ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech (2207.06389v1)

Abstract: Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinder their applications to text-to-speech deployment. Through the preliminary study on diffusion model parameterization, we find that previous gradient-based TTS models require hundreds or thousands of iterations to guarantee high sample quality, which poses a challenge for accelerating sampling. In this work, we propose ProDiff, on progressive fast diffusion model for high-quality text-to-speech. Unlike previous work estimating the gradient for data density, ProDiff parameterizes the denoising model by directly predicting clean data to avoid distinct quality degradation in accelerating sampling. To tackle the model convergence challenge with decreased diffusion iterations, ProDiff reduces the data variance in the target site via knowledge distillation. Specifically, the denoising model uses the generated mel-spectrogram from an N-step DDIM teacher as the training target and distills the behavior into a new model with N/2 steps. As such, it allows the TTS model to make sharp predictions and further reduces the sampling time by orders of magnitude. Our evaluation demonstrates that ProDiff needs only 2 iterations to synthesize high-fidelity mel-spectrograms, while it maintains sample quality and diversity competitive with state-of-the-art models using hundreds of steps. ProDiff enables a sampling speed of 24x faster than real-time on a single NVIDIA 2080Ti GPU, making diffusion models practically applicable to text-to-speech synthesis deployment for the first time. Our extensive ablation studies demonstrate that each design in ProDiff is effective, and we further show that ProDiff can be easily extended to the multi-speaker setting. Audio samples are available at \url{https://ProDiff.github.io/.}

An Overview of "ProDiff: Progressive Fast Diffusion Model for High-Quality Text-to-Speech"

The paper "ProDiff: Progressive Fast Diffusion Model for High-Quality Text-to-Speech" presents an innovative approach to overcoming the limitations of conventional diffusion models in text-to-speech (TTS) synthesis. The authors focus on reducing the computational expense associated with iterative sampling processes in denoising diffusion probabilistic models (DDPMs), while maintaining high-quality and diverse speech generation.

Key Contributions

ProDiff Framework: The paper introduces ProDiff, a progressive fast diffusion model that employs a novel generator-based parameterization to synthesize mel-spectrograms with only two iterations. This makes ProDiff significantly faster compared to existing DDPM-based TTS models requiring hundreds of steps. The framework encompasses:

1. Direct Prediction of Clean Data: Unlike gradient-based methods that estimate the gradient of data density requiring extensive iterations, ProDiff predicts clean samples directly, thus accelerating sampling.
2. Knowledge Distillation: To enhance convergence with fewer diffusion steps, ProDiff uses a teacher-student model architecture. A high-step DDIM model generates target mel-spectrograms, which are then utilized to train a student model with halved iterations, facilitating sharp and efficient predictions.

Strong Numerical Results and Claims

• Speed: ProDiff demonstrates a sampling speed 24 times faster than real-time on a single NVIDIA 2080Ti GPU, which is a substantial breakthrough in the practical deployment of diffusion models for TTS applications.
• Quality and Diversity: Despite reduced iterations, ProDiff achieves sample quality and diversity comparable to state-of-the-art models using extensive steps. The subjective MOS tests reflect high perceptual quality, closely approaching that of ground truth audio.

Implications and Future Prospects

Practical Deployment: The success of ProDiff in reducing computational burden while maintaining quality enables its use in real-world applications, such as interactive voice response systems or virtual assistant technologies.

Theoretical Advancements: The approach confirms the viability of generator-based parameterization in generative tasks, expanding the scope of diffusion models beyond traditional limitations. This could prompt further research into optimizing diffusion processes for other applications, including image processing or more generalized AI functions.

Future Developments in AI: The intertwining of diffusion models with techniques like knowledge distillation paves the way for novel architectures in efficient AI modeling. Future research might explore multimodal synthesis or adaptive generation techniques that dynamically balance speed and quality according to contextual requirements.

In summary, the ProDiff model marks a substantial advancement in TTS technology, balancing the trade-off between speed and quality while setting the stage for further innovation in fast generative tasks. The work showcases how systematic analysis and targeted architectural changes can drive substantial improvements in AI applications, promising utility and flexibility in real-world deployment scenarios.