Instructions to use basilkr/whisper_st with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use basilkr/whisper_st with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="basilkr/whisper_st")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("basilkr/whisper_st") model = AutoModelForSpeechSeq2Seq.from_pretrained("basilkr/whisper_st") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee339290ab2fc0e09194c6103b97cdee007be8aeecb66be7e12ea146eab639b7
- Size of remote file:
- 3.58 kB
- SHA256:
- 650c4915923bf420700799c827055a5f8166bb2b78f03ce97a397efd4c0d503b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.