Simple-TTS: End-to-End Text-to-Speech Synthesis with Latent Diffusion
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Primary Area: generative models
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Keywords: diffusion, latent diffusion, text-to-speech, speech generation
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TL;DR: We introduce an end-to-end text-to-speech (TTS) latent diffusion model as a simpler alternative to more complicated pipelined approaches for TTS synthesis.
Abstract: We propose an end-to-end text-to-speech (TTS) latent diffusion model as a simpler alternative to more complicated pipelined approaches for TTS synthesis. In particular, we show that one can adapt a recently proposed text-to-image diffusion architecture, U-ViT, as an excellent backbone for audio generation. We identify and explain the changes required for this adaptation and demonstrate that latent diffusion is an effective approach for end-to-end speech synthesis, without the need for phonemizers, forced aligners, or complex multi-stage pipelines. Despite its simplicity, our proposed approach, Simple-TTS, outperforms more complex models that rely on explicit alignment components and significantly outperforms the best open-source multi-speaker TTS system. We will open-source Simple-TTS upon acceptance, making it the strongest system publicly available to the community. Due to its straight-forward design, we expect that Simple-TTS can easily be adapted to many diverse TTS settings --- opening the stage to repeat the success of Stable Diffusion in computer vision, in audio generation.
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Submission Number: 6296
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