---
language:
- eu
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
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_16_1 eu
      type: mozilla-foundation/common_voice_16_1
      config: eu
      split: test
      args: eu
    metrics:
    - name: Wer
      type: wer
      value: 6.887994372362044
---

<!-- 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_16_1 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3688
- Wer: 6.8880

## 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: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 40000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.0095        | 10.04  | 1000  | 0.2023          | 9.6803 |
| 0.0032        | 20.08  | 2000  | 0.2153          | 9.0521 |
| 0.0023        | 30.11  | 3000  | 0.2234          | 8.8645 |
| 0.0023        | 40.15  | 4000  | 0.2278          | 8.4366 |
| 0.0012        | 50.19  | 5000  | 0.2260          | 7.9911 |
| 0.0005        | 60.23  | 6000  | 0.2435          | 7.9060 |
| 0.0013        | 70.26  | 7000  | 0.2254          | 7.8484 |
| 0.0004        | 80.3   | 8000  | 0.2367          | 7.4830 |
| 0.0008        | 90.34  | 9000  | 0.2289          | 7.4420 |
| 0.0007        | 100.38 | 10000 | 0.2385          | 7.5319 |
| 0.001         | 110.41 | 11000 | 0.2293          | 7.6325 |
| 0.0001        | 120.45 | 12000 | 0.2473          | 7.1430 |
| 0.0001        | 130.49 | 13000 | 0.2488          | 7.1870 |
| 0.0004        | 140.53 | 14000 | 0.2398          | 7.1831 |
| 0.0           | 150.56 | 15000 | 0.2620          | 7.0590 |
| 0.0001        | 160.6  | 16000 | 0.2547          | 7.1967 |
| 0.0           | 170.64 | 17000 | 0.2768          | 7.0736 |
| 0.0           | 180.68 | 18000 | 0.2878          | 7.0004 |
| 0.0           | 190.72 | 19000 | 0.2962          | 6.9466 |
| 0.0013        | 200.75 | 20000 | 0.2354          | 7.6042 |
| 0.0           | 210.79 | 21000 | 0.2720          | 6.8948 |
| 0.0           | 220.83 | 22000 | 0.2865          | 6.8987 |
| 0.0           | 230.87 | 23000 | 0.2954          | 6.8890 |
| 0.0           | 240.9  | 24000 | 0.3031          | 6.8821 |
| 0.0           | 250.94 | 25000 | 0.3102          | 6.8772 |
| 0.0           | 260.98 | 26000 | 0.3166          | 6.8899 |
| 0.0           | 271.02 | 27000 | 0.3233          | 6.8919 |
| 0.0           | 281.05 | 28000 | 0.3248          | 6.8919 |
| 0.0           | 291.09 | 29000 | 0.3363          | 6.9026 |
| 0.0           | 301.13 | 30000 | 0.3419          | 6.9085 |
| 0.0           | 311.17 | 31000 | 0.3471          | 6.8851 |
| 0.0           | 321.2  | 32000 | 0.3526          | 6.8704 |
| 0.0           | 331.24 | 33000 | 0.3570          | 6.8831 |
| 0.0           | 341.28 | 34000 | 0.3614          | 6.8851 |
| 0.0           | 351.32 | 35000 | 0.3645          | 6.8782 |
| 0.0           | 361.36 | 36000 | 0.3663          | 6.8714 |
| 0.0           | 371.39 | 37000 | 0.3677          | 6.8675 |
| 0.0           | 381.43 | 38000 | 0.3681          | 6.8802 |
| 0.0           | 391.47 | 39000 | 0.3686          | 6.8880 |
| 0.0           | 401.51 | 40000 | 0.3688          | 6.8880 |


### Framework versions

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