Instructions to use Akaash1/wav2vec-khmer-english-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akaash1/wav2vec-khmer-english-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Akaash1/wav2vec-khmer-english-all")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Akaash1/wav2vec-khmer-english-all") model = AutoModelForCTC.from_pretrained("Akaash1/wav2vec-khmer-english-all") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 804629f2a8aded3b9da9cc15a7b3539edb5c84cfb347affb4ee6237667e487f1
- Size of remote file:
- 1.26 GB
- SHA256:
- 8c159620972bad1b644fe54c721a760185795cb860efc701a488323f93290751
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