---
language:
- eng
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- Gabi00/english-mistakes
metrics:
- wer
model-index:
- name: Whisper Small Eng - Gabriel Mora
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: English-mistakes
      type: Gabi00/english-mistakes
      config: default
      split: validation
      args: 'config: eng, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 13.110781686527167
---

<!-- 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 Eng - Gabriel Mora

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the English-mistakes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3784
- Wer: 13.1108

## 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: 28
- eval_batch_size: 28
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.6399        | 0.4444 | 500  | 0.4393          | 13.1649 |
| 0.526         | 0.8889 | 1000 | 0.4070          | 14.4792 |
| 0.4598        | 1.3333 | 1500 | 0.3948          | 14.2654 |
| 0.4094        | 1.7778 | 2000 | 0.3806          | 14.4050 |
| 0.3556        | 2.2222 | 2500 | 0.3816          | 13.2305 |
| 0.3304        | 2.6667 | 3000 | 0.3784          | 13.1108 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1