---
license: cc-by-nc-4.0
tags:
- audio
- music
- merge
metrics:
- roc_auc
- average_precision
- f1
model_type: audio-classification
pipeline_tag: audio-classification
library_name: transformers
base_model:
- m-a-p/MERT-v1-95M
---

# CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning
📑 [**Read the full paper (to be presented at ISMIR 2025)**](https://arxiv.org/abs/2506.17818)

**CultureMERT-TA-95M** is a 95M-parameter music foundation model adapted to diverse musical cultures through [**task arithmetic**](https://arxiv.org/abs/2212.04089). Instead of direct continual pre-training on a multi-cultural mixture, as in [CultureMERT-95M](https://huggingface.co/ntua-slp/CultureMERT-95M), this model merges multiple **single-culture adapted** variants of [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M)—each continually pre-trained via our two-stage strategy on a distinct musical tradition:


| Dataset         | Music Tradition             | Hours Used |
|-----------------|-----------------------------|------------|
| [*Lyra*](https://github.com/pxaris/lyra-dataset)            | Greek traditional/folk      | 50h        |
| [*Turkish-makam*](https://dunya.compmusic.upf.edu/makam/)   | Turkish/Ottoman classical   | 200h       |
| [*Hindustani*](https://dunya.compmusic.upf.edu/hindustani/) | North Indian classical      | 200h       |
| [*Carnatic*](https://dunya.compmusic.upf.edu/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**](https://huggingface.co/ntua-slp/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](https://huggingface.co/ntua-slp/CultureMERT-95M) and report results in comparison to both it and [MERT-v1-95M](https://huggingface.co/m-a-p/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
@misc{kanatas2025culturemertcontinualpretrainingcrosscultural,
      title={CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning}, 
      author={Angelos-Nikolaos Kanatas and Charilaos Papaioannou and Alexandros Potamianos},
      year={2025},
      eprint={2506.17818},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2506.17818}, 
}
```

---