BareWave: New Direct Text-to-Speech System Generates Audio Waveforms Without Intermediate Steps
Researchers have developed BareWave, a text-to-speech system that generates audio waveforms directly from text without intermediate acoustic representations, using a flow-matching approach. Traditional high-quality TTS systems rely on multiple stages and intermediate representations, but this work demonstrates that direct waveform generation can achieve strong results in intelligibility, speaker similarity, and naturalness. This represents a potential simplification of TTS architecture that could improve efficiency and reduce system complexity.
BareWave is a waveform-native framework for text-to-speech synthesis that eliminates intermediate acoustic representations and separate decoding stages, instead performing direct text-to-wave generation using flow-matching techniques. The researchers identified three key training challenges in this approach: raw waveforms lack strong pretrained representations, different training stages require different noise schedules, and perceptual objectives in data space don't naturally align with velocity-space flow objectives. To address these challenges, the team developed a training framework combining representation alignment, staged noise scheduling, and velocity-aware perceptual alignment (VAPA), while maintaining a single waveform-native inference path without pretrained components. Experiments on zero-shot voice cloning demonstrate that the system achieves strong performance across intelligibility, speaker similarity, and naturalness metrics. The work is supported by audio demonstrations available on the project website.
What's missing
The paper does not provide direct quantitative comparisons with existing state-of-the-art TTS systems, instead focusing on demonstrating feasibility of the waveform-native approach. Computational efficiency metrics (inference speed, memory requirements) relative to traditional multi-stage systems are not discussed. The scope of evaluation is limited to zero-shot voice cloning; performance on other TTS tasks or with different speaker datasets is not reported.
What different sources said
- arXiv cs.AICenter
End-to-End Training for Discrete Token LLM based TTS System
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