| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: openai/whisper-tiny |
| | tags: |
| | - whisper-event |
| | - generated_from_trainer |
| | datasets: |
| | - asierhv/composite_corpus_eu_v2.1 |
| | metrics: |
| | - wer |
| | model-index: |
| | - name: Whisper Tiny 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: 13.56 |
| | language: |
| | - eu |
| | --- |
| | |
| | # Whisper Tiny Basque |
| |
|
| | This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) 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 14.8495 on the validation set of the `asierhv/composite_corpus_eu_v2.1` dataset, demonstrating improved accuracy compared to the base `whisper-tiny` model for Basque. |
| | * **Performance on Common Voice:** When evaluated on the Mozilla Common Voice 18.0 dataset, the model achieved a WER of 13.56. This demonstrates the model's ability to generalize to other Basque speech datasets. |
| |
|
| | ## Model description |
| |
|
| | This model leverages the power of the Whisper architecture, originally developed by OpenAI, and adapts it to the specific nuances of the Basque language. By fine-tuning the `whisper-tiny` model on a comprehensive Basque speech corpus, it learns to accurately transcribe spoken Basque. The `whisper-tiny` model is the smallest of the whisper models, providing a good balance between speed and accuracy. |
| |
|
| | ## Intended uses & limitations |
| |
|
| | **Intended uses:** |
| |
|
| | * Automatic transcription of Basque speech. |
| | * Development of Basque speech-based applications. |
| | * Research on Basque speech processing. |
| | * Accessibility tools for Basque speakers. |
| |
|
| | **Limitations:** |
| |
|
| | * Performance may vary depending on the quality of the audio input (e.g., background noise, recording quality). |
| | * The model might struggle with highly dialectal or informal speech. |
| | * While the model shows improved performance, it may still produce errors, especially with complex sentences or uncommon words. |
| | * The model is based on the small version of whisper, and thus, accuracy may be improved with larger models. |
| |
|
| | ## 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 composite corpus of Basque speech data, designed to improve the performance of Basque ASR systems. |
| | * **Evaluation Dataset:** The `test` portion of `asierhv/composite_corpus_eu_v2.1`. |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| |
|
| | * **learning_rate:** 3.75e-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:** 1000 |
| | * **training_steps:** 10000 |
| | * **mixed_precision_training:** Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | WER | |
| | |---------------|-------|-------|-----------------|----------| |
| | | 0.586 | 0.1 | 1000 | 0.6249 | 34.1639 | |
| | | 0.3145 | 0.2 | 2000 | 0.5048 | 25.2591 | |
| | | 0.225 | 0.3 | 3000 | 0.4839 | 22.0557 | |
| | | 0.3003 | 0.4 | 4000 | 0.4540 | 20.3072 | |
| | | 0.132 | 0.5 | 5000 | 0.4574 | 19.0146 | |
| | | 0.1588 | 0.6 | 6000 | 0.4380 | 17.8219 | |
| | | 0.1841 | 0.7 | 7000 | 0.4395 | 16.6667 | |
| | | 0.143 | 0.8 | 8000 | 0.3719 | 15.4490 | |
| | | 0.0967 | 0.9 | 9000 | 0.3685 | 15.1368 | |
| | | 0.1059 | 1.0 | 10000 | 0.3719 | 14.8495 | |
| | |
| | ### Framework versions |
| | |
| | * Transformers 4.49.0.dev0 |
| | * Pytorch 2.6.0+cu124 |
| | * Datasets 3.3.1.dev0 |
| | * Tokenizers 0.21.0 |
