metadata
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