Same Bit Width, Different Outcomes: Post-Training Quantization of Text-to-Speech Across Architectures

arXiv:2609.28974 · eess.AS, cs.LG, cs.SD · Submitted 2026-09-24 · Read on arXiv

eess.AS, cs.LG, cs.SD

Submitted: 2026-09-24

Updated: 2026-09-24

Comments: Submitted to ICASSP 2027. 4 pages plus references. Code and run records: https://github.com/uxfacdev/tts-ptq-map

Code: https://github.com/uxfacdev/tts-ptq-map

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Quantization — 27× in this paper · explained in 12 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Core — 6× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes

Sources

• OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models
• Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling
• VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning
• No Language Left Behind: Scaling Human-Centered Machine Translation

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