---
language:
- eu
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Large-V3 Basque
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_13_0 eu
      type: mozilla-foundation/common_voice_13_0
      config: eu
      split: validation
      args: eu
    metrics:
    - name: Wer
      type: wer
      value: 13.28860142255536
---

<!-- 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 Basque

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the mozilla-foundation/common_voice_13_0 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4180
- Wer: 13.2886

## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.1288        | 5.85   | 1000  | 0.2746          | 18.6000 |
| 0.0262        | 11.7   | 2000  | 0.2894          | 16.0934 |
| 0.0095        | 17.54  | 3000  | 0.3281          | 15.7348 |
| 0.0056        | 23.39  | 4000  | 0.3362          | 14.7394 |
| 0.0045        | 29.24  | 5000  | 0.3465          | 14.9912 |
| 0.0032        | 35.09  | 6000  | 0.3599          | 14.7172 |
| 0.002         | 40.94  | 7000  | 0.3624          | 14.4150 |
| 0.0028        | 46.78  | 8000  | 0.3647          | 14.4553 |
| 0.0019        | 52.63  | 9000  | 0.3726          | 14.4210 |
| 0.0011        | 58.48  | 10000 | 0.3784          | 14.1268 |
| 0.0011        | 64.33  | 11000 | 0.3753          | 14.2517 |
| 0.0009        | 70.18  | 12000 | 0.3845          | 13.9193 |
| 0.0008        | 76.02  | 13000 | 0.3910          | 14.0402 |
| 0.0008        | 81.87  | 14000 | 0.3988          | 13.8488 |
| 0.0004        | 87.72  | 15000 | 0.4002          | 13.5788 |
| 0.0002        | 93.57  | 16000 | 0.4021          | 13.5526 |
| 0.0002        | 99.42  | 17000 | 0.4121          | 13.5747 |
| 0.0002        | 105.26 | 18000 | 0.4178          | 13.5989 |
| 0.0005        | 111.11 | 19000 | 0.4135          | 13.3551 |
| 0.0001        | 116.96 | 20000 | 0.4180          | 13.2886 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1