metadata

language:
  - sr
license: apache-2.0
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
base_model: openai/whisper-large-v3
model-index:
  - name: Whisper Large v3 Sr - Slavko Djogic
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 16.1
          type: mozilla-foundation/common_voice_16_1
          args: 'Config: sr'
        metrics:
          - type: wer
            value: 17.2694
            name: Wer

whisper-large-v3-sr

This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set:

Loss: 0.3961
Wer: 17.2694

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: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0498	4.81	1000	0.2004	20.1799
0.0042	9.62	2000	0.3225	18.2395
0.0001	14.42	3000	0.3799	17.2694
0.0001	19.23	4000	0.3961	17.2694

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.16.1
Tokenizers 0.15.1