This model is a fine-tuned version of openai/whisper-small on the Myanmar Speech Dataset (OpenSLR-80) dataset. It achieves the following results on the evaluation set:

Loss: 0.3932
Wer: 161.2199
Cer: 90.5602

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 50
training_steps: 500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.8707	0.7018	100	0.8376	219.3678	91.2284
0.6616	1.4	200	0.6328	258.8157	91.5565
0.4185	2.0982	300	0.3932	161.2199	90.5602
0.284	2.8	400	0.2824	184.5058	90.4603
0.2265	3.4982	500	0.2524	173.4194	90.1916

Framework versions

Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.4.2
Tokenizers 0.22.1

myatsu/whisper-small-burmese

Whisper Small Burmese - SLR80 Fine-Tuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

myatsu/
whisper-small-burmese