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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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model-index: |
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- name: whisper-small-es-ja |
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results: [] |
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datasets: |
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- Marianoleiras/voxpopuli_es-ja |
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language: |
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- es |
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- ja |
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base_model: |
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- openai/whisper-small |
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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-small-es-ja |
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This model is a fine-tuned version of OpenAI's whisper-small on the Marianoleiras/voxpopuli_es-ja dataset, designed for Spanish-to-Japanese and Japanese-to-Spanish speech-to-text (STT) tasks. |
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It leverages OpenAI's Whisper architecture, which is well-suited for multilingual speech recognition and translation tasks. |
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The model achieves robust performance on both the evaluation and test sets, demonstrating its effectiveness in multilingual STT applications. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1724 |
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- Bleu: 22.2850 |
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It achieves the following results on the test set: |
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- Bleu: 21.4557 |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- training_steps: 3500 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Bleu | Validation Loss | |
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|:-------------:|:------:|:----:|:-------:|:---------------:| |
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| 1.5787 | 0.3962 | 250 | 11.6756 | 1.5196 | |
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| 1.3535 | 0.7924 | 500 | 16.0514 | 1.3470 | |
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| 1.0658 | 1.1886 | 750 | 17.7743 | 1.2533 | |
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| 1.0303 | 1.5848 | 1000 | 19.1894 | 1.2046 | |
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| 0.9893 | 1.9810 | 1250 | 20.1198 | 1.1591 | |
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| 0.7569 | 2.3772 | 1500 | 21.0054 | 1.1546 | |
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| 0.7571 | 2.7734 | 1750 | 21.6425 | 1.1378 | |
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| 0.5557 | 3.1696 | 2000 | 21.7563 | 1.1500 | |
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| 0.5612 | 3.5658 | 2250 | 21.1391 | 1.1395 | |
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| 0.5581 | 3.9620 | 2500 | 22.0412 | 1.1343 | |
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| 0.4144 | 4.3582 | 2750 | 22.2850 | 1.1724 | |
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| 0.4114 | 4.7544 | 3000 | 22.1925 | 1.1681 | |
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| 0.3005 | 5.1506 | 3250 | 21.4948 | 1.1947 | |
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| 0.2945 | 5.5468 | 3500 | 22.1454 | 1.1921 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.4.0+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
