Whisper-med-ml

This model is a fine-tuned version of openai/whisper-medium on the datasets: IMASC, MSC, OpenSLR Malayalam Train split, Festvox Malayalam .

It achieves the following results on the validation set : OpenSLR-Test

• Loss: 0.0318
• Wer: 14.7300

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• training_steps: 6000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0599	0.4	1000	0.0910	42.4981
0.0341	0.79	2000	0.0584	30.0572
0.0183	1.19	3000	0.0439	23.1650
0.0147	1.58	4000	0.0363	18.7360
0.0107	1.98	5000	0.0322	16.4220
0.0032	2.37	6000	0.0318	14.7300

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.1+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1