Sagicc/whisper-medium-sr-cmb

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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
• —trainbatchsize: 4
• —evalbatchsize: 8
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 16
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 50
• —training_steps: 1500
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

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