Instructions to use emon1521/wav2vec2-try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emon1521/wav2vec2-try with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="emon1521/wav2vec2-try")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("emon1521/wav2vec2-try") model = AutoModelForCTC.from_pretrained("emon1521/wav2vec2-try", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from emon1521/wav2vec2-try: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/emon1521/wav2vec2-try/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://emon1521/wav2vec2-try/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/emon1521/wav2vec2-try/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- b3f33d2adbf845295b0802ac15ee2b42db660f525db8ef8b736d910abf32a87b
- Size of remote file:
- 378 MB
- SHA256:
- 9df590b50008a1569222e19f84f5162468a4efa87e15dda8ceabc28c6d4dc0f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.