---
language:
- eu
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Medium 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: 14.112716355356747
---

<!-- 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 Medium Basque

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the mozilla-foundation/common_voice_13_0 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3985
- Wer: 14.1127

## 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: 64
- eval_batch_size: 32
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1127        | 5.85  | 1000  | 0.2776          | 17.5623 |
| 0.0225        | 11.7  | 2000  | 0.3129          | 15.6320 |
| 0.0074        | 17.54 | 3000  | 0.3277          | 14.9530 |
| 0.0041        | 23.39 | 4000  | 0.3551          | 14.8018 |
| 0.0032        | 29.24 | 5000  | 0.3698          | 14.6245 |
| 0.0019        | 35.09 | 6000  | 0.3877          | 14.6084 |
| 0.0014        | 40.94 | 7000  | 0.3891          | 14.4976 |
| 0.0008        | 46.78 | 8000  | 0.3946          | 14.2759 |
| 0.0007        | 52.63 | 9000  | 0.3987          | 14.3182 |
| 0.0005        | 58.48 | 10000 | 0.3985          | 14.1127 |


### Framework versions

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