Downloads · 30 days
52
63% of all-time downloads
whisper-tiny-javanese-openslr-v3
bagasshw/whisper-tiny-javanese-openslr-v3
whisper-tiny-javanese-openslr-v3 is a automatic speech recognition model from bagasshw. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
83
Public
Parameters
37.8M
4.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors151 MB · 96%
At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Mar 11, 2025
- Updated
- Mar 16, 2025
- SHA
- 47efd179
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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-Java-v3
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: 0.2980
- Wer: 0.2587
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.1788 | 0.0540 | 500 | 0.9671 | 0.6590 |
| 0.8015 | 0.1081 | 1000 | 0.6977 | 0.5305 |
| 0.6498 | 0.1621 | 1500 | 0.5725 | 0.6670 |
| 0.5828 | 0.2161 | 2000 | 0.5094 | 0.4829 |
| 0.5226 | 0.2702 | 2500 | 0.4642 | 0.3860 |
| 0.4955 | 0.3242 | 3000 | 0.4341 | 0.3915 |
| 0.4616 | 0.3782 | 3500 | 0.4128 | 0.3540 |
| 0.4474 | 0.4323 | 4000 | 0.3900 | 0.3614 |
| 0.4387 | 0.4863 | 4500 | 0.3736 | 0.3563 |
| 0.4154 | 0.5403 | 5000 | 0.3606 | 0.3274 |
| 0.419 | 0.5944 | 5500 | 0.3495 | 0.3144 |
| 0.3799 | 0.6484 | 6000 | 0.3398 | 0.2922 |
| 0.3802 | 0.7024 | 6500 | 0.3290 | 0.3044 |
| 0.3611 | 0.7565 | 7000 | 0.3225 | 0.2823 |
| 0.3548 | 0.8105 | 7500 | 0.3168 | 0.2733 |
| 0.346 | 0.8645 | 8000 | 0.3105 | 0.2660 |
| 0.3547 | 0.9186 | 8500 | 0.3063 | 0.2708 |
| 0.3211 | 0.9726 | 9000 | 0.3019 | 0.2827 |
| 0.2718 | 1.0267 | 9500 | 0.2990 | 0.2660 |
| 0.2859 | 1.0807 | 10000 | 0.2980 | 0.2587 |
Framework versions
- Transformers 4.50.0.dev0
- Pytorch 2.6.0+cu126
- Datasets 3.4.0
- Tokenizers 0.21.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- jv
- Created
- Mar 11, 2025
- Updated
- Mar 16, 2025