Instructions to use Akaash1/wav2vec-khmer-english-minor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akaash1/wav2vec-khmer-english-minor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Akaash1/wav2vec-khmer-english-minor")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Akaash1/wav2vec-khmer-english-minor") model = AutoModelForCTC.from_pretrained("Akaash1/wav2vec-khmer-english-minor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7efdff9b70d276ecf2c5e5dca15415b3f6392d47ffd8e56079ac9d4614345181
- Size of remote file:
- 1.26 GB
- SHA256:
- 4c6c657d84e70159975b85cfbfb530ef40d56592978b15716c1f56c5f3eeb362
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