Instructions to use codingninja/w2v-pa-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codingninja/w2v-pa-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="codingninja/w2v-pa-v2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("codingninja/w2v-pa-v2") model = AutoModelForCTC.from_pretrained("codingninja/w2v-pa-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2156920ed5500d7482ff1b8acdb4b04e10ffa3d09c0a209e564b40725dcc2e04
- Size of remote file:
- 4.91 GB
- SHA256:
- 4c0c09a06e1c69a9c5d0e62c7cc61a26928e0fd76c0c6717b5307a5c055c1a6f
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