Instructions to use nooobedd/wav2vec_custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nooobedd/wav2vec_custom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nooobedd/wav2vec_custom")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("nooobedd/wav2vec_custom") model = AutoModelForCTC.from_pretrained("nooobedd/wav2vec_custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nooobedd/wav2vec_custom: direct link, hf CLI and curl.
- Browser
- Download file 720 MB
-
https://huggingface.co/nooobedd/wav2vec_custom/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nooobedd/wav2vec_custom/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nooobedd/wav2vec_custom/resolve/main/pytorch_model.bin
720 MB
- Xet hash:
- 22724dd1d04e298454ac05423b5e62cea341040114853c11a77c8c08515f8488
- Size of remote file:
- 720 MB
- SHA256:
- f6b6775f97d8fd77eca41a3c36215a25b6ea03d28514212a67d300815e4b463e
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