---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- asierhv/composite_corpus_eu_v2.1
metrics:
- wer
model-index:
- name: Whisper Medium 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: 7.14
language:
- eu
---

# Whisper Medium Basque

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) 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 9.1045 on the validation set of the `asierhv/composite_corpus_eu_v2.1` dataset, demonstrating improved accuracy compared to the base `whisper-medium` model for Basque.
* **Performance on Common Voice:** When evaluated on the Mozilla Common Voice 18.0 dataset, the model achieved a WER of 7.14. This indicates strong generalization capabilities and highlights the benefits of the medium-sized model for enhanced accuracy.

## Model description

This model utilizes the `whisper-medium` architecture, which offers a substantial increase in capacity compared to smaller variants, leading to improved accuracy. By fine-tuning this model on a dedicated Basque speech corpus, it specializes in accurately transcribing Basque speech. The `whisper-medium` model strikes a balance between high accuracy and manageable computational requirements.

## Intended uses & limitations

**Intended uses:**

* High-precision automatic transcription of Basque speech for professional and research applications.
* Development of advanced Basque speech-based applications requiring very high accuracy.
* Research in Basque speech processing where the highest possible accuracy is crucial.
* Professional transcription services and applications where accuracy is paramount.
* Use in scenarios where the computational cost is acceptable for the significant improvement in accuracy.

**Limitations:**

* Performance remains 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 medium model is larger than the small, 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 meticulously curated collection of Basque speech data, designed to maximize 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:** 6.25e-06
* **train_batch_size:** 16
* **eval_batch_size:** 8
* **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      |
|---------------|-------|-------|-----------------|----------|
| 0.3412        | 0.05  | 500   | 0.5112          | 28.8685  |
| 0.1464        | 0.1   | 1000  | 0.4178          | 20.5570  |
| 0.2504        | 0.15  | 1500  | 0.3625          | 18.1279  |
| 0.2615        | 0.2   | 2000  | 0.3236          | 15.5364  |
| 0.1648        | 0.25  | 2500  | 0.3209          | 13.8129  |
| 0.0933        | 0.3   | 3000  | 0.2991          | 12.8887  |
| 0.1016        | 0.35  | 3500  | 0.2823          | 12.4329  |
| 0.1449        | 0.4   | 4000  | 0.2741          | 11.7460  |
| 0.151         | 0.45  | 4500  | 0.2791          | 11.5774  |
| 0.0917        | 0.5   | 5000  | 0.2744          | 11.2402  |
| 0.0913        | 0.55  | 5500  | 0.2901          | 11.1340  |
| 0.1085        | 0.6   | 6000  | 0.2663          | 10.3285  |
| 0.0928        | 0.65  | 6500  | 0.2705          | 10.2910  |
| 0.0725        | 0.7   | 7000  | 0.2506          | 10.3035  |
| 0.1216        | 0.75  | 7500  | 0.2758          | 9.7103   |
| 0.131         | 0.8   | 8000  | 0.2519          | 9.4292   |
| 0.0525        | 0.85  | 8500  | 0.2602          | 9.3106   |
| 0.0729        | 0.9   | 9000  | 0.2549          | 9.3606   |
| 0.0939        | 0.95  | 9500  | 0.2470          | 9.1920   |
| 0.0639        | 1.0   | 10000 | 0.2488          | 9.1045   |

### Framework versions

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