metadata

library_name: transformers
language:
  - ur
license: apache-2.0
base_model: GogetaBlueMUI/whisper-medium-ur-jalandhary
tags:
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Medium Ur - Muhammad Abdullah on Fleurs
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Fleurs Urdu
          type: google/fleurs
          config: ur_pk
          split: test
          args: 'config: ur_pk, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 23.004972768174284

Whisper Medium Ur - Muhammad Abdullah on Fleurs

This model is a fine-tuned version of GogetaBlueMUI/whisper-medium-ur-jalandhary on the Fleurs Urdu dataset. It achieves the following results on the evaluation set:

Loss: 0.4401
Wer: 23.0050

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: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use 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: 60
training_steps: 600
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1051	0.7576	100	0.3871	23.5851
0.0437	1.5152	200	0.4073	23.7390
0.0209	2.2727	300	0.4184	23.1944
0.0199	3.0303	400	0.4228	23.0523
0.009	3.7879	500	0.4347	23.3602
0.0048	4.5455	600	0.4401	23.0050

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu121
Datasets 3.3.2
Tokenizers 0.21.0