StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models (2306.07691v2)

Abstract: In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech LLMs (SLMs) to achieve human-level TTS synthesis. StyleTTS 2 differs from its predecessor by modeling styles as a latent random variable through diffusion models to generate the most suitable style for the text without requiring reference speech, achieving efficient latent diffusion while benefiting from the diverse speech synthesis offered by diffusion models. Furthermore, we employ large pre-trained SLMs, such as WavLM, as discriminators with our novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness. StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset as judged by native English speakers. Moreover, when trained on the LibriTTS dataset, our model outperforms previous publicly available models for zero-shot speaker adaptation. This work achieves the first human-level TTS on both single and multispeaker datasets, showcasing the potential of style diffusion and adversarial training with large SLMs. The audio demos and source code are available at https://styletts2.github.io/.

A Comprehensive Overview of StyleTTS 2: Advancements in Human-Level Text-to-Speech Synthesis

The paper "StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech LLMs" introduces a novel and sophisticated model in the field of text-to-speech (TTS) synthesis. StyleTTS 2 builds upon its predecessor by integrating innovative techniques such as style diffusion and adversarial training, aiming to achieve human-like naturalness in TTS systems. This essay explores the core methodologies, empirical findings, and theoretical implications presented in the paper.

Methodologies and Innovations

Style Diffusion:

The central innovation in StyleTTS 2 is the introduction of style diffusion, which models speech styles as a latent random variable sampled via diffusion models. This approach enables the system to generate diverse and contextually appropriate styles without the need for reference speech. Unlike conventional methods, style diffusion allows for the efficient and probabilistic sampling of style vectors, contributing to the model's enhanced capability to synthesize expressive and varied speech effectively.

Adversarial Training with Large Speech LLMs (SLMs):

Another significant advancement in StyleTTS 2 is the utilization of large, pre-trained speech LLMs as discriminators in the adversarial training framework. By incorporating models such as WavLM, the paper leverages the robust feature extraction capabilities of SLMs to discern and enhance the naturalness of synthetic speech. This approach aligns generated outputs more closely with human speech in terms of prosody, emotional expressiveness, and naturalness.

Differentiable Duration Modeling:

The proposed differentiable duration upsampler addresses previous limitations in modeling speech durations. This non-parametric method facilitates end-to-end training and enables the seamless flow of gradients, ensuring the upkeep of the model's performance and stability during training, particularly in adversarial contexts.

Empirical Validation and Results

StyleTTS 2's performance on benchmark datasets such as LJSpeech and VCTK underscores its advancement in TTS synthesis. Remarkably, the model not only surpasses existing state-of-the-art systems like NaturalSpeech but even human recordings in specific metrics, showcasing its unprecedented naturalness and expressiveness. Evaluations on the LJSpeech dataset yielded a comparative mean opinion score (CMOS) of +0.28 relative to human recordings, with statistical significance ($p < 0.05$). On unseen multispeaker scenarios, StyleTTS 2 achieves human-level similarity and naturalness, establishing a new benchmark for multi-speaker TTS systems.

In zero-shot speaker adaptation tasks using the LibriTTS dataset, StyleTTS 2 exceeds performance benchmarks set by Vall-E in terms of naturalness while utilizing significantly less training data, highlighting its data efficiency.

Implications and Future Directions

The implications of StyleTTS 2 extend beyond immediate performance metrics. The innovative adoption of SLMs in adversarial training could redefine how models learn the nuances of human speech, incorporating finer aspects of natural language processing and synthesis. Moreover, style diffusion introduces a paradigm where speech synthesis can dynamically adapt to diverse linguistic inputs without extensive reference data, promising enhanced applications in interactive and adaptive AI systems.

Looking forward, promising research directions include refining the model's handling of large-scale datasets and further enhancing speaker similarity metrics, particularly in zero-shot scenarios. As StyleTTS 2 exemplifies, the combination of diffusion models and adversarial training with SLMs marks a significant leap in our quest for machines to replicate human-like speech synthesization effectively.

In sum, StyleTTS 2 represents a pertinent advancement in TTS research, providing robust methodologies and promising insights that could shape the future trajectory of speech synthesis technology. Its successful amalgamation of style diffusion and adversarial SLM training sets a high benchmark for subsequent research and application in the field of text-to-speech synthesis.