transformers pytorch tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer base_model:openai/whisper-base license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-base-google-fleurs-pt-br
  results: []
---

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# whisper-base-google-fleurs-pt-br

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4987
- Wer: 22.2974
- Wer Normalized: 18.5291

## 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: 2.05e-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: reduce_lr_on_plateau
- lr_scheduler_warmup_steps: 120
- training_steps: 2400
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Wer Normalized |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------------:|
| 0.3716        | 1.01  | 400  | 0.3988          | 21.8039 | 17.8916        |
| 0.2003        | 2.02  | 800  | 0.4440          | 22.3350 | 18.6242        |
| 0.0571        | 3.02  | 1200 | 0.4960          | 22.5982 | 19.2284        |
| 0.03          | 4.03  | 1600 | 0.4987          | 22.2974 | 18.5291        |


### Framework versions

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