metadata

base_model: openai/whisper-large-v3
datasets:
  - mozilla-foundation/common_voice_17_0
language:
  - hu
license: apache-2.0
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: Whisper Large V3 HU Full -  snoopyben27
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 17.0
          type: mozilla-foundation/common_voice_17_0
          config: default
          split: test
          args: 'config: hu, split: test'
        metrics:
          - type: wer
            value: 8.860932585806099
            name: Wer

Whisper Large V3 HU Full - snoopyben27

This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.0911
Wer: 8.8609

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 7000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1301	0.3299	1000	0.1351	14.5084
0.1324	0.6598	2000	0.1208	13.2777
0.1136	0.9898	3000	0.1066	11.5548
0.0471	1.3197	4000	0.1030	10.3788
0.0337	1.6496	5000	0.0955	9.8045
0.0311	1.9795	6000	0.0875	9.2438
0.0108	2.3095	7000	0.0911	8.8609

Framework versions

Transformers 4.42.2
Pytorch 2.3.1
Datasets 2.20.0
Tokenizers 0.19.1