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 246, 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: Dataset
This repository contains the benchmark data for TRUEMUSE. The accompanying code is available at truemuse-code.
Download
The dataset is distributed as 8 zip archives. Download all of them and extract into the same root directory.
| Archive | Contents |
|---|---|
truemuse_concepts.zip |
data/concepts/ |
truemuse_generated_audioldm2.zip |
data/generated/audioldm2/ |
truemuse_generated_mustango.zip |
data/generated/mustango/ |
truemuse_generated_stable_audio_genre.zip |
data/generated/stable-audio/genre_3, genre_6 |
truemuse_generated_stable_audio_instrument.zip |
data/generated/stable-audio/instrument_3, instrument_6 |
truemuse_generated_stable_audio_melody.zip |
data/generated/stable-audio/melody_1 |
truemuse_generated_stable_audio_musician.zip |
data/generated/stable-audio/musician_3, musician_6 |
truemuse_embeddings_jamendo.zip |
data/embeddings/jamendo/ |
After downloading, extract all archives into the same directory:
for f in truemuse_*.zip; do unzip -q "$f"; done
This will reconstruct the full data/ directory structure described below.
Dataset Structure
truemuse-data/
└── data/
├── concepts/ # Ground-truth reference clips per attribute
│ ├── musician_3/ # 50 musician concepts, 3 clips each
│ ├── musician_6/ # 50 musician concepts, 6 clips each
│ ├── genre_3/ # 8 genre concepts, 3 clips each
│ ├── genre_6/ # 8 genre concepts, 6 clips each
│ ├── melody_1/ # 50 melody concepts, 1 clip each
│ └── metadata_instruments.csv # Instrument concepts (YouTube source + timestamps)
├── generated/ # Generated audio per generator × task
│ ├── audioldm2/
│ │ ├── musician_3/
│ │ ├── musician_6/
│ │ ├── instrument_3/
│ │ ├── instrument_6/
│ │ ├── genre_3/
│ │ ├── genre_6/
│ │ └── melody_1/
│ ├── mustango/ # (same structure)
│ └── stable-audio/ # (same structure)
└── embeddings/
└── jamendo/ # Pre-extracted Jamendo distractor embeddings
├── mert-v0/
├── mert-v0-public/
├── music2vec-v1/
└── dac-16k/
Concept Clips (data/concepts/)
Ground-truth reference audio clips for each attribute type:
- Musician (
musician_3/,musician_6/): 50 artists from the Free Music Archive (FMA), with vocals separated via Demucs. Each artist folder contains 3 or 6 non-overlapping 10-second clips. - Genre (
genre_3/,genre_6/): 8 genres from FMA (electronic, experimental, folk, hip-hop, jazz, pop, punk, rock), 3 or 6 clips each. - Melody (
melody_1/): 50 melody concepts from FMA, 1 clip each. - Instrument (
metadata_instruments.csv): 25 instruments sourced from YouTube. The CSV provides the YouTube ID andstart_secfor each instrument. Clips are 6 consecutive 10-second segments beginning atstart_sec(i.e.,[start_sec, start_sec+10, ..., start_sec+50]). Use the first 3 segments for the 3-clip configuration.
Generated Audio (data/generated/)
4 waveforms per attribute per prompt type (contextual and style) from each of the three finetuned generators. Total: 95,456 clips across 3 generators × 4 task types.
Jamendo Distractor Embeddings (data/embeddings/jamendo/)
Pre-extracted embeddings for 228,598 10s clips from the JamendoMaxCaps dataset, used as distractors in the retrieval evaluation. Embeddings are provided for all four encoders: MERT-v0, MERT-v0-public, Music2Vec-v1, and DAC-16k.
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