Instructions to use basilkr/Malasar_48WER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use basilkr/Malasar_48WER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="basilkr/Malasar_48WER")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("basilkr/Malasar_48WER") model = AutoModelForSpeechSeq2Seq.from_pretrained("basilkr/Malasar_48WER") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a9fe571189abf2af3a718be4dd737d269c5a4595fad6d1f560dcc845effac38
- Size of remote file:
- 14.6 kB
- SHA256:
- b0360bdf2dca6de7253e15163ab922b5a48a6f88a29bf61e0a48b68f00924339
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.