Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForXVector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForXVector with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForXVector") model = AutoModelForAudioXVector.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForXVector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18b47b44ff3d958afe3251df1aa1bfb4c544296c51a41f035e04f0228288d924
- Size of remote file:
- 214 kB
- SHA256:
- fece0535df90ebe17fd6093c581176804da271528bff2b0f3b064780209018d6
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