---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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 and Japanese-to-Spanish 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 robust performance on both the evaluation and test sets, demonstrating its effectiveness in multilingual STT applications.

It achieves the following results on the evaluation set:
- Loss: 1.1724
- Bleu: 22.2850

It achieves the following results 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