Instructions to use Splend1dchan/Taiwanese-Whisper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Splend1dchan/Taiwanese-Whisper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Splend1dchan/Taiwanese-Whisper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Splend1dchan/Taiwanese-Whisper") model = AutoModelForSpeechSeq2Seq.from_pretrained("Splend1dchan/Taiwanese-Whisper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fddf877ad93862f630d686b9927394e4df2a9d5bd198f7e1a18592813d0a7984
- Size of remote file:
- 967 MB
- SHA256:
- 7d914e13ee8b8813f8429e267286c8b4eb5f0e07438d4ec0916a03773a4e5dad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.