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 pytorch_model.bin from fkHug/modelFromWav2vec: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fkHug/modelFromWav2vec/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 525f5d0f9d4bf5b1b5eb8ad3b96229f379b7791af9a3b23579e0f47664ae8ebb
- Size of remote file:
- 1.26 GB
- SHA256:
- ca427802cb7f17ba37e134d37c1ef057881a795422d6eb9a4e25300c6b3482eb
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