63 lines
2.0 KiB
Markdown
63 lines
2.0 KiB
Markdown
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---
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library_name: transformers
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language:
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- es
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license: mit
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base_model: openai/whisper-large-v3-turbo
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tags:
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- whisper-event
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_17_0
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model-index:
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- name: Whisper Large V3 Turbo - Spanish
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Whisper Large V3 Turbo - Spanish
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 17.0 dataset - spanish subset.
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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.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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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:
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- WER for whisper-large-v3-turbo (base): 6.91%
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- WER for whisper-large-v3-turbo-es (fine-tuned): 5.34%
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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.
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 64
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- training_steps: 5000
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu121
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- Tokenizers 0.19.1
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