VITS: Advanced Text-to-Speech System
Overview
The VITS project, proposed by Jaehyeon Kim, Jungil Kong, and Juhee Son, introduces an innovative approach to text-to-speech (TTS) technology. This system leverages a unique combination of conditional variational autoencoders and adversarial learning to enhance the performance of end-to-end TTS models. Unlike previous models that required two separate stages, VITS provides a parallel, end-to-end method, achieving more natural and impressive audio output.
Key Features
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Improved Generative Modeling: VITS incorporates variational inference with normalizing flows and adversarial training to boost the expressive capabilities of the model, ensuring it can produce high-quality audio that matches human speech.
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Stochastic Duration Predictor: A notable feature of VITS is its ability to predict speech duration stochastically, allowing it to capture the natural variations in speech rhythm and pitch. This means that the same text can be read aloud in different expressive ways, simulating human-like nuances.
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Human Evaluation: In subjective tests, VITS was evaluated using the mean opinion score (MOS) on the LJ Speech dataset, composed of recordings from a single speaker. The results demonstrated that VITS not only outperforms the best existing TTS systems but also matches the naturalness of real human speech.
Demonstration and Resources
- Audio Samples: VITS offers an online demo for users to experience the audio quality firsthand.
- Pretrained Models: For developers and researchers interested in deep diving into the technology, pretrained models are available for experimentation and further improvement.
- Interactive TTS scripts are also provided, thanks to community contributions, making it accessible via platforms like Colab Notebook.
Technical Setup
Prerequisites
- Python: The software requires Python version 3.6 or higher.
- Dependencies: All necessary Python packages are listed in a
requirements.txt
, which can be installed after cloning the project's repository. - Datasets: VITS utilizes datasets like LJ Speech and VCTK for training; it supports both single and multi-speaker configurations.
Dataset Preparation
- LJ Speech: Needs to be downloaded and extracted, then linked to the project.
- VCTK: Requires downsampling of audio files to 22050 Hz and linking similarly.
Training and Inference
To start training the VITS model on different datasets, there are configuration files and scripts provided:
- For LJ Speech, initiate training using:
python train.py -c configs/ljs_base.json -m ljs_base
- For VCTK dataset, use:
python train_ms.py -c configs/vctk_base.json -m vctk_base
Inference instructions are available in the inference.ipynb
notebook, offering a guide on how to generate audio samples using a trained model.
Conclusion
VITS presents a breakthrough in the TTS realm by combining contemporary machine learning techniques into a robust framework. Its capability to produce diverse and human-like speech patterns offers numerous applications, from virtual assistants to more immersive storytelling experiences. By sharing its resources and demonstrations openly, VITS invites further research and utilization in the TTS field.