Audio Classification
Transformers
PyTorch
hubert
Speech Emotion Recognition
SER
Transformer
HuBERT
Affective Computing
custom_code
Instructions to use amiriparian/ExHuBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amiriparian/ExHuBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="amiriparian/ExHuBERT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("amiriparian/ExHuBERT", trust_remote_code=True) model = AutoModelForAudioClassification.from_pretrained("amiriparian/ExHuBERT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amiriparian/ExHuBERT: direct link, hf CLI and curl.
- Browser
- Download file 2.67 GB
-
https://huggingface.co/amiriparian/ExHuBERT/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://amiriparian/ExHuBERT@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amiriparian/ExHuBERT/resolve/refs%2Fpr%2F2/pytorch_model.bin
2.67 GB
- Xet hash:
- 54ced96ad4085d743e5344075b500dfc774831702a161b5cd4f324006481e262
- Size of remote file:
- 2.67 GB
- SHA256:
- c4c35a61b144a2c5f23a3bab59125bd5e0eb220e389187ebb67a8aca681a07f3
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