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笔记
GPT-SoVITS: Zero-Shot Voice Synthesis and Fine-Tuning Audio Synthesis Model
GPT-SoVITS is an innovative voice synthesis model capable of high-quality voice synthesis through short audio samples, particularly well-suited for Japanese. The model supports zero-shot and few-shot fine-tuning, achieving natural and fluent voice synthesis with extremely high audio fidelity. This article provides a detailed introduction to GPT-SoVITS's core features, architecture, usage methods, and installation and usage steps.
GPT-SoVITS Feature Overview
- Zero-shot TTS: Generate high-quality synthesized speech by inputting just 5 seconds of audio samples.
- Few-shot TTS: Fine-tune the model using 1 minute of training data to improve voice similarity and naturalness.
- Cross-language Support: Supports inference generation across different languages (including English, Japanese, and Chinese).
- WebUI Tools: Integrates audio and accompaniment separation, automatic training set segmentation, Chinese speech recognition (ASR), and text annotation, helping users easily create training datasets and build GPT/SoVITS models.
GPT-SoVITS Model Architecture
GPT-SoVITS is based on recent voice synthesis and voice conversion models, combining multiple advanced technologies:
- VITS: An end-to-end voice synthesis model that achieves efficient and natural voice synthesis through the introduction of Flow models and adversarial training processes.
- VITS2: Further optimized based on VITS, addressing the naturalness and computational efficiency issues of traditional end-to-end speech synthesis models.
- Bert-VITS2: A multilingual extended version of VITS2, combining Multilingual Bert to achieve stronger language compatibility.
- SoVITS (SoftVC VITS): Enables audio-to-audio conversion (Speech-to-Speech), suitable for application scenarios like RVC.
GPT-SoVITS has clear advantages in synthesis quality and supports zero-shot voice conversion, making it suitable for various speech synthesis needs.
Installing GPT-SoVITS
STEP1: To use GPT-SoVITS on Windows, you need to first install Anaconda. Then clone the GPT-SoVITS GitHub repository, download the pre-trained models, and install the required dependencies:
STEP2:
Then install the GPU version of PyTorch:
STEP3:
GPT-SoVITS Inference and Fine-tuning
Zero-shot Inference
In the WebUI, select 1-GPT-SoVITS-TTS for inference, input the reference audio file and text, then click "Start Inference" to get the generated audio. GPT-SoVITS will synthesize speech for the target text based on the input voice characteristics.
Few-Shot Fine-Tuning
Few-shot fine-tuning can further improve voice similarity. First, split audio files into shorter segments and generate text labels through ASR. After formatting the dataset, start the training process - after just a few training rounds, you can use the new model for high-fidelity audio synthesis.
Summary
GPT-SoVITS is a powerful voice synthesis tool that not only excels at generating natural speech, but also has unique advantages in supporting multiple languages and voice conversion. Its installation and configuration are straightforward, with relatively short inference and fine-tuning times, and it's expected to see widespread use in more application scenarios in the future.
One-click installation package for beginners here: https://pan.baidu.com/s/1I2wM4Q8n3iTzlBaSrwPkiQ?pwd=ioh0
[References]
- GPT-SoVITS Official GitHub Repository
- VITS Official GitHub Repository
- Bert-VITS2 Official GitHub Repository
[Related Tools]
- Anaconda Download
- Using GPT-SoVITS with ailia SDK
For more information or technical support, feel free to contact us.
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- 作者:Dr. Charlii
- 链接:https://www.charliiai.com/article/GPT-SoVITS
- 声明:本文采用 CC BY-NC-SA 4.0 许可协议，转载请注明出处。
