Instructions to use cdactvm/w2v-assames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cdactvm/w2v-assames with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cdactvm/w2v-assames")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cdactvm/w2v-assames") model = AutoModelForCTC.from_pretrained("cdactvm/w2v-assames", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce32a87040f92fbcbf0ec8cd465fe73686a9213c9e84fd8a572a1df2ed3f0b57
- Size of remote file:
- 378 MB
- SHA256:
- 738150020607ec51c38946649016d54851e5e9db2f1f4098801d35f981e47e53
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