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Introduction

• so-vits-svc-fork: A branch of the so-vits-svc project, designed to improve high-quality inference for singing voice conversion.

• Real-time inference: Supports real-time voice conversion and graphical inference interface.

• F0 Mean Pooling: A feature that smooths pitch fluctuations to reduce muffled sounds caused by pitch estimation errors.

• High-quality audio: The fork aims to enhance audio quality, particularly in high-pitched sounds.

• Compatibility: Compatible with so-vits-svc4.0 models and supports various languages and dialects.

Features [1]

• Real-time inference: Allows for real-time voice conversion, making it suitable for live performances.

• Graphical interface: Provides a user-friendly graphical interface for easier operation.

• F0 Mean Pooling: Smooths pitch fluctuations to reduce muffled sounds caused by pitch estimation errors.

• High-quality audio: Enhances audio quality, particularly in high-pitched sounds.

• Compatibility: Compatible with so-vits-svc4.0 models and supports various languages and dialects.

Installation [1]

• Python version: Requires Python 3.8.9.

• Dependencies: Install necessary dependencies using pip or conda.

• GitHub repository: Clone the repository from GitHub.

• Pre-trained models: Download pre-trained models for better performance.

• Configuration: Edit Configuration files as needed for your specific use case.

Usage [2]

• Data preparation: Prepare audio data in WAV format, 44100 Hz, 16-bit, mono.

• Preprocessing: Use provided scripts to preprocess the audio data.

• Training: Train the model using the prepared data and configuration files.

• Inference: Use the trained model for voice conversion.

• Real-time conversion: Utilize the real-time conversion feature for live performances.

Training [3]

• Data preparation: Ensure high-quality audio data for training.

• Configuration: Edit the configuration files to set training parameters.

• Training process: Use provided scripts to train the model.

• Monitoring: Use tensorboard to monitor training progress.

• Checkpointing: Save model checkpoints at regular intervals.

Inference [3]

• Model loading: Load the trained model and configuration files.

• Audio input: Provide the audio file for conversion.

• Parameter tuning: Adjust parameters for optimal results.

• Real-time inference: Use the real-time inference feature for live performances.

• Output: Save the converted audio file.

Comparisons [3]

• so-vits-svc vs. so-vits-svc-fork: The fork offers real-time inference and a graphical interface.

• so-vits-svc-fork vs. DDSP-SVC: DDSP-SVC requires fewer hardware resources and supports real-time models.

• so-vits-svc-fork vs. Bert-VITS2: Bert-VITS2 is more suited for TTS applications.

• so-vits-svc-fork vs. RVC: RVC offers faster optimization and more features.

• so-vits-svc-fork vs. original so-vits-svc: The fork provides enhanced audio quality, especially for high-pitched sounds.

Related Videos

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