Instructions to use NouRed/wav2vec2-finetuned-ks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NouRed/wav2vec2-finetuned-ks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="NouRed/wav2vec2-finetuned-ks")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("NouRed/wav2vec2-finetuned-ks") model = AutoModelForAudioClassification.from_pretrained("NouRed/wav2vec2-finetuned-ks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NouRed/wav2vec2-finetuned-ks: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/NouRed/wav2vec2-finetuned-ks/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NouRed/wav2vec2-finetuned-ks/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NouRed/wav2vec2-finetuned-ks/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 2c413183c38eb5b7fd665bcd38bff6bd41a7e15979c6423d4ff49579a75e4844
- Size of remote file:
- 378 MB
- SHA256:
- 8f6db94926884861ae6dd30d4411b1dd0acdb8e5628698cadc25f82a0cbe7d4d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.