GitHub

GPT-SoVITS-WebUI

What is the project about?

GPT-SoVITS-WebUI is a powerful web-based tool for voice conversion and text-to-speech (TTS) that leverages few-shot learning. It allows users to clone voices with minimal audio data and generate speech in multiple languages.

What problem does it solve?

The project addresses the need for high-quality, personalized voice cloning and TTS with limited training data. Traditional methods often require large datasets, making it difficult for individuals or small projects to create custom voices. GPT-SoVITS-WebUI simplifies this process, enabling voice cloning and TTS with just a few seconds of audio. It also solves the problem of cross-lingual speech synthesis.

What are the features of the project?

  • Zero-shot TTS: Convert text to speech using a voice cloned from a short (5-second) audio sample.
  • Few-shot TTS: Fine-tune the model with minimal training data (about 1 minute) to improve voice similarity and realism.
  • Cross-lingual Support: Generate speech in English, Japanese, Chinese, Korean and Cantonese, even if the training data is in a different language.
  • WebUI Tools: Includes integrated tools for:
    • Voice/accompaniment separation (using UVR5).
    • Automatic training set segmentation.
    • Chinese Automatic Speech Recognition (ASR).
    • English and Japanese ASR (using Faster Whisper).
    • Text labeling.

What are the technologies used in the project?

  • Python: The primary programming language.
  • PyTorch: Deep learning framework.
  • GPT: (Generative Pre-trained Transformer) models for text generation and few-shot learning.
  • SoVITS: Singing Voice Conversion, adapted for speech.
  • VITS: Variational Inference with adversarial learning for end-to-end Text-to-Speech.
  • ContentVec: For extracting speech content representations.
  • BigVGAN: Vocoder for high-fidelity audio generation.
  • FFmpeg: For audio processing.
  • Gradio: For creating the WebUI.
  • Faster Whisper: For English and Japanese ASR.
  • FunASR: For Chinese ASR.
  • Docker: For containerization and deployment (optional).
  • Conda: For environment management.

What are the benefits of the project?

  • Ease of Use: The WebUI provides a user-friendly interface, making it accessible to users without extensive technical expertise.
  • Low Data Requirements: Voice cloning and TTS can be achieved with very small amounts of audio data.
  • Fast Training: Fine-tuning is quick, requiring only about a minute of data.
  • Multilingual: Supports multiple languages, broadening its applicability.
  • Open Source: The project is released under the MIT license, allowing for free use and modification.
  • Integrated Tools: The built-in tools streamline the process of creating training datasets and models.

What are the use cases of the project?

  • Creating custom voices for virtual assistants or chatbots.
  • Generating personalized audiobooks or podcasts.
  • Dubbing videos or games with cloned voices.
  • Developing assistive technology for individuals with speech impairments.
  • Creating unique voiceovers for creative projects.
  • Research in voice cloning and speech synthesis.
  • Preserving voices of loved ones.
  • Language learning and pronunciation practice.
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GPT-SoVITS screenshot