Text-to-Music Generation

MusicCaps is a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts. For each 10-second music clip, MusicCaps provides: 1) A free-text caption consisting of four sentences on average, describing the music and 2) A list of music aspects, describing genre, mood, tempo, singer voices, instrumentation, dissonances, rhythm, etc. Source:MusicLM: Generating Music From Text

The MusicBench dataset is a music audio-text pair dataset that was designed for text-to-music generation purpose and released along with Mustango text-to-music model. MusicBench is based on the MusicCaps dataset, which it expands from 5,521 samples to 52,768 training and 400 test samples! Dataset Details MusicBench expands MusicCaps by: Including music features of chords, beats, tempo, and key that are extracted from the audio. Describing these music features using text templates and thus enhancing the original text prompts. Expanding the number of audio samples by performing musically meaningful augmentations: semitone pitch shifts, tempo changes, and volume changes. Train set size = 52,768 samples Test set size = 400 This dataset also includes FMACaps, which was used as a second test set.

The Song Describer Dataset (SDD) contains ~1.1k captions for 706 permissively licensed music recordings. It is designed for use in evaluation of models that address music-and-language (M&L) tasks such as music captioning, text-to-music generation and music-language retrieval.

The AIME dataset contains 6,000 audio tracks generated by 12 music generation models in addition to 500 tracks from MTG-Jamendo. The prompts used to generate music are combinations of representative and diverse tags from the MTG-Jamendo dataset.

📊 Dataset Details - Name: JamendoMaxCaps - URL: https://huggingface.co/datasets/amaai-lab/JamendoMaxCaps - Content: 362,238 songs with captions generated by Qwen2-Audio ### Metadata Fields - genre - speed - variable tags ## 🎯 Rationale 1. Scale: Over a quarter-million tracks, offering a rich multimodal corpus. 2. Quality: Captions generated by the state-of-the-art Qwen2-Audio model. 3. Use cases: Supports audio captioning, retrieval, genre classification, tempo/speed analysis, and more.

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