---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- jv_id_asr_split
metrics:
- wer
model-index:
- name: whisper-tiny-javanese-openslr-v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: jv_id_asr_split
      type: jv_id_asr_split
      config: jv_id_asr_source
      split: None
      args: jv_id_asr_source
    metrics:
    - name: Wer
      type: wer
      value: 0.6603038810125197
---

<!-- 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-tiny-javanese-openslr-v2

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the jv_id_asr_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3406
- Wer: 0.6603

## 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: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.6676        | 0.8643 | 500  | 0.5638          | 0.4824 |
| 0.4792        | 1.7277 | 1000 | 0.4284          | 0.5330 |
| 0.3988        | 2.5912 | 1500 | 0.3772          | 0.5687 |
| 0.3565        | 3.4546 | 2000 | 0.3528          | 0.6204 |
| 0.3386        | 4.3181 | 2500 | 0.3406          | 0.6603 |


### Framework versions

- Transformers 4.50.0.dev0
- Pytorch 2.6.0+cu126
- Datasets 3.3.2
- Tokenizers 0.21.0