Datasets:
Tasks:
Audio Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K<n<10K
License:
| # coding=utf-8 | |
| # Copyright 2024 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import os | |
| import pandas as pd | |
| import numpy as np | |
| import datasets | |
| _CITATION = """ | |
| @inproceedings{defferrard2016fma, | |
| title={FMA: A Dataset for Music Analysis}, | |
| author={Defferrard, Micha{\"e}l and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier}, | |
| booktitle={18th International Society for Music Information Retrieval Conference}, | |
| year={2017}, | |
| } | |
| """ | |
| _DESCRIPTION = """ | |
| The Free Music Archive (FMA) is an open and easily accessible dataset of music collections. | |
| """ | |
| _HOMEPAGE = "https://github.com/mdeff/fma" | |
| _LICENSE = "Creative Commons Attribution 4.0 International License" | |
| _URLs = { | |
| "small": "https://os.unil.cloud.switch.ch/fma/fma_small.zip", | |
| "metadata": "https://os.unil.cloud.switch.ch/fma/fma_metadata.zip", | |
| } | |
| class FMADataset(datasets.GeneratorBasedBuilder): | |
| """FMA small dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig(name="small", version=VERSION, description="The small subset of FMA dataset"), | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "track_id": datasets.Value("int32"), | |
| "title": datasets.Value("string"), | |
| "artist": datasets.Value("string"), | |
| "genre": datasets.Value("string"), | |
| "audio": datasets.Audio(sampling_rate=44100), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| data_dir = dl_manager.download_and_extract(_URLs) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir["small"], "fma_small"), | |
| "metadata_path": os.path.join(data_dir["metadata"], "fma_metadata"), | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, metadata_path): | |
| """Yields examples.""" | |
| # Load metadata | |
| tracks = pd.read_csv(os.path.join(metadata_path, "tracks.csv"), index_col=0, header=[0, 1]) | |
| # Iterate through audio files | |
| for root, _, files in os.walk(filepath): | |
| for file in files: | |
| if file.endswith('.mp3'): | |
| track_id = int(file.split('.')[0]) | |
| audio_path = os.path.join(root, file) | |
| # Get metadata | |
| title = tracks.loc[track_id, ('track', 'title')] | |
| artist = tracks.loc[track_id, ('artist', 'name')] | |
| genre = tracks.loc[track_id, ('track', 'genre_top')] | |
| yield track_id, { | |
| "track_id": track_id, | |
| "title": title, | |
| "artist": artist, | |
| "genre": genre, | |
| "audio": audio_path, | |
| } | |
| def manual_download_instructions(self): | |
| return """ | |
| To use the FMA dataset, you need to download it manually. Please follow these steps: | |
| 1. Go to https://github.com/mdeff/fma | |
| 2. Download the 'fma_small.zip' and 'fma_metadata.zip' files | |
| 3. Extract both zip files | |
| 4. Copy the 'fma_small' folder and the 'fma_metadata' folder to the root of this dataset repository | |
| Once you have completed these steps, the dataset will be ready to use. | |
| """ coding=utf-8 | |
| # Copyright 2024 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import os | |
| import pandas as pd | |
| import numpy as np | |
| import datasets | |
| _CITATION = """ | |
| @inproceedings{defferrard2016fma, | |
| title={FMA: A Dataset for Music Analysis}, | |
| author={Defferrard, Micha{\"e}l and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier}, | |
| booktitle={18th International Society for Music Information Retrieval Conference}, | |
| year={2017}, | |
| } | |
| """ | |
| _DESCRIPTION = """ | |
| The Free Music Archive (FMA) is an open and easily accessible dataset of music collections. | |
| """ | |
| _HOMEPAGE = "https://github.com/mdeff/fma" | |
| _LICENSE = "Creative Commons Attribution 4.0 International License" | |
| _URLs = { | |
| "small": "https://os.unil.cloud.switch.ch/fma/fma_small.zip", | |
| "metadata": "https://os.unil.cloud.switch.ch/fma/fma_metadata.zip", | |
| } | |
| class FMADataset(datasets.GeneratorBasedBuilder): | |
| """FMA small dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig(name="small", version=VERSION, description="The small subset of FMA dataset"), | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "track_id": datasets.Value("int32"), | |
| "title": datasets.Value("string"), | |
| "artist": datasets.Value("string"), | |
| "genre": datasets.Value("string"), | |
| "audio": datasets.Audio(sampling_rate=44100), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| data_dir = dl_manager.download_and_extract(_URLs) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir["small"], "fma_small"), | |
| "metadata_path": os.path.join(data_dir["metadata"], "fma_metadata"), | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, metadata_path): | |
| """Yields examples.""" | |
| # Load metadata | |
| tracks = pd.read_csv(os.path.join(metadata_path, "tracks.csv"), index_col=0, header=[0, 1]) | |
| # Iterate through audio files | |
| for root, _, files in os.walk(filepath): | |
| for file in files: | |
| if file.endswith('.mp3'): | |
| track_id = int(file.split('.')[0]) | |
| audio_path = os.path.join(root, file) | |
| # Get metadata | |
| title = tracks.loc[track_id, ('track', 'title')] | |
| artist = tracks.loc[track_id, ('artist', 'name')] | |
| genre = tracks.loc[track_id, ('track', 'genre_top')] | |
| yield track_id, { | |
| "track_id": track_id, | |
| "title": title, | |
| "artist": artist, | |
| "genre": genre, | |
| "audio": audio_path, | |
| } |