Duplicate from Dragunflie-420/FMADataset

Co-authored-by: Nikki Russell <Dragunflie-420@users.noreply.huggingface.co>

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+license: cc-by-4.0
+pretty_name: Free Music Archive (FMA) Dataset
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- audio-classification
+task_ids:
+- multi-class-classification
+---
+
+# Free Music Archive (FMA) Dataset
+
+## Overview
+
+This repository contains the Free Music Archive (FMA) dataset, curated and made available on Hugging Face by [dragunflie-420](https://huggingface.co/dragunflie-420). The FMA dataset is a large-scale, open-source dataset of music tracks, designed for music information retrieval and machine learning tasks.
+
+## Dataset Description
+
+The Free Music Archive (FMA) is an open and easily accessible dataset consisting of full-length audio tracks with associated metadata. This particular version focuses on the "small" subset of the FMA, which includes:
+
+- 8,000 tracks of 30 seconds each
+- 8 balanced genres (Electronic, Experimental, Folk, Hip-Hop, Instrumental, International, Pop, Rock)
+- Audio files in 128k MP3 format
+- Comprehensive metadata for each track
+
+## Contents
+
+This dataset provides:
+
+1. Audio files: 30-second MP3 clips of music tracks
+2. Metadata: Information about each track, including:
+   - Track ID
+   - Title
+   - Artist
+   - Genre
+   - Additional features (e.g., acoustic features, music analysis data)
+
+## Data Files
+
+To use this dataset, you need to manually download and place the following files in the repository:
+
+1. `fma_small.zip`: Contains the audio files
+2. `fma_metadata.zip`: Contains the metadata for the tracks
+
+After downloading, extract these files and ensure the following directory structure:
+
+```
+fma_dataset/
+├── fma_small/
+│   ├── 000/
+│   ├── 001/
+│   └── ...
+└── fma_metadata/
+    ├── tracks.csv
+    ├── genres.csv
+    └── features.csv
+```
+
+## Usage
+
+To use this dataset in your Hugging Face projects:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("dragunflie-420/fma")
+
+# Access the first example
+first_example = dataset['train'][0]
+print(first_example['title'], first_example['artist'], first_example['genre'])
+
+# Play the audio (if in a notebook environment)
+from IPython.display import Audio
+Audio(first_example['audio']['array'], rate=first_example['audio']['sampling_rate'])
+```
+
+[... rest of the README content remains the same ...]
+
+
+
+
+
+---
+language:
+- en
+license: cc-by-4.0
+pretty_name: Free Music Archive (FMA) Dataset
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- audio-classification
+task_ids:
+- multi-class-classification
+---
+
+# Free Music Archive (FMA) Dataset
+
+## Overview
+
+This repository contains the Free Music Archive (FMA) dataset, curated and made available on Hugging Face by [dragunflie-420](https://huggingface.co/dragunflie-420). The FMA dataset is a large-scale, open-source dataset of music tracks, designed for music information retrieval and machine learning tasks.
+
+[... rest of the README content remains the same ...] Free Music Archive (FMA) Dataset
+
+## Dataset Description
+
+The Free Music Archive (FMA) is an open and easily accessible dataset consisting of full-length audio tracks with associated metadata. This particular version focuses on the "small" subset of the FMA, which includes:
+
+- 8,000 tracks of 30 seconds each
+- 8 balanced genres (Electronic, Experimental, Folk, Hip-Hop, Instrumental, International, Pop, Rock)
+- Audio files in 128k MP3 format
+- Comprehensive metadata for each track
+
+## Contents
+
+This dataset provides:
+
+1. Audio files: 30-second MP3 clips of music tracks
+2. Metadata: Information about each track, including:
+   - Track ID
+   - Title
+   - Artist
+   - Genre
+   - Additional features (e.g., acoustic features, music analysis data)
+
+## Usage
+
+To use this dataset in your Hugging Face projects:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("dragunflie-420/fma")
+
+# Access the first example
+first_example = dataset['train'][0]
+print(first_example['title'], first_example['artist'], first_example['genre'])
+
+# Play the audio (if in a notebook environment)
+from IPython.display import Audio
+Audio(first_example['audio']['array'], rate=first_example['audio']['sampling_rate'])
+```
+
+## Dataset Structure
+
+Each example in the dataset contains:
+
+- `track_id`: Unique identifier for the track
+- `title`: Title of the track
+- `artist`: Name of the artist
+- `genre`: Top-level genre classification
+- `audio`: Audio file in the format compatible with Hugging Face's Audio feature
+
+## Applications
+
+This dataset is suitable for various music information retrieval and machine learning tasks, including:
+
+- Music genre classification
+- Artist identification
+- Music recommendation systems
+- Audio feature extraction and analysis
+- Music generation and style transfer
+
+## Citation
+
+If you use this dataset in your research, please cite the original FMA paper:
+
+```
+@inproceedings{defferrard2016fma,
+  title={FMA: A Dataset for Music Analysis},
+  author={Defferrard, Micha{\"e}l and Ben

fma.py

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+# 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,
+                    }
+
+    @property
+    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,
+                    }