---
language:
- pt
license: mit
base_model: RodrigoLimaRFL/distil-whisper-nurc-sp-fine-tuned
tags:
- generated_from_trainer
datasets:
- nilc-nlp/CORAA-MUPE-ASR
metrics:
- wer
model-index:
- name: CORAA-MUPE-ASR distil-whisper fine-tuned
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: CORAA-MUPE-ASR
      type: nilc-nlp/CORAA-MUPE-ASR
      config: default
      split: test
      args: 'split: test'
    metrics:
    - name: Wer
      type: wer
      value: 15.273584751709397
---

<!-- 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. -->

# CORAA-MUPE-ASR distil-whisper fine-tuned

This model is a fine-tuned version of [RodrigoLimaRFL/distil-whisper-nurc-sp-fine-tuned](https://huggingface.co/RodrigoLimaRFL/distil-whisper-nurc-sp-fine-tuned) on the CORAA-MUPE-ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3488
- Wer: 15.2736

## 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4451        | 0.0558 | 1000 | 0.4482          | 18.5598 |
| 0.4006        | 0.1116 | 2000 | 0.4095          | 17.3061 |
| 0.2992        | 0.1674 | 3000 | 0.3848          | 16.5660 |
| 0.2781        | 0.2232 | 4000 | 0.3609          | 15.5857 |
| 0.2839        | 0.2790 | 5000 | 0.3488          | 15.2736 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1