Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Arabic
whisper
Generated from Trainer
Instructions to use Baselhany/Graduation_Project_Whisper_tiny3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Baselhany/Graduation_Project_Whisper_tiny3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Baselhany/Graduation_Project_Whisper_tiny3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Baselhany/Graduation_Project_Whisper_tiny3") model = AutoModelForSpeechSeq2Seq.from_pretrained("Baselhany/Graduation_Project_Whisper_tiny3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 658f1a272de71da713c31f725a47b6322167d172810b4aad69620fee1945ba14
- Size of remote file:
- 151 MB
- SHA256:
- f95f2c9d9783968cdbc52a160691d72affb0b95667cb7e2b894e61e97ec581a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.