Whisper Large V3 Turbo Es

🚀 Whisper Large V3 Turbo - Spanish

This model is a fine - tuned version of openai/whisper-large-v3-turbo on the Spanish subset of the Common Voice 17.0 dataset. It significantly improves the transcription accuracy for Spanish audios.

🚀 Quick Start

This section will guide you through the basic steps to use the model. However, specific usage code is not provided in the original README.

✨ Features

• This model is a fine - tuned version of openai/whisper-large-v3-turbo on the Common Voice 17.0 dataset - Spanish subset.
• The fine - tuning process reduced the Word Error Rate (WER) from 6.91% to 5.34%, demonstrating significant improvement in transcription accuracy for Spanish audios.

📦 Installation

No installation steps are provided in the original README, so this section is skipped.

📚 Documentation

Training and evaluation data

The model was trained using the Common Voice 17.0 dataset - Spanish subset (mozilla - foundation/common_voice_17_0). Both the base model, whisper - large - v3 - turbo, and the fine - tuned model, whisper - large - v3 - turbo - es, were evaluated using Word Error Rate (WER) on the test split of the same dataset. The results are as follows:

• WER for whisper - large - v3 - turbo (base): 6.91%
• WER for whisper - large - v3 - turbo - es (fine - tuned): 5.34%

This significant reduction in WER shows that fine - tuning the model for Spanish audio led to improved transcription accuracy compared to the original base model.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e - 05
• train_batch_size: 64
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 5000
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Tokenizers 0.19.1

📄 License

This model is released under the MIT license.