README.md

metadata

language:
  - fa
license: apache-2.0
base_model: vargha/whisper-large-v3
tags:
  - generated_from_trainer
datasets:
  - vargha/persian_customer-service_datasets
metrics:
  - wer
model-index:
  - name: Whisper large V3 Persian-Tuned
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: persian_customer-service
          type: vargha/persian_customer-service_datasets
          args: 'config: fa, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 45.70446735395189

Whisper large V3 Persian-Tuned

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

• Loss: 0.6814
• Wer: 45.7045

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: 8
• eval_batch_size: 4
• 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
0.0019	23.2558	2000	0.6164	46.6590
0.0001	46.5116	4000	0.6814	45.7045

Framework versions

• Transformers 4.44.0
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1