Buckets:
| license: mit | |
| task_categories: | |
| - text-to-audio | |
| - audio-classification | |
| tags: | |
| - music | |
| - audio | |
| - suno | |
| - music-generation | |
| - ai-music | |
| pretty_name: Suno Audio Dataset | |
| viewer: true | |
| size_categories: | |
| - 10K<n<100K | |
| # 🎵 Suno Audio Dataset | |
| A comprehensive dataset of **49,698** AI-generated music tracks from Suno, organized in **50** batches of 1000 samples each. | |
| 🎧 **All audio files are playable directly in the dataset viewer!** | |
| ## Dataset Structure | |
| The dataset is organized into batches (`batch_0`, `batch_1`, etc.), each containing up to 1000 audio samples with metadata. | |
| ### Fields | |
| - **`audio`**: 🎵 **Playable MP3 audio file** (click to play in viewer!) | |
| - `id`: Unique track identifier | |
| - `title`: Song title | |
| - `display_name`: Creator/artist name | |
| - `handle`: Creator handle | |
| - `tags`: Music tags, genres, and styles | |
| - `prompt`: Text prompt used for generation | |
| - `duration`: Track duration in seconds | |
| - `play_count`: Number of plays on Suno | |
| - `upvote_count`: Community upvotes | |
| - `model_name`: Suno model version used | |
| - `created_at`: Creation timestamp | |
| - `status`: Track status | |
| - `is_public`: Public visibility flag | |
| ## Usage | |
| ### Load Entire Dataset | |
| ```python | |
| from datasets import load_dataset | |
| # Load all batches | |
| dataset = load_dataset("Humair332/suno-audio") | |
| print(f"Total tracks: {len(dataset['train'])}") | |
| ``` | |
| ### Load Specific Batch | |
| ```python | |
| # Load only batch 0 | |
| dataset = load_dataset("Humair332/suno-audio", data_dir="batch_0") | |
| ``` | |
| ### Play Audio | |
| ```python | |
| # Get audio data | |
| audio_data = dataset['train'][0]['audio'] | |
| audio_array = audio_data['array'] | |
| sampling_rate = audio_data['sampling_rate'] | |
| # Play in Jupyter/Colab | |
| from IPython.display import Audio | |
| Audio(audio_array, rate=sampling_rate) | |
| ``` | |
| ### Filter by Tags | |
| ```python | |
| # Filter by genre | |
| rock_songs = dataset['train'].filter(lambda x: 'rock' in x['tags'].lower()) | |
| print(f"Found {len(rock_songs)} rock songs") | |
| ``` | |
| ### Most Popular Tracks | |
| ```python | |
| # Sort by play count | |
| from datasets import Dataset | |
| df = dataset['train'].to_pandas() | |
| top_tracks = df.nlargest(10, 'play_count')[['title', 'display_name', 'play_count']] | |
| print(top_tracks) | |
| ``` | |
| ## Dataset Statistics | |
| - **Total Tracks**: 49,698 | |
| - **Batches**: 50 | |
| - **Batch Size**: 1000 | |
| - **Format**: Apache Arrow with embedded MP3 audio | |
| - **Audio Format**: MP3 | |
| - **Metadata**: Tags, prompts, engagement metrics | |
| ## Explore the Music! 🎶 | |
| Click on the dataset viewer above and browse through the tracks. Click any row to play the audio directly in your browser! | |
| ## Source | |
| Original dataset: [nyuuzyou/suno](https://huggingface.co/datasets/nyuuzyou/suno) | |
| ## License | |
| MIT License | |
| ## Citation | |
| If you use this dataset, please cite: | |
| ```bibtex | |
| @dataset{suno_audio_dataset, | |
| title={Suno Audio Dataset}, | |
| author={Humair332}, | |
| year={2026}, | |
| publisher={Hugging Face}, | |
| url={https://huggingface.co/datasets/Humair332/suno-audio} | |
| } | |
| ``` | |
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