Instructions to use fkHug/modelFromWav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fkHug/modelFromWav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fkHug/modelFromWav2vec")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("fkHug/modelFromWav2vec") model = AutoModelForCTC.from_pretrained("fkHug/modelFromWav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from fkHug/modelFromWav2vec: direct link, hf CLI and curl.
- Browser
- Download file 623 Bytes
-
https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/scheduler.pt
- Command line
-
hf download hf://fkHug/modelFromWav2vec/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/scheduler.pt
623 Bytes
- Xet hash:
- 588c46a86a08850e148343b5b2948de85ed9b0aa502616233708648a225ee550
- Size of remote file:
- 623 Bytes
- SHA256:
- 5304ceda6dea9ea09626fa5e6ce08826c643e59f7d830b3543f6a4267eb7ec49
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