Instructions to use kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train") model = AutoModelForAudioXVector.from_pretrained("kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e41787f7d447bfd116904a76cc61e910ee39917f8c915dbe95c804b9a67d676e
- Size of remote file:
- 4.09 kB
- SHA256:
- 7637a0f3e9f6f246c0676a127ee3cf0d092b9533ce1a7cfe0db72bff656f379a
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