---
library_name: transformers
language:
- ha
license: apache-2.0
base_model: EYEDOL/whisper-tiny-hausa2
tags:
- generated_from_trainer
datasets:
- EYEDOL/naija-voices-hausa-split_0-6
metrics:
- wer
model-index:
- name: EYEDOL/whisper-tiny-hausa3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: EYEDOL/naija-voices-hausa-split_0-6
      type: EYEDOL/naija-voices-hausa-split_0-6
    metrics:
    - name: Wer
      type: wer
      value: 0.43485342019543977
---

<!-- 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. -->

# EYEDOL/whisper-tiny-hausa3

This model is a fine-tuned version of [EYEDOL/whisper-tiny-hausa2](https://huggingface.co/EYEDOL/whisper-tiny-hausa2) on the EYEDOL/naija-voices-hausa-split_0-6 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6238
- Wer Ortho: 0.5015
- Wer: 0.4349

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 1.2512        | 1.0   | 665  | 0.6263          | 0.5023    | 0.4362 |
| 1.1143        | 2.0   | 1330 | 0.6204          | 0.4940    | 0.4280 |
| 1.0009        | 3.0   | 1995 | 0.6238          | 0.5015    | 0.4349 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2