---
language:
- eu
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Base 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: 16.17652806002814
---

<!-- 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_16_1 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5038
- Wer: 16.1765

## 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: 2.5e-05
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0225        | 10.0  | 1000  | 0.3059          | 19.0812 |
| 0.0037        | 20.0  | 2000  | 0.3530          | 18.4618 |
| 0.0012        | 30.0  | 3000  | 0.3724          | 17.9332 |
| 0.0004        | 40.0  | 4000  | 0.4025          | 17.8951 |
| 0.0002        | 50.0  | 5000  | 0.4245          | 17.8951 |
| 0.0001        | 60.0  | 6000  | 0.4459          | 17.9772 |
| 0.0001        | 70.0  | 7000  | 0.4665          | 18.0163 |
| 0.0           | 80.0  | 8000  | 0.4882          | 18.1081 |
| 0.0003        | 90.0  | 9000  | 0.3803          | 16.3807 |
| 0.0001        | 100.0 | 10000 | 0.4047          | 16.2293 |
| 0.0001        | 110.0 | 11000 | 0.4207          | 16.2420 |
| 0.0001        | 120.0 | 12000 | 0.4353          | 16.2879 |
| 0.0           | 130.0 | 13000 | 0.4502          | 16.3700 |
| 0.0           | 140.0 | 14000 | 0.4653          | 16.5087 |
| 0.0           | 150.0 | 15000 | 0.4805          | 16.4393 |
| 0.0           | 160.0 | 16000 | 0.4964          | 16.4941 |
| 0.0           | 170.0 | 17000 | 0.5128          | 16.5107 |
| 0.0           | 180.0 | 18000 | 0.5285          | 16.6377 |
| 0.0           | 190.0 | 19000 | 0.5457          | 16.6572 |
| 0.0102        | 200.0 | 20000 | 0.4229          | 18.1902 |
| 0.0           | 210.0 | 21000 | 0.4498          | 16.2117 |
| 0.0           | 220.0 | 22000 | 0.4646          | 16.2146 |
| 0.0           | 230.0 | 23000 | 0.4754          | 16.1961 |
| 0.0           | 240.0 | 24000 | 0.4853          | 16.1863 |
| 0.0           | 250.0 | 25000 | 0.4946          | 16.1912 |
| 0.0           | 260.0 | 26000 | 0.5038          | 16.1765 |
| 0.0           | 270.0 | 27000 | 0.5133          | 16.2215 |
| 0.0           | 280.0 | 28000 | 0.5228          | 16.2224 |
| 0.0           | 290.0 | 29000 | 0.5326          | 16.2557 |
| 0.0           | 300.0 | 30000 | 0.5427          | 16.2420 |
| 0.0           | 310.0 | 31000 | 0.5525          | 16.2635 |
| 0.0           | 320.0 | 32000 | 0.5624          | 16.2957 |
| 0.0           | 330.0 | 33000 | 0.5706          | 16.3299 |
| 0.0           | 340.0 | 34000 | 0.5798          | 16.3534 |
| 0.0           | 350.0 | 35000 | 0.5880          | 16.3495 |
| 0.0           | 360.0 | 36000 | 0.5948          | 16.3622 |
| 0.0           | 370.0 | 37000 | 0.6005          | 16.3934 |
| 0.0           | 380.0 | 38000 | 0.6045          | 16.3876 |
| 0.0           | 390.0 | 39000 | 0.6074          | 16.4325 |
| 0.0           | 400.0 | 40000 | 0.6085          | 16.4315 |


### Framework versions

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