ifc0nfig/whisper-medium-v3-hi-vyapar-output

Whisper_Medium_Hi_Vyapar_V3

This model is a fine-tuned version of openai/whisper-medium on the Vyapar_Calling_Data_500_hours dataset. It achieves the following results on the evaluation set:

• Loss: 0.5455
• Wer: 31.9144

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Use adafactor and the args are: No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 20000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.816	0.0259	1000	0.8171	43.2510
0.7255	0.0519	2000	0.7386	51.1530
0.7224	0.0778	3000	0.6966	39.1227
0.7127	0.1038	4000	0.6729	37.4067
0.6472	0.1297	5000	0.6562	36.8165
0.6547	0.1557	6000	0.6404	36.5441
0.6207	0.1816	7000	0.6248	36.8320
0.6445	0.2076	8000	0.6162	35.6743
0.6371	0.2335	9000	0.6057	34.4885
0.5909	0.2594	10000	0.5983	33.8377
0.6115	0.2854	11000	0.5895	34.3792
0.6298	0.3113	12000	0.5834	33.9399
0.6091	0.3373	13000	0.5757	33.0228
0.5421	0.3632	14000	0.5702	32.8652
0.6022	0.3892	15000	0.5654	33.0867
0.5559	0.4151	16000	0.5596	32.6704
0.5314	0.4410	17000	0.5544	32.3119
0.5714	0.4670	18000	0.5503	32.2174
0.5171	0.4929	19000	0.5471	31.9557
0.5268	0.5189	20000	0.5455	31.9144

Framework versions

• Transformers 4.48.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0