Instructions to use knagode/vocal-coach with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use knagode/vocal-coach with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("knagode/vocal-coach", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Singing voice classifiers (WavLM embeddings + logistic regression)
One model per task (<task>.joblib). Each scores 1-second windows: frozen microsoft/wavlm-base-plus embeddings
(mean over layers and time) โ StandardScaler โ LogisticRegression.
breathiness
Labels: breathy, clear. Trained on 8 clips (57 windows). Evaluation: leave-one-session-out
precision recall f1-score support
breathy 1.00 0.75 0.86 4
clear 0.80 1.00 0.89 4
accuracy 0.88 8
macro avg 0.90 0.88 0.87 8
weighted avg 0.90 0.88 0.87 8
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