metadata

base_model: openai/whisper-large-v3
datasets:
  - google/fleurs
language:
  - fa
license: apache-2.0
metrics:
  - wer
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Whisper Large V3 fa ft - Chee Li
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: google/fleurs
          config: fa_ir
          split: None
          args: 'config: fa split: test'
        metrics:
          - type: wer
            value: 30.854777578296428
            name: Wer

Whisper Large V3 fa ft - Chee Li

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: 0.2141
Wer: 30.8548

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-06
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
0.0755	4.6083	1000	0.1422	20.4518
0.0144	9.2166	2000	0.1790	28.1817
0.0036	13.8249	3000	0.2039	28.6605
0.0033	18.4332	4000	0.2141	30.8548

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1