| --- |
| license: other |
| license_name: truemuse-research |
| pretty_name: TrueMuse |
| tags: |
| - music |
| - audio |
| - text-to-music |
| - training-data-attribution |
| - benchmark |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # TrueMuse: A Benchmark for Data Attribution in Text-to-Music Models |
|
|
| [**Paper (arXiv)**](https://arxiv.org/abs/2610.00835) | [**Code (GitHub)**](https://github.com/ZeliuFoo/TrueMuse) |
|
|
| TrueMuse evaluates **training-data attribution for text-to-music generation**. Each attribute (a musician, |
| an instrument, a genre or a melody) is injected into a generator through DreamBooth fine-tuning on a small set |
| of source clips, so the source of every generated sample is known. Attribution methods are asked to retrieve |
| those source clips from the generated audio, among a large pool of distractors. |
|
|
| | | | |
| |---|---| |
| | Attribute types | musician, instrument, genre, melody | |
| | Attributes | 133 (50 musicians, 25 instruments, 8 genres, 50 melodies) | |
| | Source clips per attribute | 3 or 6 (musician, instrument, genre); 1 (melody); 10 s each | |
| | Generators | AudioLDM2, Mustango, Stable Audio Open | |
| | Fine-tuned models | 648 | |
| | Generated clips | 95,456 | |
| | Distractors | 228,598 ten-second segments from JamendoMaxCaps (embeddings provided) | |
|
|
| This repository contains the data. Fine-tuning, generation and evaluation code is on |
| [GitHub](https://github.com/ZeliuFoo/TrueMuse). |
|
|
| --- |
|
|
| ## Download |
|
|
| The data is split into 8 zip archives (about 150 GB in total). |
|
|
| | Archive | Extracts to | |
| |---|---| |
| | `truemuse_concepts.zip` | `concepts/` (no `data/` prefix: extract with `-d data`) | |
| | `truemuse_generated_audioldm2.zip` | `data/generated/audioldm2/` | |
| | `truemuse_generated_mustango.zip` | `data/generated/mustango/` | |
| | `truemuse_generated_stable_audio_{genre,instrument,melody}.zip` | `data/generated/stable-audio/<task>/` | |
| | `truemuse_generated_stable_audio_musician.zip` | `musician_{3,6}/` (no prefix: extract with `-d data/generated/stable-audio`) | |
| | `truemuse_embeddings_jamendo.zip` | `data/embeddings/jamendo/` | |
|
|
| ```bash |
| hf download AnonymousAuthorsssss/TrueMuse --repo-type dataset --local-dir <dataset_root> |
| cd <dataset_root> |
| unzip -q truemuse_concepts.zip -d data # no data/ prefix |
| unzip -q truemuse_generated_stable_audio_musician.zip -d data/generated/stable-audio # no data/ prefix |
| for f in truemuse_generated_audioldm2.zip truemuse_generated_mustango.zip \ |
| truemuse_generated_stable_audio_{genre,instrument,melody}.zip truemuse_embeddings_jamendo.zip; do |
| unzip -q "$f" |
| done |
| ``` |
|
|
| The instrument source audio is not included; rebuild it from YouTube with |
| [`scripts/download_instruments.py`](https://github.com/ZeliuFoo/TrueMuse/blob/main/scripts/download_instruments.py) |
| from the code repository. |
|
|
| --- |
|
|
| ## Structure |
|
|
| ``` |
| <dataset_root>/data/ |
| ├── concepts/ # source clips used for fine-tuning |
| │ ├── musician_6/ # 50 musicians: composer_XXX/song{1,2}_{1,2,3}.wav |
| │ ├── musician_3/ # same, song1_{1,2,3}.wav only |
| │ ├── genre_3/ genre_6/ # 8 genres: <genre>/clip_0X.wav |
| │ ├── melody_1/ # 50 melodies: melody_XXX/clip_01.wav (+ class_name.json) |
| │ └── metadata_instruments.csv # 25 instruments: YouTube ID and start time (no audio) |
| ├── generated/ # generated audio (the attribution queries) |
| │ ├── audioldm2/ |
| │ ├── mustango/ |
| │ └── stable-audio/ |
| │ └── <task>/<concept>_{ldm2,mustango,sa}_db/ |
| │ ├── contextual/*.wav |
| │ ├── style/*.wav |
| │ └── metadata.csv # prompt_type, prompt_id, prompt, file |
| └── embeddings/jamendo/ # distractor embeddings |
| └── {mert-v0, mert-v0-public, music2vec-v1, dac-16k}/pool.npz |
| ``` |
|
|
| `<task>` is one of `musician_3`, `musician_6`, `instrument_3`, `instrument_6`, `genre_3`, `genre_6`, `melody_1`. |
| The 3-clip sets use the first 3 clips of the corresponding 6-clip sets. |
|
|
| ### Source clips (`data/concepts/`) |
|
|
| - **Musician**: 50 artists from the [Free Music Archive (FMA)](https://github.com/mdeff/fma) small split; two |
| tracks per artist, three consecutive 10 s clips per track, vocals removed with |
| [Demucs](https://github.com/facebookresearch/demucs). |
| - **Genre**: 8 FMA genres (electronic, experimental, folk, hip_hop, jazz, pop, punk, rock); each clip from a different |
| track of the genre, vocals removed with Demucs. |
| - **Melody**: 50 FMA tracks, one 10 s clip each. |
| - **Instrument**: 25 instruments. `metadata_instruments.csv` gives the YouTube ID and `start_sec`; the clips are 6 |
| consecutive 10 s segments starting at `start_sec`, and the 3-clip set uses the first 3. |
|
|
| `concept_licenses.csv` (in the root of this repository) lists, for every source clip, its FMA track ID, artist, title and license (or its YouTube |
| source for instruments). |
|
|
| ### Generated audio (`data/generated/`) |
|
|
| Each fine-tuned model generates 4 waveforms per prompt from two prompt types: **contextual** prompts place the |
| attribute in a richer musical scene, **style** prompts request it in a given genre (15 contextual + 28 style |
| prompts; Stable Audio uses 15 + 8, and 15 + 28 for genre). Clips are about 10 s long (10.24 s for Mustango): |
| 16 kHz mono for AudioLDM2 and Mustango, 44.1 kHz stereo for Stable Audio Open. The `metadata.csv` in each concept folder records the prompt |
| of every clip. |
|
|
| ### Distractor embeddings (`data/embeddings/jamendo/`) |
|
|
| `pool.npz` holds `emb` (228,598 x D, float32, L2-normalised; D = 9,984 for MERT-v0, MERT-v0-public and |
| Music2Vec-v1, 12,288 for DAC-16k) and `files`, the source of each row as `<track>.mp3@<offset>s`, from |
| [JamendoMaxCaps](https://huggingface.co/datasets/amaai-lab/JamendoMaxCaps) (non-overlapping 10 s segments). |
|
|
| --- |
|
|
| ## Evaluation protocol |
|
|
| Attributes are split 70 / 15 / 15 into train / validation / test (seed 42, shared across encoders and generators). |
| For each test query, the gallery holds the source clips of all test attributes of the same task (for example |
| `musician_3`) plus the 228,598 distractors. R@k is the fraction of the query's own source clips ranked in the top k; mAP is also |
| reported. See the code repository for scripts. |
|
|
| --- |
|
|
| ## Licensing |
|
|
| TrueMuse is meant for **non-commercial research**. Following [FMA](https://github.com/mdeff/fma), we do not hold |
| the copyright on the source audio and distribute it under the license chosen by the artist. |
|
|
| | Component | Terms | |
| |---|---| |
| | FMA source clips (musician, genre, melody) | license chosen by the artist of each source track, as in FMA (listed per clip in `concept_licenses.csv`) | |
| | Instrument source clips | not redistributed; only YouTube IDs and timestamps are provided | |
| | Generated audio | released for research; generated with AudioLDM2 ([CC BY-NC-SA 4.0](https://huggingface.co/cvssp/audioldm2)), Mustango ([Apache-2.0](https://huggingface.co/declare-lab/mustango)) and Stable Audio Open 1.0 ([Stability AI Community License](https://huggingface.co/stabilityai/stable-audio-open-1.0)) | |
| | Distractor embeddings | derived from JamendoMaxCaps ([CC BY-SA 3.0](https://huggingface.co/datasets/amaai-lab/JamendoMaxCaps)) with MERT and Music2Vec ([CC BY-NC 4.0](https://huggingface.co/m-a-p/MERT-v0)) and DAC (MIT) | |
| | Metadata and annotations | CC BY 4.0, as FMA metadata | |
|
|
| Using the dataset does not grant any rights to the underlying recordings, compositions or performances. |
|
|
| ## Intended use |
|
|
| The data is intended for research on data attribution and credit assignment in music generation. Please do |
| not use it to imitate or impersonate specific artists, for commercial style cloning, or to train models that |
| reproduce individual artists. |
|
|
| Rights holders who would like an item removed can open a discussion on this page or an issue on |
| [GitHub](https://github.com/ZeliuFoo/TrueMuse/issues). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{yu2026truemuse, |
| title={TrueMuse: A Benchmark for Data Attribution in Text-to-Music Models}, |
| author={Yu, Jiawei and Liu, Jian}, |
| journal={arXiv preprint arXiv:2610.00835}, |
| year={2026} |
| } |
| ``` |
|
|
