sharjeel103/whisper-tiny-urdu

Whisper Tiny Urdu

This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1247
• Wer: 16.0339

Model description

Whisper Tiny Urdu ASR Model This Whisper Tiny model has been fine-tuned on the Common Voice 17 dataset, which includes over 55 hours of Urdu speech data. The model was trained twice with different hyperparameters to optimize its performance:

First Training: The model was trained on the training set and evaluated on the test set for 20 epochs. Second Training: The model was retrained on the combined train and validation sets, with the test set used for validation, also for 20 epochs. Despite being the smallest variant in its family, this model achieves state-of-the-art performance for Urdu ASR tasks. It can be used for deployment on small devices, offering an excellent balance between efficiency and accuracy.

Intended Use:

Intended uses & limitations

This model is particularly suited for applications on edge devices with limited computational resources. Additionally, it can be converted to a FasterWhisper model using the CTranslate2 library, allowing for even faster inference on devices with lower processing power.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 4e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 3000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0057	10.1351	1500	0.1443	18.1511
0.0005	20.2703	3000	0.1247	16.0339

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1