Instructions to use Ali116/wav2vec2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ali116/wav2vec2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ali116/wav2vec2-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ali116/wav2vec2-base") model = AutoModelForAudioClassification.from_pretrained("Ali116/wav2vec2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ali116/wav2vec2-base: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/Ali116/wav2vec2-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ali116/wav2vec2-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ali116/wav2vec2-base/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 6869d61a0051c03189374374360c2c2087c27ba564ac1fc23325dae3141d5998
- Size of remote file:
- 378 MB
- SHA256:
- 368bcea9bf18c8183141180875396d2acca0c9238763f593618851e97c803fe8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.