---
base_model: openai/whisper-large-v3
datasets:
- google/fleurs
language:
- pt
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Large V3 pt Fleurs Aug - Chee Li
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Google Fleurs
      type: google/fleurs
      config: pt_br
      split: None
      args: 'config: pt split: test'
    metrics:
    - type: wer
      value: 418.6592073715387
      name: Wer
---

<!-- 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 Large V3 pt Fleurs Aug - Chee Li

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1648
- Wer: 418.6592

## 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0298        | 1.2579 | 1000 | 0.1279          | 73.4662  |
| 0.0053        | 2.5157 | 2000 | 0.1516          | 315.7726 |
| 0.0058        | 3.7736 | 3000 | 0.1560          | 433.2424 |
| 0.0005        | 5.0314 | 4000 | 0.1648          | 418.6592 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1