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 pytorch_model.bin from makhataei/Wav2vec2-xlsr-Shemo-Ravdess: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/makhataei/Wav2vec2-xlsr-Shemo-Ravdess/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://makhataei/Wav2vec2-xlsr-Shemo-Ravdess/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/makhataei/Wav2vec2-xlsr-Shemo-Ravdess/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- 1a618645498214a55b95d90188246753bce0dc4be45953ce8d933d07bb51c66d
- Size of remote file:
- 1.27 GB
- SHA256:
- 009e78d98455796067a03689e5a3cb2d3c69a9214fcd342df19ec8e07b42297c
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