metadata

library_name: transformers
language:
  - fa
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: 'whisper-large-v3-turbo-fa-c17-avs '
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: mozilla-foundation/common_voice_17_0
          config: fa
          split: None
          args: 'config: fa, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 27.209121534076186

whisper-large-v3-turbo-fa-c17-avs

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

Loss: 0.2583
Wer: 27.2091

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2293	0.4055	1000	0.4008	40.3172
0.167	0.8110	2000	0.3385	34.0503
0.0948	1.2165	3000	0.3067	31.7494
0.0669	1.6221	4000	0.2878	29.7909
0.0458	2.0276	5000	0.2583	27.2091

Framework versions

Transformers 4.52.2
Pytorch 2.7.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1