Instructions to use kehanlu/mandarin-wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kehanlu/mandarin-wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kehanlu/mandarin-wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("kehanlu/mandarin-wav2vec2") model = AutoModel.from_pretrained("kehanlu/mandarin-wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf809cb47cc2870ea82b6442dbcdc7164490eb203c8a4438d321c4fb0c2f0254
- Size of remote file:
- 378 MB
- SHA256:
- 9377766bf9752f49a4114ff766368e89b562b1d1d74c91bb99d9a658ea198429
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