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.
Model description
More information needed
Intended uses & limitations
More information needed
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
