metadata

language:
  - vi
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Large V3 Vi - Prateek Jain
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: vi_vn
          split: None
          args: 'config: vi, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 218.83302440531355

Whisper Large V3 Vi - Prateek Jain

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

Loss: 0.2355
Wer: 218.8330

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: 250
training_steps: 1500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0148	2.66	500	0.2193	80.1012
0.0014	5.32	1000	0.2275	247.5556
0.0004	7.98	1500	0.2355	218.8330

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.1