---
language:
- yo
license: apache-2.0
base_model: DereAbdulhameed/new_whisper_yoruba
tags:
- whisper-event
- generated_from_trainer
datasets:
- OpenSLR
metrics:
- wer
model-index:
- name: Whisper Small Yoruba - Dere Abdulhameed
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1 & SLR86
      type: OpenSLR
      config: yo
      split: None
      args: yo
    metrics:
    - name: Wer
      type: wer
      value: 33.08135740700457
---

<!-- 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. -->

# Whisper Small Yoruba - Dere Abdulhameed

This model is a fine-tuned version of [DereAbdulhameed/new_whisper_yoruba](https://huggingface.co/DereAbdulhameed/new_whisper_yoruba) on the Common Voice 16.1 & SLR86 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7726
- Wer: 33.0814

## 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0001        | 17.01 | 1000 | 1.6166          | 33.3043 |
| 0.0001        | 35.01 | 2000 | 1.7029          | 32.9563 |
| 0.0           | 53.01 | 3000 | 1.7515          | 33.0868 |
| 0.0           | 71.01 | 4000 | 1.7726          | 33.0814 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2