pidgin-wav2vec2-base-100H

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pidgin-wav2vec2-base-960h

This model is a fine-tuned version of facebook/wav2vec2-base on the Nigerian Pidgin dataset. It achieves the following results on the evaluation set:

• Loss: 1.0898
• Wer: 0.3966

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 3407
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.37.2
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.15.2

Citation

@misc{rufai2025endtoendtrainingautomaticspeech, title={Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin}, author={Amina Mardiyyah Rufai and Afolabi Abeeb and Esther Oduntan and Tayo Arulogun and Oluwabukola Adegboro and Daniel Ajisafe}, year={2025}, eprint={2010.11123}, archivePrefix={arXiv}, primaryClass={eess.AS}, url={https://arxiv.org/abs/2010.11123}, }