metadata

library_name: transformers
language:
  - es
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
model-index:
  - name: Whisper Large V3 Turbo - Spanish
    results: []

Whisper Large V3 Turbo - Spanish

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Common Voice 17.0 dataset.

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): 10.18%
WER for whisper-large-v3-turbo-es (fine-tuned): 2.69%

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