Instructions to use hts98/temp1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/temp1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/temp1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hts98/temp1") model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/temp1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ea02ff73614b720f31c5c0862f5f0309844c0619b00df453792a67207848f02
- Size of remote file:
- 3.06 GB
- SHA256:
- a2d0f4ca8537c83c31ff9c6569857d58afc1913cfb332fa43950177c89c1c35f
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