---
library_name: transformers
language:
- jav
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- SLR41
metrics:
- wer
model-index:
- name: Whisper Small Java
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: SLR Javanenese
      type: SLR41
      args: 'config: java, split: train, test'
    metrics:
    - name: Wer
      type: wer
      value: 26.77317840716534
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Small Java

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SLR Javanenese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2729
- Wer: 26.7732

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.8934        | 0.3003 | 100  | 0.8003          | 52.4086 |
| 0.4852        | 0.6006 | 200  | 0.5305          | 39.4578 |
| 0.4111        | 0.9009 | 300  | 0.4214          | 32.8250 |
| 0.2101        | 1.2012 | 400  | 0.3655          | 30.4527 |
| 0.1803        | 1.5015 | 500  | 0.3257          | 29.1939 |
| 0.1845        | 1.8018 | 600  | 0.3072          | 27.4752 |
| 0.0899        | 2.1021 | 700  | 0.2997          | 26.4585 |
| 0.0816        | 2.4024 | 800  | 0.2850          | 26.3617 |
| 0.078         | 2.7027 | 900  | 0.2755          | 26.7248 |
| 0.0769        | 3.0030 | 1000 | 0.2729          | 26.7732 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1