Instructions to use snehatyagi/wav2vec2_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use snehatyagi/wav2vec2_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="snehatyagi/wav2vec2_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("snehatyagi/wav2vec2_test") model = AutoModelForCTC.from_pretrained("snehatyagi/wav2vec2_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from snehatyagi/wav2vec2_test: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/snehatyagi/wav2vec2_test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://snehatyagi/wav2vec2_test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/snehatyagi/wav2vec2_test/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 8cb78d349192763f675bc2b966279f8cb6154339f3d5a7c9ef131023c32d5b98
- Size of remote file:
- 378 MB
- SHA256:
- eb3898f4c2d1d885412e80a999d61c796803dae5df8686d92b871d04fb5856ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.