In recent years, speech synthesis has undergone a profound transformation thanks to the emergence of large-scale generative models. This development has led to significant advances in head-to-head text-to-speech systems, including text-to-speech (TTS), voice conversion (VC), and editing. These systems aim to generate speech by incorporating unseen speaker features from a reference audio segment during inference without requiring additional training data.
The latest advances in this area leverage language and diffusion models for in-context speech generation on large-scale datasets. However, due to the intrinsic mechanisms of language and diffusion models, the process of generating these methods often involves considerable computational time and cost.
To address the challenge of slow generation speed while maintaining high-quality speech synthesis, a team of researchers presented FlashSpeech as a revolutionary step toward effective zero-shot speech synthesis. This new approach builds on recent advances in generative models, particularly the latent coherence model (LCM), which opens a promising avenue for accelerating inference speed.
FlashSpeech leverages LCM and adopts the encoder of a neural audio codec to convert speech waveforms into latent vectors as the training target. To effectively train the model, researchers introduce adversarial coherence training, a new technique that combines coherence and adversarial training using pre-trained speech models as discriminators.
One of the key components of FlashSpeech is the prosody generator module, which improves prosody diversity while maintaining stability. By conditioning the LCM on prior vectors obtained from a phoneme encoder, a prompt encoder, and the prosody generator, FlashSpeech allows for more diverse expressions and prosody in the generated speech.
In terms of performance, FlashSpeech not only exceeds audio quality standards, but also matches them in terms of speaker similarity. What's truly remarkable is that it achieves this at approximately 20 times the speed of comparable systems, marking an unprecedented level of efficiency in zero-shot speech synthesis.
The introduction of FlashSpeech represents a significant step forward in the field of zero-shot text-to-speech. By addressing key limitations of existing approaches and leveraging recent innovations in generative modeling, FlashSpeech presents a compelling solution for real-world applications that demand fast, high-quality speech synthesis.
With its efficient generation speed and superior performance, FlashSpeech holds great promise for a variety of applications, including virtual assistants, audio content creation, and accessibility tools. As the field continues to evolve, FlashSpeech sets a new standard for effective and efficient text-to-speech systems.
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Arshad is an intern at MarktechPost. He is currently pursuing his Int. MSc Physics from Indian Institute of Technology Kharagpur. Understanding things at a fundamental level leads to new discoveries which lead to technological advancements. He is passionate about fundamentally understanding nature using tools such as mathematical models, ML models and AI.