---
base_model: openai/whisper-large-v3
datasets:
- Gabi00/english-mistakes
language:
- eng
library_name: peft
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Small Eng - Gabriel Mora
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: English-mistakes
      type: Gabi00/english-mistakes
      config: default
      split: validation
      args: 'config: eng, split: test'
    metrics:
    - type: wer
      value: 12.985346941102685
      name: Wer
---

<!-- 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: 0.3644
- Wer: 12.9853

## 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: 8
- 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: 50
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.9139        | 0.1270 | 500   | 0.6388          | 24.1376 |
| 0.5572        | 0.2541 | 1000  | 0.4884          | 17.9087 |
| 0.5416        | 0.3811 | 1500  | 0.4371          | 15.2460 |
| 0.5542        | 0.5081 | 2000  | 0.4156          | 13.7921 |
| 0.6599        | 0.6352 | 2500  | 0.4036          | 13.4956 |
| 0.6117        | 0.7622 | 3000  | 0.3960          | 13.2676 |
| 0.5569        | 0.8892 | 3500  | 0.3890          | 13.1336 |
| 0.537         | 1.0163 | 4000  | 0.3850          | 12.5292 |
| 0.4677        | 1.1433 | 4500  | 0.3815          | 12.6261 |
| 0.5017        | 1.2703 | 5000  | 0.3792          | 12.4836 |
| 0.5346        | 1.3974 | 5500  | 0.3761          | 12.3126 |
| 0.4858        | 1.5244 | 6000  | 0.3735          | 12.2926 |
| 0.5478        | 1.6514 | 6500  | 0.3715          | 12.4009 |
| 0.5277        | 1.7785 | 7000  | 0.3699          | 12.2327 |
| 0.5153        | 1.9055 | 7500  | 0.3693          | 12.1643 |
| 0.5825        | 2.0325 | 8000  | 0.3681          | 12.1387 |
| 0.6049        | 2.1596 | 8500  | 0.3670          | 12.3211 |
| 0.5248        | 2.2866 | 9000  | 0.3662          | 12.1501 |
| 0.554         | 2.4136 | 9500  | 0.3653          | 12.0645 |
| 0.5031        | 2.5407 | 10000 | 0.3654          | 12.9312 |
| 0.5253        | 2.6677 | 10500 | 0.3647          | 12.9739 |
| 0.5132        | 2.7947 | 11000 | 0.3641          | 12.9511 |
| 0.5789        | 2.9217 | 11500 | 0.3644          | 12.9853 |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1