metadata

library_name: transformers
language:
  - ur
license: apache-2.0
base_model: openai/whisper-medium
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: 27.720097349677363

Whisper Medium Ur - Your Name

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

Loss: 0.3564
Wer: 27.7201

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: 150
training_steps: 1500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3965	0.6557	500	0.3952	30.0288
0.3086	1.3108	1000	0.3665	27.9635
0.2877	1.9666	1500	0.3564	27.7201

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu121
Datasets 3.4.1
Tokenizers 0.21.0