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 training_args.bin from fkHug/modelFromWav2vec: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
-
https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/training_args.bin
- Command line
-
hf download hf://fkHug/modelFromWav2vec/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/training_args.bin
2.29 kB
- Xet hash:
- 55bc19abff5a29223b88d9b207bc2f0de2026187a6e906a4552d81a9f630a5fc
- Size of remote file:
- 2.29 kB
- SHA256:
- 5cdd827ab4948ec800328d97bf7db5684aeb3d304bd1e99a26af5d254783e45b
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