Imagine the first time you heard a voice from a machine that sounded unmistakably human. The mix of wonder and disbelief, the curiosity about how far this technology could go. Fast forward to today, and we're standing on the brink of a new era with GPT-SoVITS, a tool that's not just pushing the boundaries but redefining them. This isn't just about machines talking; it's about them speaking with a voice that carries the nuance, emotion, and uniqueness of human speech. Welcome to the future of voice technology.
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GPT-SoVITS is not just a tool; it's a breakthrough in voice technology that brings several innovative features to the table. Let's explore these features in detail:
Together, these features make GPT-SoVITS a comprehensive solution for anyone looking to explore the frontiers of voice technology, from hobbyists and content creators to researchers and professionals in the field.
Setting up GPT-SoVITS requires specific preparations based on your operating system. Here's a step-by-step guide to ensure you're ready to dive into the world of advanced voice synthesis with GPT-SoVITS.
ffmpeg.exe and ffprobe.exe. These are crucial for handling multimedia files and should be placed directly in the GPT-SoVITS root directory. This allows GPT-SoVITS to process audio files seamlessly.Conda Environment Creation:
conda create -n GPTSoVits python=3.9 to create a new environment named 'GPTSoVits' with Python 3.9. This version is chosen for its compatibility with GPT-SoVITS requirements.conda activate GPTSoVits.Dependency Installation:
pip install -r requirements.txt. This command reads all the dependencies listed in the requirements.txt file and installs them in your Conda environment.FFmpeg Installation:
ffmpeg.exe and ffprobe.exe, ensure they are in the GPT-SoVITS root directory.brew install ffmpeg. This simplifies the installation process and ensures that FFmpeg integrates well with your system.Final Checks:
By following these steps, you'll have a robust environment ready for exploring the capabilities of GPT-SoVITS, whether you're on Windows or Mac. This preparation ensures that you can focus on creating and experimenting with voice synthesis without worrying about technical hiccups.
Download Prezip File:
Unzip the File:
Launch GPT-SoVITS:
go-webui.bat. This action will start the GPT-SoVITS WebUI, making the application ready for use.Install FFmpeg (if needed):
ffmpeg.exe and ffprobe.exe from a trusted source. Place these files in the root directory of GPT-SoVITS to enable audio processing functionalities.Pretrained Models:
By following these steps, Windows users can easily set up GPT-SoVITS and explore its voice cloning capabilities.
xcode-select --install in the terminal.Docker Setup:
Clone GPT-SoVITS Repository:
Navigate to GPT-SoVITS Directory:
Docker Compose:
docker-compose.yaml file within the GPT-SoVITS directory. Ensure it's configured according to your needs, adjusting environment variables and volume configurations as necessary.Launch with Docker Compose:
docker compose -f "docker-compose.yaml" up -d in your terminal. This command will start the GPT-SoVITS application within a Docker container.Accessing GPT-SoVITS:
By utilizing Docker, Mac users can overcome compatibility issues and leverage GPT-SoVITS's advanced voice synthesis capabilities without direct installation on macOS.
Here's a detailed step-by-step guide to using GPT-SoVITS with Google Colab:
Access Colab Notebook: Go to the provided Colab notebook link. Make sure you're logged into your Google account since Colab requires it. Alternatively, you can also use this Kaggle Notebook link.
Run the Notebook: Once the notebook is open, click on Runtime > Run all to start the installation process of GPT-SoVITS. This will automatically download and install the necessary software components.
Grant Permissions: During the setup, you might encounter pop-ups asking for permissions, especially for accessing Google Drive. You need to allow these to enable the notebook to interact with your Drive for storing and accessing files.
Prepare Voice Material: You need a 1-2 minute long voice recording, preferably with no background noise, in .wav format. Currently, only Chinese language training is supported.
Upload to Google Drive: In your Google Drive, create a new folder named voice_files. Inside this folder, create two subfolders: raw for the original voice files and slicer for the processed files. Upload your .wav files to the raw folder.
Run Audio Slicer: Back in the Colab notebook, locate the section for running the audio slicer, input the path to your raw folder (/content/drive/MyDrive/voice_files/raw), and execute the slicer. This will segment your audio file into smaller pieces suitable for training.
Automatic Speech Recognition (ASR): After slicing, provide the path to the slicer folder (/content/drive/MyDrive/voice_files/slicer) in the ASR section of the notebook and run the ASR process. This will transcribe the audio segments.
Verify Transcriptions: Once ASR is complete, it's essential to check the accuracy of the transcriptions. You can do this by accessing the generated .list file, which contains the transcriptions, and manually verifying them.
Organize Data: Ensure the transcribed audio files and their corresponding transcriptions are correctly formatted and organized. The expected format is usually a .list file with entries in the format: audio_file_path|speaker_name|language|transcription.
Upload Formatted Data: Upload this .list file along with the segmented audio files to the appropriate directories in your Google Drive, as specified by the Colab notebook instructions.
Initiate Training: In the Colab notebook, locate the section for fine-tuning the model. Input the necessary paths to your formatted data and start the training process.
Monitor Progress: Training might take a while, so monitor the progress through the logs provided in the Colab notebook. Ensure there are no errors, and the process completes successfully.
Setup Inference: After training, set up the inference environment in the Colab notebook by providing the paths to the trained model weights.
Generate Voice: Input the text you want to synthesize in the voice model and run the inference. The model will generate voice output based on the text provided.
Download and Compare: Download the generated audio file and compare it with the original voice material to assess the quality and similarity.
By following these steps, you should be able to use GPT-SoVITS on Google Colab for voice cloning and text-to-speech synthesis. Remember to use the technology ethically and respect copyright and privacy laws. If you find this guide helpful, consider exploring more about GPT-SoVITS and voice synthesis technologies.
To get started with GPT-SoVITS, you'll need to download and use pretrained models. These models have been trained on a vast dataset and can save you considerable time and resources.
Main Models: Download the primary pretrained models from the official GPT-SoVITS repository or model distribution page. Place these models in the pretrained_models directory within your GPT-SoVITS installation folder.
Specialized Tasks: For tasks such as vocal/accompaniment separation and reverberation removal, additional models are available. Download these specialized models and place them in the designated directories, typically within the tools or extensions folder of your GPT-SoVITS installation.
For Text-to-Speech (TTS) annotation, the dataset should be organized in a specific format. Each entry should include:
An example entry might look like: /path/to/audio.wav|John Doe|en|Hello, world!
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GPT-SoVITS is not just about cloning voices; it's equipped with advanced features that make it a versatile tool in the realm of TTS and voice synthesis:
Cross-lingual Support: GPT-SoVITS can generate speech in languages different from the training data, broadening its applicability across various linguistic contexts.
Integrated WebUI Tools: The platform includes a range of tools within its WebUI for tasks like voice separation, training set segmentation, and more, making it accessible even to beginners.
The roadmap for GPT-SoVITS includes exciting developments:
Localization: Plans for localization in Japanese and English are underway, aiming to make the tool more user-friendly across different regions.
User Guides: Comprehensive user documentation is in the pipeline to help new users navigate the platform with ease.
Dataset Fine-Tuning: Enhancements in dataset fine-tuning for Japanese and English are expected, improving the quality and naturalness of the synthesized speech.
Ask your question about GPT-SoVITS on its GitHub repo.
GPT-SoVITS is reshaping the landscape of voice cloning and TTS technology. Its ease of use, minimal data requirements for personalization, and support for multiple languages make it a powerful tool for various applications. From content creation to personalized voice assistants, GPT-SoVITS offers a gateway to innovative voice synthesis capabilities. As the platform continues to evolve, its impact on the field is poised to grow, making voice technology more accessible and versatile than ever before.
Want to explore more AI Voice Generator Tools?
Looking for a cheaper Elevenlabs Alternative?
Try out Anakin AI's AI Voice Cloning Tool! Where you can easily clone AI Voice with your own dataset!
