Instructions to use hts98/temp2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/temp2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/temp2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hts98/temp2") model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/temp2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc93a6a137cced5961c3d2c588de90caad6e1d156b1f937c3ae16a62a00d3aef
- Size of remote file:
- 3.06 GB
- SHA256:
- b35c6c663b8d26555867fdd1dc983f60921435d3d569895f7e349c1ca699cf1f
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