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---
library_name: transformers
language:
- jav
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- SLR41_35
metrics:
- wer
model-index:
- name: Whisper Small Java
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: SLR Javanenese 41_35
type: SLR41_35
args: 'config: java, split: train, test'
metrics:
- type: wer
value: 29.24663420223432
name: Wer
---
<!-- 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 41_35 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4200
- Wer: 29.2466
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4922 | 0.16 | 100 | 0.6047 | 37.4678 |
| 0.435 | 0.32 | 200 | 0.5572 | 35.9424 |
| 0.5688 | 0.48 | 300 | 0.5090 | 33.5649 |
| 0.4779 | 0.64 | 400 | 0.4799 | 31.8390 |
| 0.4247 | 0.8 | 500 | 0.4540 | 30.8364 |
| 0.42 | 0.96 | 600 | 0.4368 | 30.2492 |
| 0.2276 | 1.12 | 700 | 0.4330 | 29.6333 |
| 0.2137 | 1.28 | 800 | 0.4264 | 29.5832 |
| 0.236 | 1.44 | 900 | 0.4215 | 29.2395 |
| 0.1971 | 1.6 | 1000 | 0.4200 | 29.2466 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|