---
library_name: transformers
language:
- spa
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Tiny Few Audios - vfranchis
  results: []
---

<!-- 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 Tiny Few Audios - vfranchis

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Few audios 1.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3835
- Wer: 15.7143

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 1.5625        | 2.8571  | 10   | 1.4533          | 77.1429 |
| 0.6893        | 5.7143  | 20   | 0.7903          | 32.8571 |
| 0.1921        | 8.5714  | 30   | 0.5135          | 34.2857 |
| 0.0623        | 11.4286 | 40   | 0.4158          | 11.4286 |
| 0.0222        | 14.2857 | 50   | 0.3903          | 14.2857 |
| 0.0107        | 17.1429 | 60   | 0.3846          | 14.2857 |
| 0.0069        | 20.0    | 70   | 0.3847          | 15.7143 |
| 0.0055        | 22.8571 | 80   | 0.3842          | 15.7143 |
| 0.0046        | 25.7143 | 90   | 0.3836          | 15.7143 |
| 0.0044        | 28.5714 | 100  | 0.3835          | 15.7143 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1