---
language:
- pt
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Tiny Portuguese
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_13_0 pt
      type: mozilla-foundation/common_voice_13_0
      config: pt
      split: test
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 28.965267965695134
---

<!-- 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 Portuguese

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co./openai/whisper-tiny) on the mozilla-foundation/common_voice_13_0 pt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5191
- Wer: 28.9653

## 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-06
- train_batch_size: 256
- eval_batch_size: 128
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4763        | 14.08 | 1000 | 0.5686          | 31.3114 |
| 0.3784        | 28.17 | 2000 | 0.5350          | 30.0693 |
| 0.3286        | 42.25 | 3000 | 0.5239          | 29.2413 |
| 0.3073        | 56.34 | 4000 | 0.5200          | 29.4138 |
| 0.2971        | 70.42 | 5000 | 0.5191          | 28.9653 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.15.1