TrueMuse: A Benchmark for Data Attribution in Text-to-Music Models
Paper • 2610.00835 • Published
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 250, in _split_generators
raise ValueError(
"`file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files"
)
ValueError: `file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
TrueMuse: A Benchmark for Data Attribution in Text-to-Music Models
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.
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/ |
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
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) small split; two tracks per artist, three consecutive 10 s clips per track, vocals removed with 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.csvgives the YouTube ID andstart_sec; the clips are 6 consecutive 10 s segments starting atstart_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 (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, 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), Mustango (Apache-2.0) and Stable Audio Open 1.0 (Stability AI Community License) |
| Distractor embeddings | derived from JamendoMaxCaps (CC BY-SA 3.0) with MERT and Music2Vec (CC BY-NC 4.0) 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.
Citation
@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}
}
- Downloads last month
- 63