Sagicc/whisper-medium-sr-cmb

Update

Use an updated fine tunned version Sagicc/whisper-medium-sr-v2 with new 10+ hours of dataset.

Whisper Medium cmb

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 13 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1374
• Wer Ortho: 0.1589
• Wer: 0.0658

Model description

This is a fine tunned on merged datasets Common Voice 13 + Fleurs + Juzne vesti (South news)

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: 4
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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
0.342	0.48	500	0.1604	0.1863	0.0862
0.3454	0.95	1000	0.1388	0.1589	0.0667
0.2247	1.43	1500	0.1374	0.1589	0.0658

Framework versions

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