All in One Version : Youtube WAV Download, Separating Vocal, Splitting Audio, Training, and Inference Using Google Colab
-
Updated
May 27, 2024 - Jupyter Notebook
All in One Version : Youtube WAV Download, Separating Vocal, Splitting Audio, Training, and Inference Using Google Colab
AI Vtuber是一个由 【ChatterBot/ChatGPT/claude/langchain/chatglm/text-gen-webui/闻达/千问/kimi/ollama】 驱动的虚拟主播【Live2D/UE/xuniren】,可以在 【Bilibili/抖音/快手/微信视频号/拼多多/斗鱼/YouTube/twitch/TikTok】 直播中与观众实时互动 或 直接在本地进行聊天。它使用TTS技术【edge-tts/VITS/elevenlabs/bark/bert-vits2/睿声】生成回答并可以选择【so-vits-svc/DDSP-SVC】变声;指令协同SD画图。
This Python script visualizes the decision boundaries created by a linear Support Vector Classifier (SVC) on the Iris dataset. It utilizes scikit-learn for machine learning functionalities and matplotlib for plotting. The code loads the Iris dataset, trains a linear SVC on the first two features (sepal length and sepal width)
This is a binary classification problem. There are numerous factors that can contribute to the presence of heart disease. What is the most important factor causing heart disease? Can an accurate classifier be built to predict the presence of heart disease in patients? These are the questions we want to answer with this project.
This project focuses on building a fraud detection model for credit card transactions using a dataset containing transactions made by European cardholders in September 2013. We are working with a highly unbalanced dataset and the challenge lies in effectively detecting fraudulent transactions while minimizing false positives.
⚡️ 80x faster language detection with Fasttext | Split text by language for TTS
🚀 Docker image with supervisord and sshd (rootless version)
🚀 Docker image with supervisord and sshd
It contains different types of machine learning models like Linear Regression, Logistic Regression, Clustering techniques, Time Series Forecasting etc
Core Engine of Singing Voice Conversion & Singing Voice Clone
Customer Churn Prediction is a machine learning project aimed at predicting whether a specific user will leave a service or not. The project involves extensive exploratory data analysis (EDA), model training and deployment of a Streamlit web application for user interaction.
Bachelor thesis work: Debugging of a counterfactual-explanation library (CFNOW) and its usage in the context of eXplainable Affective Computing. Final evaluation: 30/30.
Detecting Fake Job Postings - Data Visualization, TF-IDF, XGBoost, SVC
This repository contains my solutions and implementations for assignments assigned during the Machine Learning course.
Add a description, image, and links to the svc topic page so that developers can more easily learn about it.
To associate your repository with the svc topic, visit your repo's landing page and select "manage topics."