Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Instructions to use navin-kumar-j/whisper-base-en-w-pcd-10-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use navin-kumar-j/whisper-base-en-w-pcd-10-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="navin-kumar-j/whisper-base-en-w-pcd-10-3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("navin-kumar-j/whisper-base-en-w-pcd-10-3") model = AutoModelForSpeechSeq2Seq.from_pretrained("navin-kumar-j/whisper-base-en-w-pcd-10-3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24020aea6e590dd6525b9a121215308f764d9510b3da3c3790987a304cc623e6
- Size of remote file:
- 290 MB
- SHA256:
- a8ae6da5589f5c99adf4f534a22b153e59625fd378e4468977f9a34fd0f4bf60
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.