Instructions to use vasista22/ccc-wav2vec2-360h-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasista22/ccc-wav2vec2-360h-base with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("vasista22/ccc-wav2vec2-360h-base") model = AutoModelForPreTraining.from_pretrained("vasista22/ccc-wav2vec2-360h-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vasista22/ccc-wav2vec2-360h-base: direct link, hf CLI and curl.
- Browser
- Download file 380 MB
-
https://huggingface.co/vasista22/ccc-wav2vec2-360h-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vasista22/ccc-wav2vec2-360h-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vasista22/ccc-wav2vec2-360h-base/resolve/main/pytorch_model.bin
380 MB
- Xet hash:
- 372fb4310f5488af3ec1679dd25e0feccb69b60a48e265f5961a4522b1c1cced
- Size of remote file:
- 380 MB
- SHA256:
- e04fc630d6282b2751ff21cfa6f0f996b714f68255d526db62a270aa6451c244
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