Instructions to use ThomasFG/100-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasFG/100-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ThomasFG/100-10")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ThomasFG/100-10") model = AutoModelForSpeechSeq2Seq.from_pretrained("ThomasFG/100-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65882eeee29ec5012055de8c3fe3ebb8b047965be1536a0252a2fa061c156bd7
- Size of remote file:
- 967 MB
- SHA256:
- b3672be2e63ed2f255a403902a90656915079f6fd019a1dcc4d8136f9bcd3f7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.