Instructions to use Foxasdf/EnglishSpeechToText with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Foxasdf/EnglishSpeechToText with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Foxasdf/EnglishSpeechToText")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Foxasdf/EnglishSpeechToText") model = AutoModelForCTC.from_pretrained("Foxasdf/EnglishSpeechToText", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9625ae358c62c28e15b6af6334fc0957d9ea2dfd8c89334ac2bcbd963f263f4d
- Size of remote file:
- 1.26 GB
- SHA256:
- 1dec4ad919fe9a826d3d7ec322ef5aa5839c701f5dcdc8e19ff1927543e50a01
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.