--- library_name: transformers tags: - generated_from_trainer metrics: - bleu model-index: - name: whisper-small-es-ja results: [] datasets: - Marianoleiras/voxpopuli_es-ja language: - es - ja base_model: - openai/whisper-small --- # whisper-small-es-ja This model is a fine-tuned version of OpenAI's whisper-small on the Marianoleiras/voxpopuli_es-ja dataset, designed for Spanish-to-Japanese speech-to-text (STT) tasks. It leverages OpenAI's Whisper architecture, which is well-suited for multilingual speech recognition and translation tasks. The model achieves the following results on the evaluation set: - Loss: 1.1724 - Bleu: 22.2850 And the following result on the test set: - Bleu: 21.4557 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - training_steps: 3500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Bleu | Validation Loss | |:-------------:|:------:|:----:|:-------:|:---------------:| | 1.5787 | 0.3962 | 250 | 11.6756 | 1.5196 | | 1.3535 | 0.7924 | 500 | 16.0514 | 1.3470 | | 1.0658 | 1.1886 | 750 | 17.7743 | 1.2533 | | 1.0303 | 1.5848 | 1000 | 19.1894 | 1.2046 | | 0.9893 | 1.9810 | 1250 | 20.1198 | 1.1591 | | 0.7569 | 2.3772 | 1500 | 21.0054 | 1.1546 | | 0.7571 | 2.7734 | 1750 | 21.6425 | 1.1378 | | 0.5557 | 3.1696 | 2000 | 21.7563 | 1.1500 | | 0.5612 | 3.5658 | 2250 | 21.1391 | 1.1395 | | 0.5581 | 3.9620 | 2500 | 22.0412 | 1.1343 | | 0.4144 | 4.3582 | 2750 | 22.2850 | 1.1724 | | 0.4114 | 4.7544 | 3000 | 22.1925 | 1.1681 | | 0.3005 | 5.1506 | 3250 | 21.4948 | 1.1947 | | 0.2945 | 5.5468 | 3500 | 22.1454 | 1.1921 | ### Framework versions - Transformers 4.47.1 - Pytorch 2.4.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0