---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- asierhv/composite_corpus_eu_v2.1
metrics:
- wer
model-index:
- name: Whisper Small Basque
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Mozilla Common Voice 18.0
      type: mozilla-foundation/common_voice_18_0
    metrics:
    - name: Wer
      type: wer
      value: 7.63
language:
- eu
---

# Whisper Small Basque

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) specifically for Basque (eu) language Automatic Speech Recognition (ASR). It was trained on the [asierhv/composite_corpus_eu_v2.1](https://huggingface.co./datasets/asierhv/composite_corpus_eu_v2.1) dataset, which is a composite corpus designed to improve Basque ASR performance.

**Key improvements and results compared to the base model:**

* **Significant WER reduction:** The fine-tuned model achieves a Word Error Rate (WER) of 9.5479 on the validation set of the `asierhv/composite_corpus_eu_v2.1` dataset, demonstrating improved accuracy compared to the base `whisper-small` model for Basque.
* **Performance on Common Voice:** When evaluated on the Mozilla Common Voice 18.0 dataset, the model achieved a WER of 7.63. This demonstrates the model's ability to generalize to other Basque speech datasets, and highlights the improved accuracy due to the larger model size.

## Model description

This model leverages the `whisper-small` architecture, which offers a balance between accuracy and computational efficiency. By fine-tuning it on a dedicated Basque speech corpus, the model specializes in accurately transcribing Basque speech. This model has a larger capacity than `whisper-base`, improving accuracy at the cost of increased computational resources.

## Intended uses & limitations

**Intended uses:**

* High-accuracy automatic transcription of Basque speech for professional applications.
* Development of advanced Basque speech-based applications that require high precision.
* Research in Basque speech processing where the highest possible accuracy is needed.
* Professional transcription services and applications requiring very high accuracy.
* Use in scenarios where a higher computational cost is justified by the significant improvement in accuracy.

**Limitations:**

* Performance is still influenced by audio quality, with challenges arising from background noise and poor recording conditions.
* Accuracy may be affected by highly dialectal or informal Basque speech.
* Despite improved performance, the model may still produce errors, particularly with complex linguistic structures or rare words.
* The small model is larger than both the base and tiny models, so inference will be slower and require more resources.

## Training and evaluation data

* **Training dataset:** [asierhv/composite_corpus_eu_v2.1](https://huggingface.co./datasets/asierhv/composite_corpus_eu_v2.1). This dataset is a comprehensive collection of Basque speech data, tailored to enhance the performance of Basque ASR systems.
* **Evaluation Dataset:** The `test` split of `asierhv/composite_corpus_eu_v2.1`.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

* **learning_rate:** 1.25e-05
* **train_batch_size:** 32
* **eval_batch_size:** 16
* **seed:** 42
* **optimizer:** AdamW 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      |
|---------------|-------|-------|-----------------|----------|
| 0.3863        | 0.1   | 1000  | 0.4090          | 21.2189  |
| 0.1897        | 0.2   | 2000  | 0.3457          | 15.4490  |
| 0.1379        | 0.3   | 3000  | 0.3283          | 13.5756  |
| 0.1825        | 0.4   | 4000  | 0.3024          | 12.3954  |
| 0.0775        | 0.5   | 5000  | 0.3198          | 11.8771  |
| 0.0975        | 0.6   | 6000  | 0.2924          | 11.2589  |
| 0.1132        | 0.7   | 7000  | 0.2969          | 10.8468  |
| 0.0852        | 0.8   | 8000  | 0.2237          | 9.7727   |
| 0.0585        | 0.9   | 9000  | 0.2317          | 9.6291   |
| 0.0654        | 1.0   | 10000 | 0.2353          | 9.5479   |

### Framework versions

* Transformers 4.49.0.dev0
* Pytorch 2.6.0+cu124
* Datasets 3.3.1.dev0
* Tokenizers 0.21.0