In recent years, speech synthesis has undergone a profound transformation thanks to the emergence of large-scale generative models. This evolution has led to significant advances in zero-shot speech synthesis systems, including text-to-speech (TTS), speech conversion (VC), and editing. These systems aim to generate speech by incorporating invisible speaker features from a reference audio segment during inference without requiring additional training data.
Recent advances in this domain leverage language and broadcast style models for in-context speech generation on large-scale data sets. However, due to the intrinsic mechanisms of the language and diffusion models, the generation process of these methods often involves a lot of time and computational cost.
To address the challenge of slow generation speed while maintaining high-quality speech synthesis, a team of researchers has introduced FlashSpeech as an innovative step towards efficient, zero-shot speech synthesis. This novel approach builds on recent advances in generative models, particularly the latent consistency model (LCM), which paves a promising path to accelerate inference speed.
FlashSpeech leverages the LCM and adopts the encoder of a neural audio codec to convert speech waveforms into latent vectors as a training target. To train the model efficiently, the researchers introduce adversarial consistency training, a novel technique that combines consistency and adversarial training using pre-trained speech and language 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 cue encoder, and the prosody generator, FlashSpeech achieves more diverse expressions and prosody in the generated speech.
When it comes to performance, FlashSpeech not only surpasses solid standards in audio quality, but also matches them in speaker similarity. What is truly remarkable is that it achieves this at a rate approximately 20 times faster than comparable systems, marking an unprecedented level of efficiency in zero-shot speech synthesis.
The introduction of FlashSpeech represents a significant advance in the field of zero-shot speech synthesis. By addressing the core 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 immense 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 efficient and effective zero-shot speech synthesis systems.
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Arshad is an intern at MarktechPost. He is currently pursuing his international career. Master's degree in Physics from the Indian Institute of technology Kharagpur. Understanding things down to the fundamental level leads to new discoveries that lead to the advancement of technology. He is passionate about understanding nature fundamentally with the help of tools such as mathematical models, machine learning models, and artificial intelligence.
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