---
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: 16.035875888817067
---

<!-- 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: 0.2220
- Wer: 16.0359
- Wer Normalized: 10.3867

## 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: 5e-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------------:|
| 0.2484        | 0.26  | 500  | 0.2712          | 19.2259 | 13.0929        |
| 0.2184        | 0.52  | 1000 | 0.2464          | 17.8895 | 11.9404        |
| 0.236         | 0.77  | 1500 | 0.2339          | 17.1348 | 11.3016        |
| 0.1401        | 1.03  | 2000 | 0.2285          | 16.7001 | 11.0432        |
| 0.1206        | 1.29  | 2500 | 0.2251          | 16.3235 | 10.6467        |
| 0.1199        | 1.55  | 3000 | 0.2236          | 16.1732 | 10.5424        |
| 0.1231        | 1.81  | 3500 | 0.2197          | 16.1587 | 10.5038        |
| 0.0935        | 2.06  | 4000 | 0.2220          | 16.0359 | 10.3867        |


### Framework versions

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