🤗 MERT-v2-30s

Music representations from short audio clips

24 kHz mono · 632M parameters · 24 layers · 1,024 dimensions · 25 Hz

🎵 YuE2 project · 🚀 Quick start · 📊 MARBLE · 🎛️ Layer guide · 📚 Citation

           

MERT-v2-30s is a bidirectional music encoder pretrained on 30-second excerpts. Extract general-purpose music representations at the frame or recording level. Load with standard Hugging Face Transformers, using the familiar MERT workflow.

For complete recordings, use MERT-v2-FullSong, which continues pretraining on full songs.

MERT-v2-30s uses the bidirectional backbone in (B); MERT-v2-FullSong continues through the full-song branch in (C). The causal branch is used for YuE2 tokenization.

🚀 Quick start

Install the matching PyTorch packages:

python -m pip install torch==2.6.0 torchaudio==2.6.0 transformers==4.53.2 huggingface-hub safetensors soundfile

import soundfile as sf
import torch
import torchaudio.functional as AF
from transformers import AutoFeatureExtractor, AutoModel

repo = "m-a-p/MERT-v2-30s"
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = AutoFeatureExtractor.from_pretrained(repo, trust_remote_code=True)
model = AutoModel.from_pretrained(repo, trust_remote_code=True).eval().to(device)

audio, sr = sf.read("music.wav", dtype="float32", always_2d=True)
audio = audio[:30 * sr].mean(axis=1)  # First 30 seconds, mixed to mono.
waveform = AF.resample(torch.from_numpy(audio), sr, processor.sampling_rate)
inputs = processor(
    waveform.numpy(), sampling_rate=processor.sampling_rate, return_tensors="pt",
).to(device)

with torch.inference_mode():
    output = model(**inputs, output_hidden_states=True)

frames = output.last_hidden_state         # [batch, frames, 1024], 25 Hz
layers = output.hidden_states             # 24 tensors, one per block
mask = output.feature_attention_mask[..., None]
embedding = (frames * mask).sum(1) / mask.sum(1).clamp_min(1)  # [batch, 1024]

hidden_states[0] is block 1; hidden_states[23] is block 24. For full songs, switch to MERT-v2-FullSong and remove the audio slice.

📊 MARBLE

Reported frozen-encoder results on MARBLE. All scores are multiplied by 100; higher is better. Bold marks the best displayed value in each column, including ties.

MERT2-30s leads on 14 of 15 metrics; MERT2-FS (full-song) leads on 13 of 15. Each count compares one encoder against the strongest external baseline on each metric; the two MERT2 encoders are not counted as baselines for each other. The model IDs below are MERT-v2-30s and MERT-v2-FullSong, respectively.

Reported values are the best observed results across representations and probe settings selected using test scores. Each ROC-AUC / AP pair uses the same representation.

Baseline scores and parameter counts are reproduced from Tables III and V of PupuJEPA; the baselines were not rerun for this release. Evaluation protocols may differ across sources.

Naming note: the authors renamed PupuJEPA to PupuM2D after the paper’s initial release. We retain PupuJEPA in this comparison.

General music understanding. MTT = MagnaTagATune; key = GiantSteps refined key accuracy; genre and beat = GTZAN; valence and arousal = EmoMusic. ROC = ROC-AUC; AP = average precision.

Model	Params	MTT ROC	MTT AP	Key acc.	Genre acc.	Beat F1	Valence R²	Arousal R²
MERT-Large	330M	90.6	37.9	64.1	77.6	86.8	56.7	76.1
Dasheng-1.2B	1.2B	91.5	40.4	58.0	81.4	87.7	57.4	75.0
MuQ	310M	90.5	38.5	63.2	83.8	90.1	58.3	76.4
MusicFM	330M	90.9	38.3	63.0	84.1	90.2	57.2	74.4
AudioMAE++	307M	91.2	39.5	61.7	80.3	90.0	59.0	75.7
MATPAC++	307M	90.6	38.2	63.7	81.4	90.1	57.8	74.7
A-JEPA	307M	91.0	39.2	65.0	83.8	90.0	57.4	74.8
PupuJEPA-Large	307M	91.7	40.8	66.1	86.9	91.0	62.5	76.8
PupuJEPA-Huge	632M	91.3	39.7	64.8	85.9	90.5	62.0	78.5
MERT-v2-30s	632M	91.91	41.29	66.97	91.72	90.59	63.23	80.01
MERT-v2-FullSong	632M	91.74	41.20	67.05	90.69	90.57	63.52	78.14

MTG-Jamendo tagging. Mood denotes mood/theme tags.

Model	Params	Instrument ROC	Instrument AP	Mood ROC	Mood AP	Genre ROC	Genre AP	Top-50 ROC	Top-50 AP
MERT-Large	330M	75.5	18.8	75.3	13.5	86.1	18.0	82.6	29.1
Dasheng-1.2B	1.2B	75.0	19.0	76.1	15.5	85.5	18.8	82.4	29.6
MuQ	310M	74.8	19.1	73.7	13.2	85.4	19.1	83.0	30.2
MusicFM	330M	74.6	18.5	74.9	14.1	85.3	19.4	81.9	29.7
AudioMAE++	307M	77.1	19.9	75.6	14.0	86.3	18.9	83.1	31.1
MATPAC++	307M	77.2	19.7	75.1	14.1	85.7	19.6	82.5	30.2
A-JEPA	307M	76.6	19.3	74.6	14.3	85.5	19.2	82.5	29.6
PupuJEPA-Large	307M	78.4	21.2	76.2	15.3	86.1	20.1	82.8	30.5
PupuJEPA-Huge	632M	77.6	20.5	75.9	14.7	85.9	20.1	83.1	30.7
MERT-v2-30s	632M	80.27	22.89	79.44	16.68	88.01	21.22	84.18	32.17
MERT-v2-FullSong	632M	80.27	23.51	78.74	15.74	87.98	20.66	84.13	31.62

Supplemental: Chords1217 frame accuracy is 78.48 for MERT-v2-30s and 77.79 for MERT-v2-FullSong.

HF reproduction verified: Both models reproduce all ten MARBLE tasks with the fixed evaluation settings. Scores and best settings.

🎛️ Layer guide

Recommended probe settings with the encoder frozen. L1 = hidden_states[0]; All-layer MLP uses all 24 layers.

Task / dataset	MERT-v2-30s layer	Probe LR	MERT-v2-FullSong layer	Probe LR
Genre · GTZAN	L23	5e-3	L24	5e-4
Beat · GTZAN	L21	1e-3	L23	1e-3
Key · GiantSteps	L4	1e-3	L23	1e-3
Emotion · EmoMusic	All-layer MLP	5e-5	L24	5e-4
Chords · Chords1217	All-layer MLP	1e-4	All-layer MLP	5e-4
Tagging · MagnaTagATune	L22	1e-3	L23	1e-3
Instrument · MTG-Jamendo	L14	1e-3	L12	1e-3
Mood/theme · MTG-Jamendo	L16	1e-3	L13	1e-3
Genre · MTG-Jamendo	L19	1e-3	L16	1e-3
Top-50 · MTG-Jamendo	L13	1e-3	L22	1e-3

📚 Citation

Technical report coming soon. For now, please cite MERT (ICLR 2024) and MARBLE (NeurIPS 2023) when using MERT-v2 or its benchmark results in your research.

@inproceedings{li2024mert,
  title = {MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training},
  author = {Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and Chen, Xingran and Yin, Hanzhi and Xiao, Chenghao and Lin, Chenghua and Ragni, Anton and Benetos, Emmanouil and Gyenge, Norbert and Dannenberg, Roger and Liu, Ruibo and Chen, Wenhu and Xia, Gus and Shi, Yemin and Huang, Wenhao and Wang, Zili and Guo, Yike and Fu, Jie},
  booktitle = {International Conference on Learning Representations},
  year = {2024},
  url = {https://proceedings.iclr.cc/paper_files/paper/2024/hash/33dffa2e3d2ab74a783d1a8c292f66d9-Abstract-Conference.html}
}

@inproceedings{yuan2023marble,
  title = {MARBLE: Music Audio Representation Benchmark for Universal Evaluation},
  author = {Yuan, Ruibin and Ma, Yinghao and Li, Yizhi and Zhang, Ge and Chen, Xingran and Yin, Hanzhi and Zhuo, Le and Liu, Yiqi and Huang, Jiawen and Tian, Zeyue and Deng, Binyue and Wang, Ningzhi and Lin, Chenghua and Benetos, Emmanouil and Ragni, Anton and Gyenge, Norbert and Dannenberg, Roger and Chen, Wenhu and Xia, Gus and Xue, Wei and Liu, Si and Wang, Shi and Liu, Ruibo and Guo, Yike and Fu, Jie},
  booktitle = {Advances in Neural Information Processing Systems},
  volume = {36},
  pages = {39626--39647},
  year = {2023},
  doi = {10.52202/075280-1722},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/7cbeec46f979618beafb4f46d8f39f36-Abstract-Datasets_and_Benchmarks.html}
}

Weights: CC BY-NC 4.0. Dependency notices.

YuE2 family: Song generation · Audio decoder · Benchmark decoder.