metadata

library_name: transformers
language:
  - my
license: apache-2.0
base_model: openai/whisper-small
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - openai-whisper-burmese
metrics:
  - wer
model-index:
  - name: Whisper Small My - Ye Bhone Lin
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: openai-whisper-SLR
          type: openai-whisper-burmese
          args: 'config: my, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 78.27149522328372

Whisper Small My - Ye Bhone Lin

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

Loss: 0.2066
Wer: 78.2715

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: 500
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0386	7.8740	1000	0.1604	94.4679
0.0006	15.7480	2000	0.2066	78.2715

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0