---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- fsicoli/cv19-fleurs
metrics:
- wer
model-index:
- name: whisper-large-v3-pt-cv19-fleurs
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fsicoli/cv19-fleurs default
      type: fsicoli/cv19-fleurs
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.0756
---

<!-- 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-cv19-fleurs

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the fsicoli/cv19-fleurs default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1823
- Wer: 0.0756

## 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: 6.25e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- training_steps: 50000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 0.0559        | 2.2883  | 5000  | 0.1096          | 0.0730 |
| 0.0581        | 4.5767  | 10000 | 0.1326          | 0.0829 |
| 0.0225        | 6.8650  | 15000 | 0.1570          | 0.0849 |
| 0.0088        | 9.1533  | 20000 | 0.1704          | 0.0840 |
| 0.0065        | 11.4416 | 25000 | 0.1823          | 0.0849 |
| 0.006         | 13.7300 | 30000 | 0.1808          | 0.0809 |
| 0.0055        | 16.0183 | 35000 | 0.1811          | 0.0790 |
| 0.0031        | 18.3066 | 40000 | 0.1907          | 0.0784 |
| 0.0011        | 20.5950 | 45000 | 0.1852          | 0.0771 |
| 0.0003        | 22.8833 | 50000 | 0.1848          | 0.0756 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1
- Datasets 2.21.0
- Tokenizers 0.19.1