Instructions to use flax-community/wav2vec2-dhivehi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flax-community/wav2vec2-dhivehi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="flax-community/wav2vec2-dhivehi")# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("flax-community/wav2vec2-dhivehi") model = AutoModelForPreTraining.from_pretrained("flax-community/wav2vec2-dhivehi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e35d32fad8dfa7630b5667491cc1b5e511c5ef794884f38dfd9806e536fd1fc5
- Size of remote file:
- 380 MB
- SHA256:
- 3cc7fbe6476cbd5ec145410db35e6f8bdf4d38f0cae8f00b35e80f2bf8a65339
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