This model is a fine-tuned version of openai/whisper-large-v3 on the common_voice_17_0 dataset. And IIT Madras SPRING-INX Data. It achieves the following results on the evaluation set:

Loss: 0.0669
Wer: 30.8364

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 16
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: constant_with_warmup
lr_scheduler_warmup_steps: 500
training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1229	0.2618	500	0.1370	109.1423
0.097	0.5236	1000	0.1005	41.9964
0.0853	0.7853	1500	0.0852	32.2293
0.0472	1.0471	2000	0.0786	56.4450
0.0503	1.3089	2500	0.0746	87.3848
0.0479	1.5707	3000	0.0716	30.8487
0.0511	1.8325	3500	0.0665	32.1765
0.0294	2.0942	4000	0.0710	48.9811
0.03	2.3560	4500	0.0695	52.9004
0.0333	2.6178	5000	0.0669	30.8364

Framework versions

Transformers 4.46.3
Pytorch 2.4.0
Datasets 3.1.0
Tokenizers 0.20.3

bijaykumarsingh/whisper-large-v3-bn-cv17

whisper-large-v3-bn-cv17

Training hyperparameters

Training results

Framework versions

bijaykumarsingh/
whisper-large-v3-bn-cv17