---
license: apache-2.0
base_model: guilhermebastos96/whisper-large-v2-finetuning-2
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-large-v2-finetuning-3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: pt
      split: None
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 7.925445186866588
---

<!-- 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-v2-finetuning-3

This model is a fine-tuned version of [guilhermebastos96/whisper-large-v2-finetuning-2](https://huggingface.co/guilhermebastos96/whisper-large-v2-finetuning-2) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2959
- Wer: 7.9254

## 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: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.0365        | 0.5089 | 1000  | 0.2219          | 12.8233 |
| 0.0154        | 1.0178 | 2000  | 0.2462          | 9.3545  |
| 0.0255        | 1.5267 | 3000  | 0.2492          | 9.2442  |
| 0.0178        | 2.0356 | 4000  | 0.2386          | 9.3401  |
| 0.0121        | 2.5445 | 5000  | 0.2447          | 8.9741  |
| 0.0051        | 3.0534 | 6000  | 0.2619          | 8.8478  |
| 0.0034        | 3.5623 | 7000  | 0.2634          | 8.3427  |
| 0.0014        | 4.0712 | 8000  | 0.2776          | 8.0597  |
| 0.001         | 4.5802 | 9000  | 0.2961          | 8.0022  |
| 0.0006        | 5.0891 | 10000 | 0.2959          | 7.9254  |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1