Instructions to use StephennFernandes/wav2vec2-XLS-R-300m-assamese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StephennFernandes/wav2vec2-XLS-R-300m-assamese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="StephennFernandes/wav2vec2-XLS-R-300m-assamese")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("StephennFernandes/wav2vec2-XLS-R-300m-assamese") model = AutoModel.from_pretrained("StephennFernandes/wav2vec2-XLS-R-300m-assamese") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22d3000ce124d3c1540c10b015ec024f89f9378b41411a444af566bb0c34fd90
- Size of remote file:
- 1.26 GB
- SHA256:
- 2687f5b3f3b0adf7c2c18750d9cca054ca7c80663407ad8c3471f00dd066a78e
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