Instructions to use makhataei/Wav2vec2-xlsr-Shemo-Ravdess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use makhataei/Wav2vec2-xlsr-Shemo-Ravdess with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("makhataei/Wav2vec2-xlsr-Shemo-Ravdess") model = Wav2Vec2ForSpeechClassification.from_pretrained("makhataei/Wav2vec2-xlsr-Shemo-Ravdess", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from makhataei/Wav2vec2-xlsr-Shemo-Ravdess: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/makhataei/Wav2vec2-xlsr-Shemo-Ravdess/resolve/main/training_args.bin
- Command line
-
hf download hf://makhataei/Wav2vec2-xlsr-Shemo-Ravdess/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/makhataei/Wav2vec2-xlsr-Shemo-Ravdess/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- 34cb09635b969f6964b12e283ad911e9fd422e4dadbd7d3a115a325b1bb80284
- Size of remote file:
- 3.9 kB
- SHA256:
- 07469b7c69a131f82667fa760dfaf6d0d95409f0087936cd5e6d1f3b12db1b03
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.