whisper-medium-eu

Whisper Medium Basque

This model is a fine-tuned version of openai/whisper-medium specifically for Basque (eu) language Automatic Speech Recognition (ASR). It was trained on the 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. 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

Framework versions

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