This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set:

Loss: 0.1401
Wer Ortho: 0.1663
Wer: 0.0689

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: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 1500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.1691	0.03	500	0.1776	0.2060	0.0941
0.1538	0.05	1000	0.1459	0.1743	0.0730
0.1522	0.08	1500	0.1401	0.1663	0.0689

Framework versions

Transformers 4.37.2
Pytorch 2.0.1+cu117
Datasets 2.16.1
Tokenizers 0.15.1

Sagicc/whisper-large-sr-v2

Whisper Large v2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Sagicc/
whisper-large-sr-v2