metadata
library_name: transformers
language:
- jv
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- whisper
- javanese
- asr
- generated_from_trainer
datasets:
- jv_id_asr_split
metrics:
- wer
model-index:
- name: bagasshw/whisper-large-v2-jv-filtered
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: validation
args: jv_id_asr_source
metrics:
- name: Wer
type: wer
value: 6.3495792761984315
bagasshw/whisper-large-v2-jv-filtered
This model is a fine-tuned version of openai/whisper-large-v2 on the jv_id_asr_split dataset. It achieves the following results on the evaluation set:
- Loss: 0.0756
- Wer: 6.3496
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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: 500
- training_steps: 60000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2396 | 0.1535 | 5000 | 0.2416 | 19.2376 |
| 0.1912 | 0.3071 | 10000 | 0.2009 | 16.2781 |
| 0.1744 | 0.4606 | 15000 | 0.1687 | 14.4586 |
| 0.1448 | 0.6142 | 20000 | 0.1494 | 12.9593 |
| 0.138 | 0.7677 | 25000 | 0.1333 | 11.8420 |
| 0.1312 | 0.9213 | 30000 | 0.1192 | 10.7587 |
| 0.0605 | 1.0748 | 35000 | 0.1096 | 9.9808 |
| 0.0524 | 1.2284 | 40000 | 0.1022 | 9.3473 |
| 0.0506 | 1.3819 | 45000 | 0.0929 | 8.2091 |
| 0.0476 | 1.5355 | 50000 | 0.0859 | 7.7627 |
| 0.0389 | 1.6890 | 55000 | 0.0795 | 6.7298 |
| 0.0357 | 1.8426 | 60000 | 0.0756 | 6.3496 |
Framework versions
- Transformers 4.50.0.dev0
- Pytorch 2.7.0+cu128
- Datasets 2.18.0
- Tokenizers 0.21.1