CultureMERT-TA-95M is a 95M-parameter music foundation model adapted to diverse musical cultures through task arithmetic. Instead of direct continual pre-training on a multi-cultural mixture, as in CultureMERT-95M, this model merges multiple single-culture adapted variants of MERT-v1-95M—each continually pre-trained via our two-stage strategy on a distinct musical tradition:

Dataset	Music Tradition	Hours Used
Lyra	Greek traditional/folk	50h
Turkish-makam	Turkish/Ottoman classical	200h
Hindustani	North Indian classical	200h
Carnatic	South Indian classical	200h

🧪 The final model was merged using a scaling factor of λ = 0.2, which yielded the best overall performance across all task arithmetic variants evaluated.

🔀 This model serves as an alternative to CultureMERT-95M. It merges culturally specialized models in weight space via task arithmetic to form a unified multi-cultural model. Each single-culture adapted model is obtained using the same two-stage continual pre-training strategy as CultureMERT-95M, applied separately to each musical tradition prior to merging.

📊 Evaluation

We follow the same evaluation protocol as CultureMERT-95M and report results in comparison to both it and MERT-v1-95M:

ROC-AUC / mAP

	Turkish-makam	Hindustani	Carnatic	Lyra	FMA-medium	MTAT	Avg.
MERT-v1-95M	83.2% / 53.3%	82.4% / 52.9%	74.9% / 39.7%	85.7% / 56.5%	90.7% / 48.1%	89.6% / 35.9%	66.1%
CultureMERT-95M	89.6% / 60.6%	88.2% / 63.5%	79.2% / 43.1%	86.9% / 56.7%	90.7% / 48.1%	89.4% / 35.9%	69.3%
CultureMERT-TA-95M	89.0% / 61.0%	87.5% / 59.3%	79.1% / 43.3%	87.3% / 57.3%	90.8% / 49.1%	89.6% / 36.4%	69.1%

Micro-F1 / Macro-F1

	Turkish-makam	Hindustani	Carnatic	Lyra	FMA-medium	MTAT	Avg.
MERT-v1-95M	73.0% / 38.9%	71.1% / 33.2%	80.1% / 30.0%	72.4% / 42.6%	57.0% / 36.9%	35.7% / 21.2%	49.3%
CultureMERT-95M	77.4% / 45.8%	77.8% / 50.4%	82.7% / 32.5%	73.1% / 43.1%	58.3% / 36.6%	35.6% / 22.9%	52.9%
CultureMERT-TA-95M	76.9% / 45.4%	74.2% / 45.0%	82.5% / 32.1%	73.0% / 45.3%	59.1% / 38.2%	35.7% / 21.5%	52.4%

📈 CultureMERT-TA-95M performs comparably to CultureMERT-95M on non-Western datasets, while surpassing it on Lyra and Western benchmarks. It also outperforms MERT-v1-95M on Western tasks (MTAT and FMA-medium) by an average margin of +0.7% across all metrics.

🔧 Model Usage

python

from transformers import Wav2Vec2FeatureExtractor, AutoModel
import torch
from torch import nn
import torchaudio.transforms as T
from datasets import load_dataset

# Load model weights and preprocessor config
model = AutoModel.from_pretrained("ntua-slp/CultureMERT-TA-95M", trust_remote_code=True)
processor = Wav2Vec2FeatureExtractor.from_pretrained("ntua-slp/CultureMERT-TA-95M", trust_remote_code=True)

# Load example audio
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation", trust_remote_code=True).sort("id")
audio_array = dataset[0]["audio"]["array"]
sampling_rate = dataset.features["audio"].sampling_rate

# Resample if needed
resample_rate = processor.sampling_rate
if resample_rate != sampling_rate:
    print(f'Setting sample rate from {sampling_rate} to {resample_rate}')
    resampler = T.Resample(sampling_rate, resample_rate)
else:
    resampler = None

# Audio file is decoded on the fly
if resampler is None:
	input_audio = dataset[0]["audio"]["array"]
else:
  input_audio = resampler(torch.from_numpy(dataset[0]["audio"]["array"]).to(dtype=resampler.kernel.dtype))

# Extract hidden states
inputs = processor(input_audio, sampling_rate=resample_rate, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs, output_hidden_states=True)

# Representations: 13 layers (CNN feature extractor + 12 Transformer)
# NOTE: each layer performs differently in different downstream tasks - you should choose empirically
all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
print(all_layer_hidden_states.shape) # [13 layers, Time steps, 768 feature_dim]

# For utterance-level classification tasks, you can simply reduce the representation in time
time_reduced_hidden_states = all_layer_hidden_states.mean(-2)
print(time_reduced_hidden_states.shape) # [13, 768]

# You can even use a learnable weighted average representation over all layers
aggregator = nn.Conv1d(in_channels=13, out_channels=1, kernel_size=1)
weighted_avg_hidden_states = aggregator(time_reduced_hidden_states.unsqueeze(0)).squeeze()
print(weighted_avg_hidden_states.shape) # [768]

Ethical Considerations

This model is released under a non-commercial CC BY-NC 4.0 license and is intended for research purposes. While it is designed to address cultural bias in MIR, its training data and pretraining paradigm may still reflect cultural and dataset-specific biases. The model should not be used in commercial or generative applications without explicit consideration of cultural representation, proper attribution, and consent from relevant communities or dataset curators.

📚 Citation

shell

@inproceedings{kanatas2025culturemert,
  title     = {{CultureMERT}: Continual Pre-Training for Cross-Cultural Music Representation Learning},
  author    = {Kanatas, Angelos-Nikolaos and Papaioannou, Charilaos and Potamianos, Alexandros},
  booktitle = {Proceedings of the 26th International Society for Music Information Retrieval Conference (ISMIR)},
  address   = {Daejeon, South Korea},
  pages     = {569--578},
  year      = {2025},
  doi       = {10.5281/zenodo.17706517},
  url       = {https://arxiv.org/abs/2506.17818}
}