Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Urdu
whisper
Generated from Trainer
Instructions to use Aly116/whisper-medium-ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aly116/whisper-medium-ur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Aly116/whisper-medium-ur")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Aly116/whisper-medium-ur") model = AutoModelForSpeechSeq2Seq.from_pretrained("Aly116/whisper-medium-ur") - Notebooks
- Google Colab
- Kaggle
Whisper Small Ur - San Gandhi
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.
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: 16
- eval_batch_size: 8
- seed: 42
- 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
- training_steps: 500
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Aly116/whisper-medium-ur
Base model
openai/whisper-small