---
base_model: facebook/wav2vec2-base
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: pidgin-wav2vec2-base-960h
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# pidgin-wav2vec2-base-960h

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [Nigerian Pidgin](https://huggingface.co/datasets/asr-nigerian-pidgin/nigerian-pidgin-1.0) 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

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3949        | 1.48  | 500   | 3.3325          | 0.9999 |
| 2.4656        | 2.95  | 1000  | 1.4727          | 0.8026 |
| 1.1896        | 4.43  | 1500  | 1.0925          | 0.6252 |
| 0.8558        | 5.91  | 2000  | 0.9467          | 0.5422 |
| 0.6427        | 7.39  | 2500  | 0.9856          | 0.5096 |
| 0.5371        | 8.86  | 3000  | 0.9794          | 0.5093 |
| 0.4553        | 10.34 | 3500  | 0.8719          | 0.4641 |
| 0.3921        | 11.82 | 4000  | 0.9344          | 0.4566 |
| 0.3406        | 13.29 | 4500  | 1.0211          | 0.4550 |
| 0.3046        | 14.77 | 5000  | 0.8668          | 0.4423 |
| 0.2651        | 16.25 | 5500  | 1.0384          | 0.4261 |
| 0.244         | 17.73 | 6000  | 1.0437          | 0.4296 |
| 0.2203        | 19.2  | 6500  | 0.9244          | 0.4228 |
| 0.1995        | 20.68 | 7000  | 0.9832          | 0.4165 |
| 0.1838        | 22.16 | 7500  | 1.1455          | 0.4112 |
| 0.1632        | 23.63 | 8000  | 1.1102          | 0.4102 |
| 0.1576        | 25.11 | 8500  | 1.0769          | 0.4044 |
| 0.1388        | 26.59 | 9000  | 1.1008          | 0.4013 |
| 0.1346        | 28.06 | 9500  | 1.0940          | 0.4000 |
| 0.1204        | 29.54 | 10000 | 1.0898          | 0.3966 |


### 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}, 
}