Instructions to use ThomasFG/101.25-33.75 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasFG/101.25-33.75 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ThomasFG/101.25-33.75")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ThomasFG/101.25-33.75") model = AutoModelForSpeechSeq2Seq.from_pretrained("ThomasFG/101.25-33.75", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81f5a0c926de443f9460db6efca744e4e9e90ec65be674d70afaaaece7a62643
- Size of remote file:
- 4.73 kB
- SHA256:
- 85ad473d332448a5f0c0183f6cef4fb56bf3132b8955c03f38692a4caf808e66
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.