same labels as in `labels`.
 
 ### Data Splits
+
 The distribuion of audio clips is as follows:
 
 #### `balanced` configuration
 |           |train|test |
 |-----------|----:|----:|
-|# instances|18685|17142|
+|# instances|18683|17141|
 
 #### `unbalanced` configuration
 |           |train  |test |
 |-----------|------:|----:|
-|# instances|1738788|17142|
+|# instances|1738657|17141|
 
 
 ## Dataset Creation
 
 ### Curation Rationale
+
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
+
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 #### Who are the source language producers?
+
 The labels are from the AudioSet ontology. Audio clips are from YouTube.
 
 ### Annotations
 
 #### Annotation process
+
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 #### Who are the annotators?
+
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 ### Personal and Sensitive Information
+
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
+
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 ### Discussion of Biases
+
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 ### Other Known Limitations
-1. The YouTube videos in this copy of AudioSet were downloaded in March
-   2023, so not all of the original audios are available. The number of
-   clips able to be downloaded is as follows:
-   - Balanced train: 18685 audio clips out of 22160 originally.
+
+1. The YouTube videos in this copy of AudioSet were downloaded in March 2023, so not all of the original audios are available. The number of clips able to be downloaded is as follows:
+   - Balanced train: 18683 audio clips out of 22160 originally.
    - Unbalanced train: 1738788 clips out of 2041789 originally.
-   - Evaluation: 17142 audio clips out of 20371 originally.
-2. Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at
-   44.1 kHz 24 bit. Audio files are stored in the FLAC format.
+   - Evaluation: 17141 audio clips out of 20371 originally.
+2. Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at 44.1 kHz 24 bit. Audio files are stored in the FLAC format.
 
 ## Additional Information
 
 ### Dataset Curators
+
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 ### Licensing Information
+
 The AudioSet data is licensed under CC-BY-4.0
 
 ## Citation
+
 ```bibtex
 @inproceedings{jort_audioset_2017,
 	title	= {Audio Set: An ontology and human-labeled dataset for audio events},

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