Wolof-HuBERT-CTC

This model is a fine-tuned version of soynade-research/Wolof-HuBERT-Base. It achieves the following results on a challenging evaluation set:

• Loss: 0.4031
• Wer: 0.3565

It outperforms HuBERT models by Meta and Orange.

Usage

import torch
from transformers import pipeline

pipeline = pipeline(
    task="automatic-speech-recognition",
    model="soynade-research/Wolof-HuBERT-CTC",
    dtype=torch.float16,
    device=0
)

pipeline("https://huggingface.co/soynade-research/Wolof-HuBERT-CTC/resolve/main/story.wav")

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10.0

Training results

Framework versions

• Transformers 4.56.0.dev0
• Pytorch 2.8.0+cu128
• Datasets 2.20.0
• Tokenizers 0.21.4

How to Cite

If you use this model, please cite:

@misc{sy2025speechlanguagemodelsunderrepresented,
      title={Speech Language Models for Under-Represented Languages: Insights from Wolof}, 
      author={Yaya Sy and Dioula Doucouré and Christophe Cerisara and Irina Illina},
      year={2025},
      eprint={2509.15362},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.15362}, 
}