metadata

language:
  - ar
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - nadsoft
  - generated_from_trainer
datasets:
  - nadsoft/arabic-98
metrics:
  - wer
model-index:
  - name: ./hamsa-v0.6Q
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: nadsoft/arabic-98
          type: nadsoft/arabic-98
        metrics:
          - name: Wer
            type: wer
            value: 23.412419116812348

./hamsa-v0.6Q

This model is a fine-tuned version of openai/whisper-medium on the nadsoft/arabic-98 dataset. It achieves the following results on the evaluation set:

Loss: 0.2781
Wer: 23.4124

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: 32
eval_batch_size: 16
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: 300
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.9225	0.33	100	0.8743	24.5136
0.2721	0.67	200	0.2782	24.1367
0.2474	1.0	300	0.2781	23.4124

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.2.dev0
Tokenizers 0.15.0