README.md

metadata

library_name: transformers
language:
  - jv
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - whisper
  - javanese
  - asr
  - generated_from_trainer
datasets:
  - jv_id_asr_split
metrics:
  - wer
model-index:
  - name: Whisper-Tiny-Java-v3
    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: 0.2586507557925852

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