Instructions to use basilkr/Whisper_Malasar_50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use basilkr/Whisper_Malasar_50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="basilkr/Whisper_Malasar_50")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("basilkr/Whisper_Malasar_50") model = AutoModelForSpeechSeq2Seq.from_pretrained("basilkr/Whisper_Malasar_50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f2dc329000abd6741bbfbd2bdf9b66fbb312f335420acda183afc605ebbbe94
- Size of remote file:
- 967 MB
- SHA256:
- 42e6f30fca2a0b1a132c0d7dd1aa3faf1e576d54db462fdbce70c3dc8a254658
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.