This model is a fine-tuned version of openai/whisper-large-v3-turbo on the whisper-large-finetune-appen-dhc dataset. It achieves the following results on the evaluation set:

Loss: 0.8896
Model Preparation Time: 0.0074
Wer: 0.4650

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: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
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: linear
lr_scheduler_warmup_steps: 400
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Wer
0.5406	1.0	250	0.8937	0.0074	0.5801
0.6665	2.0	500	0.8749	0.0074	0.4803
0.5642	3.0	750	0.6730	0.0074	0.3495
0.373	4.0	1000	0.5504	0.0074	0.2714
0.2652	5.0	1250	0.5017	0.0074	0.2281
0.1719	6.0	1500	0.4657	0.0074	0.2038
0.1011	7.0	1750	0.4561	0.0074	0.1905
0.0503	7.9685	1992	0.4572	0.0074	0.1710

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.2

dhaval0108/whisper-large-v3-turbo-finetuned-appen-dhc-20k

whisper-large-v3-turbo-finetuned-appen-dhc

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dhaval0108/
whisper-large-v3-turbo-finetuned-appen-dhc-20k