Instructions to use jarvisx17/wav2vec2-base-Med-ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jarvisx17/wav2vec2-base-Med-ASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jarvisx17/wav2vec2-base-Med-ASR")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jarvisx17/wav2vec2-base-Med-ASR") model = AutoModelForCTC.from_pretrained("jarvisx17/wav2vec2-base-Med-ASR") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67940f2f9f6c2f2b9baaa0b0fe69a6621571b543ddb3f86aafaef819a83cf712
- Size of remote file:
- 378 MB
- SHA256:
- 6585ed0b85ce7e4ef70277e703595a7b3bc20fa0fb4a2ad5703bfb2b07dfbe7b
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