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Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis (2411.01156v2)

Published 2 Nov 2024 in cs.SD and eess.AS

Abstract: Text-to-Speech (TTS) systems face ongoing challenges in processing complex linguistic features, handling polyphonic expressions, and producing natural-sounding multilingual speech - capabilities that are crucial for future AI applications. In this paper, we present Fish-Speech, a novel framework that implements a serial fast-slow Dual Autoregressive (Dual-AR) architecture to enhance the stability of Grouped Finite Scalar Vector Quantization (GFSQ) in sequence generation tasks. This architecture improves codebook processing efficiency while maintaining high-fidelity outputs, making it particularly effective for AI interactions and voice cloning. Fish-Speech leverages LLMs for linguistic feature extraction, eliminating the need for traditional grapheme-to-phoneme (G2P) conversion and thereby streamlining the synthesis pipeline and enhancing multilingual support. Additionally, we developed FF-GAN through GFSQ to achieve superior compression ratios and near 100\% codebook utilization. Our approach addresses key limitations of current TTS systems while providing a foundation for more sophisticated, context-aware speech synthesis. Experimental results show that Fish-Speech significantly outperforms baseline models in handling complex linguistic scenarios and voice cloning tasks, demonstrating its potential to advance TTS technology in AI applications. The implementation is open source at \href{https://github.com/fishaudio/fish-speech}{https://github.com/fishaudio/fish-speech}.

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Citations (2)
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Summary

  • The paper introduces a novel TTS framework leveraging LLMs to bypass traditional G2P challenges for multilingual speech synthesis.
  • It employs a dual autoregressive architecture combining Slow and Fast Transformers with GFSQ to enhance codebook efficiency and output quality.
  • The framework achieves real-time processing with lower WER and higher MOS, supporting scalable applications in voice cloning and global communication.

Fish-Speech: Enhancing Multilingual Text-to-Speech Synthesis with LLMs

The paper "Fish-Speech: Leveraging LLMs for Advanced Multilingual Text-to-Speech Synthesis" introduces a sophisticated Text-to-Speech (TTS) framework that utilizes LLMs for advancing multilingual speech synthesis. The primary focus of the paper is on addressing prevailing challenges in the TTS domain, including linguistic complexity, polyphonic expressions, and the generation of natural-sounding multilingual speech. Fish-Speech innovatively circumvents the limitations characteristic of conventional grapheme-to-phoneme (G2P) conversion by integrating LLMs for direct linguistic feature extraction.

Architectural Innovations

The cornerstone of the Fish-Speech framework is the serial fast-slow dual autoregressive (Dual-AR) architecture. This model architecture addresses the instability issues typically associated with sequence generation tasks by employing grouped finite scalar vector quantization (GFSQ), thereby optimizing the balance between codebook processing efficiency and output quality. The Dual-AR architecture comprises two complementary components: a Slow Transformer, which processes global linguistic structures, and a Fast Transformer, which refines acoustic details and manages codebook embeddings. This setup enhances the model’s capability in synthesizing high-fidelity speech while maintaining computational efficiency.

Complementing the Dual-AR architecture, the researchers have developed Firefly-GAN (FF-GAN). This vocoder utilizes vector quantization strategies to optimize compression and codebook usage, achieving near 100% utilization. FF-GAN's architecture centers around improving audio quality by incorporating advanced convolutional techniques, such as depth-wise separable and dilated convolutions, designed to capture extensive receptive fields with reduced computational overhead.

Experimental Insights

Evaluations conducted within the paper indicate that Fish-Speech outperforms existing baseline models across various metrics. Notably, in voice cloning tasks, it demonstrated a significantly lower Word Error Rate (WER) compared to existing models, underscoring its superior linguistic processing capability. Perceptual evaluations confirmed that Fish-Speech excels in generating high-quality, natural-sounding speech, evidenced by substantial improvements in Mean Opinion Score (MOS) compared to other frameworks.

The implementation is noteworthy for its computational effectiveness, achieving real-time processing speeds on modern GPUs, thus making it suitable for practical applications where latency is a critical constraint. The choice of dataset—a robust corpus spanning 720,000 hours across multiple languages—has facilitated the development of a richly versatile model capable of learning and reproducing diverse linguistic and phonetic constructs.

Implications and Future Directions

The introduction of Fish-Speech holds significant implications for the future development of TTS systems and AI applications. Its ability to synthesize multilingual speech without the G2P bottleneck proposes a scalable pathway for incorporating TTS capabilities in global, multi-lingual platforms. Additionally, the open-source availability of the framework encourages further research and development, potentially leading to applications in AI-driven communication tools, voice assistants, and educational technologies.

Looking forward, the authors suggest enhancements through reinforced learning and the inclusion of varied emotional tones, aiming at further improving the model's cross-lingual robustness and emotional expressivity. The foundations laid by Fish-Speech open up new avenues for integrating TTS systems within larger AI LLMs, thus heralding a new era of interactive, speech-capable machines.

In summary, Fish-Speech represents a considerable advancement in the TTS field, positioning itself as a robust framework for future AI systems requiring nuanced and contextually-aware speech generation. It offers compelling evidence that integrating LLMs and innovative architecture can effectively address longstanding challenges within the TTS domain. As research progresses, this work holds the potential to inform and inspire subsequent innovations across both academic and industrial settings.

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GitHub

  1. GitHub - fishaudio/fish-speech: Brand new TTS solution (13,822 stars)