---
language:
- es
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper small es - m1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: google/fleurs
      config: es_419
      split: None
      args: 'config: es_419, split: test, train'
    metrics:
    - name: Wer
      type: wer
      value: 7.583182873355687
---

<!-- 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 es - m1

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2369
- Wer: 7.5832

## 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: 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_steps: 500
- training_steps: 2500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.6526        | 2.8571  | 500  | 0.1860          | 7.4972 |
| 0.321         | 5.7143  | 1000 | 0.2052          | 7.2866 |
| 0.0887        | 8.5714  | 1500 | 0.2237          | 7.3639 |
| 0.0429        | 11.4286 | 2000 | 0.2327          | 7.5144 |
| 0.0285        | 14.2857 | 2500 | 0.2369          | 7.5832 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1