---
library_name: transformers
language:
- eu
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_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_17_0 eu
      type: mozilla-foundation/common_voice_17_0
      config: eu
      split: test
      args: eu
    metrics:
    - name: Wer
      type: wer
      value: 8.8020814247499
---

<!-- 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_17_0 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1787
- Wer: 8.8021

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3171        | 0.0625 | 500  | 0.3369          | 25.5304 |
| 0.1852        | 0.125  | 1000 | 0.2409          | 17.3110 |
| 0.2353        | 0.1875 | 1500 | 0.2050          | 14.2228 |
| 0.1569        | 1.037  | 2000 | 0.1815          | 12.2861 |
| 0.125         | 1.0995 | 2500 | 0.1692          | 11.1144 |
| 0.12          | 1.162  | 3000 | 0.1600          | 10.6975 |
| 0.069         | 2.0115 | 3500 | 0.1540          | 9.7649  |
| 0.0606        | 2.074  | 4000 | 0.1550          | 9.8199  |
| 0.0434        | 2.1365 | 4500 | 0.1580          | 9.4571  |
| 0.0455        | 2.199  | 5000 | 0.1533          | 9.1410  |
| 0.0216        | 3.0485 | 5500 | 0.1620          | 9.0842  |
| 0.017         | 3.111  | 6000 | 0.1704          | 9.0980  |
| 0.0174        | 3.1735 | 6500 | 0.1681          | 9.0723  |
| 0.0098        | 4.023  | 7000 | 0.1725          | 8.8625  |
| 0.0076        | 4.0855 | 7500 | 0.1765          | 8.8351  |
| 0.007         | 4.148  | 8000 | 0.1787          | 8.8021  |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.2.dev0
- Tokenizers 0.20.0