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WaveGen achieves state-of-the-art end-to-end text-to-waveform generation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced WaveGen, a novel diffusion framework for text-to-waveform generation that operates entirely end-to-end. This model bypasses the need for external components like audio codecs, vocoders, or alignment modules by jointly learning spectral and waveform trajectories. WaveGen achieves state-of-the-art results on the Emilia training dataset, demonstrating its effectiveness and scalability with model sizes ranging up to 0.6 billion parameters. AI

IMPACT This end-to-end approach could simplify and improve the quality of text-to-speech systems.

RANK_REASON The item describes a new research paper detailing a novel model for text-to-waveform generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

WaveGen achieves state-of-the-art end-to-end text-to-waveform generation

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The item describes a new research paper detailing a novel model for text-to-waveform generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    WaveGen: Truly End-to-End Waveform Generation via Internal Spectral Trajectory Learning

    WaveGen is a fully end-to-end diffusion framework for text-to-waveform generation that jointly learns spectral and waveform trajectories via Spectral Forcing without external audio codecs, vocoders, latent models, alignment modules, or self-supervised representations, achieving s…

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