README.md

metadata

library_name: transformers
language:
  - ur
license: apache-2.0
base_model: GogetaBlueMUI/whisper-medium-ur-v3
tags:
  - generated_from_trainer
datasets:
  - fsicoli/common_voice_19_0
metrics:
  - wer
model-index:
  - name: Whisper Medium Ur - Your Name
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 19.0
          type: fsicoli/common_voice_19_0
          config: ur
          split: test
          args: 'config: ur, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 25.0787058744725

Whisper Medium Ur - Your Name

This model is a fine-tuned version of GogetaBlueMUI/whisper-medium-ur-v3 on the Common Voice 19.0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.3692
• Wer: 25.0787

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: 3e-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: 100
• training_steps: 1000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1648	0.3279	250	0.3832	28.1711
0.1748	0.6557	500	0.3737	30.1650
0.1887	0.9836	750	0.3587	24.8532
0.132	1.3108	1000	0.3692	25.0787

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu121
• Datasets 3.4.1
• Tokenizers 0.21.0