metadata
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
whisper-tiny-javanese-openslr-v2
This model is a fine-tuned version of 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