metadata

base_model: openai/whisper-tiny
datasets:
  - fleurs
language:
  - id
library_name: transformers
license: apache-2.0
metrics:
  - wer
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Whisper Tiny Indonesian - Chee Li
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: fleurs
          config: he_il
          split: None
          args: 'config: id split: test'
        metrics:
          - type: wer
            value: 61.51004728132388
            name: Wer

Whisper Tiny Indonesian - Chee Li

This model is a fine-tuned version of openai/whisper-tiny on the Google Fleurs dataset. It achieves the following results on the evaluation set:

Loss: 1.1541
Wer: 61.5100

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 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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3796	4.4643	1000	0.7934	60.0473
0.097	8.9286	2000	0.8975	61.0446
0.0167	13.3929	3000	1.0411	61.1998
0.0057	17.8571	4000	1.1252	62.8694
0.0044	22.3214	5000	1.1541	61.5100

Framework versions

Transformers 4.46.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.1