---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-base-yoruba-colab-CV17.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: yo
      split: test
      args: yo
    metrics:
    - name: Wer
      type: wer
      value: 0.7073728329205563
---

<!-- 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-base-yoruba-colab-CV17.0

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0478
- Wer: 0.7074

## 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: 5e-05
- train_batch_size: 16
- 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_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 1.7762        | 1.5385  | 200  | 0.9767          | 0.8377 |
| 0.5374        | 3.0769  | 400  | 0.8458          | 0.7728 |
| 0.2352        | 4.6154  | 600  | 0.8632          | 0.7407 |
| 0.1038        | 6.1538  | 800  | 0.9157          | 0.7078 |
| 0.0426        | 7.6923  | 1000 | 0.9531          | 0.7246 |
| 0.0201        | 9.2308  | 1200 | 0.9950          | 0.7219 |
| 0.0093        | 10.7692 | 1400 | 1.0347          | 0.6989 |
| 0.0037        | 12.3077 | 1600 | 1.0351          | 0.7063 |
| 0.0013        | 13.8462 | 1800 | 1.0429          | 0.7069 |
| 0.0008        | 15.3846 | 2000 | 1.0478          | 0.7074 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1