This model is a fine-tuned version of openai/whisper-small on the Vyapar Calling Data 1200 hours dataset. It achieves the following results on the evaluation set:

Loss: 0.9627
Wer: 48.4083

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 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: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.8904	0.9251	1000	1.0187	56.1507
1.7278	1.8501	2000	0.9433	53.3196
1.1629	2.7752	3000	0.9364	48.6768
0.8499	3.7003	4000	0.9627	48.4083

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu118
Datasets 3.2.0
Tokenizers 0.21.0

ifc0nfig/whisper-small-hi-vyapar_v2

Whisper Small Hi - Vyapar V2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ifc0nfig/
whisper-small-hi-vyapar_v2