---
language:
- pt
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base 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: 25.436328377504847
---

<!-- 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 Base using Common Voice 16 (pt)

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Mozilla Common Voices - 16.0 - Portuguese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3552
- Wer: 25.4363
- Wer Normalized: 19.4668

## 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: 2e-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: 400
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Wer Normalized |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------------:|
| 0.6085        | 0.19  | 500  | 0.4465          | 32.1833 | 25.3383        |
| 0.4624        | 0.37  | 1000 | 0.4131          | 28.9867 | 22.8488        |
| 0.4375        | 0.56  | 1500 | 0.3936          | 27.8135 | 21.3817        |
| 0.4372        | 0.74  | 2000 | 0.3784          | 27.5695 | 21.7171        |
| 0.4704        | 0.93  | 2500 | 0.3630          | 26.1167 | 20.5133        |
| 0.2013        | 1.11  | 3000 | 0.3600          | 25.5462 | 19.7750        |
| 0.2261        | 1.3   | 3500 | 0.3570          | 25.5010 | 19.5181        |
| 0.2118        | 1.48  | 4000 | 0.3552          | 25.4363 | 19.4668        |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.0