---
language:
- pt
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper small using Common Voice 16 (pt)
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Mozilla Common Voices - 16.0 - Portuguese
      type: mozilla-foundation/common_voice_16_0
      config: pt
      split: test
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 17.33354880413704
---

<!-- 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 using Common Voice 16 (pt)

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the Mozilla Common Voices - 16.0 - Portuguese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2712
- Wer: 17.3335
- Wer Normalized: 11.3321

## 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     | Wer Normalized |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------------:|
| 0.2591        | 0.37  | 500  | 0.2776          | 20.0727 | 13.6707        |
| 0.2626        | 0.74  | 1000 | 0.2599          | 19.4005 | 13.3337        |
| 0.1131        | 1.11  | 1500 | 0.2516          | 18.0414 | 12.1330        |
| 0.1016        | 1.48  | 2000 | 0.2482          | 18.3597 | 11.9244        |
| 0.1094        | 1.85  | 2500 | 0.2411          | 17.4192 | 11.6017        |
| 0.0524        | 2.22  | 3000 | 0.2512          | 17.3546 | 11.4637        |
| 0.0433        | 2.59  | 3500 | 0.2496          | 17.0895 | 11.2984        |
| 0.0453        | 2.96  | 4000 | 0.2479          | 17.0362 | 11.2679        |
| 0.0201        | 3.33  | 4500 | 0.2693          | 17.6632 | 11.7109        |
| 0.0229        | 3.7   | 5000 | 0.2712          | 17.3335 | 11.3321        |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1