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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc9e0b4d4192d346c56e456f94843158f9c901b51598dc4da535b5001bd0a63c
- Size of remote file:
- 3.06 GB
- SHA256:
- 53dd33eafe80a408bd36996672ab0594e3f69c430395115eedcf3e5fce03a378
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.