---
library_name: transformers
language:
- eu
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Base 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: 13.316721023122872
---

<!-- 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 Base Basque

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_17_0 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4594
- Wer: 13.3167

## 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: 3.75e-05
- train_batch_size: 128
- eval_batch_size: 64
- 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: 40000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch    | Step  | Validation Loss | Wer     |
|:-------------:|:--------:|:-----:|:---------------:|:-------:|
| 0.0638        | 4.6948   | 1000  | 0.2553          | 17.4365 |
| 0.0058        | 9.3897   | 2000  | 0.2922          | 16.3024 |
| 0.0063        | 14.0845  | 3000  | 0.3181          | 16.5021 |
| 0.0047        | 18.7793  | 4000  | 0.3290          | 16.3152 |
| 0.0038        | 23.4742  | 5000  | 0.3358          | 15.9698 |
| 0.002         | 28.1690  | 6000  | 0.3449          | 15.7408 |
| 0.0028        | 32.8638  | 7000  | 0.3470          | 15.6089 |
| 0.0015        | 37.5587  | 8000  | 0.3582          | 15.5576 |
| 0.0019        | 42.2535  | 9000  | 0.3658          | 15.3157 |
| 0.0013        | 46.9484  | 10000 | 0.3606          | 15.0619 |
| 0.0018        | 51.6432  | 11000 | 0.3786          | 15.2415 |
| 0.0008        | 56.3380  | 12000 | 0.3760          | 15.0867 |
| 0.0016        | 61.0329  | 13000 | 0.3736          | 15.1114 |
| 0.0005        | 65.7277  | 14000 | 0.3750          | 14.5022 |
| 0.0019        | 70.4225  | 15000 | 0.3899          | 15.2433 |
| 0.0005        | 75.1174  | 16000 | 0.3851          | 15.0207 |
| 0.0007        | 79.8122  | 17000 | 0.3922          | 14.8613 |
| 0.0003        | 84.5070  | 18000 | 0.3889          | 14.4628 |
| 0.0           | 89.2019  | 19000 | 0.3941          | 14.0056 |
| 0.0           | 93.8967  | 20000 | 0.4005          | 13.8591 |
| 0.0           | 98.5915  | 21000 | 0.4057          | 13.7097 |
| 0.0           | 103.2864 | 22000 | 0.4110          | 13.6044 |
| 0.0           | 107.9812 | 23000 | 0.4165          | 13.5274 |
| 0.0           | 112.6761 | 24000 | 0.4225          | 13.4871 |
| 0.0           | 117.3709 | 25000 | 0.4290          | 13.4120 |
| 0.0           | 122.0657 | 26000 | 0.4361          | 13.4633 |
| 0.0           | 126.7606 | 27000 | 0.4438          | 13.4358 |
| 0.0           | 131.4554 | 28000 | 0.4515          | 13.3506 |
| 0.0           | 136.1502 | 29000 | 0.4594          | 13.3167 |
| 0.0           | 140.8451 | 30000 | 0.4672          | 13.4386 |
| 0.0           | 145.5399 | 31000 | 0.4750          | 13.4441 |
| 0.0           | 150.2347 | 32000 | 0.4827          | 13.4248 |
| 0.0           | 154.9296 | 33000 | 0.4891          | 13.4294 |
| 0.0           | 159.6244 | 34000 | 0.4952          | 13.4221 |
| 0.0           | 164.3192 | 35000 | 0.5003          | 13.3634 |
| 0.0           | 169.0141 | 36000 | 0.5041          | 13.3561 |
| 0.0           | 173.7089 | 37000 | 0.5069          | 13.3579 |
| 0.0           | 178.4038 | 38000 | 0.5091          | 13.3479 |
| 0.0           | 183.0986 | 39000 | 0.5103          | 13.3396 |


### Framework versions

- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1