transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer es ja dataset:Marianoleiras/voxpopuli_es-ja base_model:openai/whisper-small base_model:finetune:openai/whisper-small endpoints_compatible region:us
---
base_model:
- openai/whisper-small
datasets:
- Marianoleiras/voxpopuli_es-ja
language:
- es
- ja
library_name: transformers
metrics:
- bleu
tags:
- generated_from_trainer
model-index:
- name: whisper-small-es-ja
  results: []
---

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# whisper-small-es-ja

## Model Overview
This model was developed as part of a workshop organized by Yasmin Moslem, focusing on **speech-to-text pipelines**.
The workshop's primary goal was to enable accurate transcription and translation of spoken source languages into written target languages while learning about end-to-end and cascaded approaches in the process. 

This model represents an **end-to-end solution** for Spanish-to-Japanese speech-to-text (STT) tasks and is a fine-tuned version of OpenAI's Whisper-small, specifically trained on the **[Marianoleiras/voxpopuli_es-ja](https://huggingface.co/datasets/Marianoleiras/voxpopuli_es-ja)** dataset for Spanish-to-Japanese speech-to-text (STT) tasks.

The model achieves performance metrics on the provided dataset:

**Evaluation Set:**
- Loss: **1.1724**
- BLEU: **22.2850**

**Test Set:**
- BLEU: **20.8607**
- ChrF++: **23.3571**
- Comet: **77.6979**

(Baseline evaluation on test set: BLEU 0.4793)

## 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

## Linked Models

- **[Whisper-Small-es](https://huggingface.co/Marianoleiras/whisper-small-es)**: The ASR model of the cascaded approach built using this dataset.
- **[NLLB-200-Distilled-es-ja](https://huggingface.co/Marianoleiras/nllb-200-distilled-es-ja)**: The MT model of the cascaded approach built using this dataset.

# Model Card Contact

Mariano González (marianoleiras@hotmail.com)