metadata

language:
  - sr
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Large v3 Sr
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 13
          type: mozilla-foundation/common_voice_13_0
          config: sr
          split: test
          args: sr
        metrics:
          - name: Wer
            type: wer
            value: 0.05560382276281494

UPDATE

Use an updated fine tunned version Sagicc/whisper-large-v3-sr-cmb with new 50+ hours of dataset.

Whisper Large v3 Sr

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

Loss: 0.1628
Wer Ortho: 0.1635
Wer: 0.0556

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
0.0567	1.34	500	0.1512	0.1676	0.0717
0.0256	2.67	1000	0.1482	0.1585	0.0610
0.0114	4.01	1500	0.1628	0.1635	0.0556

Framework versions

Transformers 4.35.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1