Sagicc/whisper-large-v3-sr-cmb

Whisper Large v3 cmb

This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 13, Google Fleurs and juzne vesti dataset. It achieves the following results on the evaluation set:

• Loss: 0.1111
• Wer Ortho: 0.1339
• Wer: 0.0415

Model description

Dataset Juzne vesti is published by

Rupnik, Peter and Ljubešić, Nikola, 2022,
ASR training dataset for Serbian JuzneVesti-SR v1.0, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042,
http://hdl.handle.net/11356/1679.

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
0.2766	0.48	500	0.1350	0.1670	0.0595
0.2813	0.95	1000	0.1134	0.1426	0.0491
0.1858	1.43	1500	0.1111	0.1339	0.0415

Framework versions

• Transformers 4.35.2
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.14.1