Instructions to use paxstan/wav2vec2-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paxstan/wav2vec2-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="paxstan/wav2vec2-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("paxstan/wav2vec2-test") model = AutoModelForCTC.from_pretrained("paxstan/wav2vec2-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from paxstan/wav2vec2-test: direct link, hf CLI and curl.
- Browser
- Download file 3 kB
-
https://huggingface.co/paxstan/wav2vec2-test/resolve/main/training_args.bin
- Command line
-
hf download hf://paxstan/wav2vec2-test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/paxstan/wav2vec2-test/resolve/main/training_args.bin
3 kB
- Xet hash:
- 7bec35923fc9f3992d791beed0d70f05a48e38c482f172f1e895cf270fdcccff
- Size of remote file:
- 3 kB
- SHA256:
- d0370befd9648cac7f0aac1d35dce8cc9afddd66e9f3067a755358d664c43f3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.