[GPT-SoVITS模型] GPT-SoVITS Speed Edition Installation, Detail Optimization, Vocal Emotion, and Training/Inference Details Tutorial

1. Preparation and Download
System requirements: GPT-SoVITS Speed Edition is only available for Windows and requires an NVIDIA graphics card (at least 6 GB); 8 GB or more RAM is recommended.
Download method: Baidu Netdisk: https://pan.baidu.com/s/1ZW0pHe4 ... =uh31#list/path=%2F
Initial setup: After downloading and extracting, users are advised to perform initial setup, including showing file extensions, adjusting graphics card settings (via NVIDIA Control Panel), and checking virtual memory settings to ensure sufficient memory during training and inference.
2. Usage Tips
Impact of reference audio: Choosing an appropriate reference audio significantly affects the speech rate and tone of the synthesized output. By listening to different reference audios, users can adjust speech rate and tone as needed.
Punctuation and paragraph segmentation: Proper use of punctuation and paragraph segmentation can control speech rate and tone. Especially when processing long texts, segmentation can prevent VRAM overflow and also improve speech naturalness.
3. Notes on Model Training
Preparing voice material: Ensure the audio in training material is clean and has low background noise to improve training quality.
Training parameter settings: Set the batch_size parameter appropriately according to VRAM size. For training material of different lengths, adjust the number of training epochs and the learning rate appropriately to achieve the best training results.
Incremental training: Both SoVITS and GPT models support incremental training, allowing users to continue training the model from the existing base to improve model performance.
Avoiding voice leakage: If voice leakage occurs after multiple synthesis runs, such as the synthesized result containing unexpected content, it is recommended to restart the inference page or try models from different epochs.
4. Synthesis and Sharing
Adjusting synthesis results: By selecting models from different epochs, users can trade off accuracy and tone to achieve the best synthesis effect.
Model sharing: If you want to share a trained model with others, simply copy the corresponding model files and prepare some prompt audio.