Instructions to use krishan23/whisper_train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krishan23/whisper_train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="krishan23/whisper_train")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("krishan23/whisper_train") model = AutoModelForSpeechSeq2Seq.from_pretrained("krishan23/whisper_train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df8b02dd860468d4c8b85b3f76e46c42a94111d0e1148b01c779a80e99773530
- Size of remote file:
- 3.03 GB
- SHA256:
- 6211158808f20e9f041ec0affc831b8a33aa4052067c91ca8d34da4884d12f5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.