Instructions to use kavyamanohar/w2v2-Malasar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kavyamanohar/w2v2-Malasar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kavyamanohar/w2v2-Malasar")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kavyamanohar/w2v2-Malasar") model = AutoModelForCTC.from_pretrained("kavyamanohar/w2v2-Malasar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88baf4a451c756c0b45f42022c3c007cd943a62bcdaf35ae17438b5045870bbd
- Size of remote file:
- 4.73 kB
- SHA256:
- c424d4cce13250e06367f0d85eeb1edfdfde075f3404d0a4813c2c9ebdd45052
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