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F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group Relative Policy Optimization

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arxiv 2504.02407 v3 pith:PIPADSK6 submitted 2025-04-03 cs.SD eess.AS

Xiaohui Sun , Ruitong Xiao , Jianye Mo , Bowen Wu , Qun Yu , Baoxun Wang This is my paper
classification cs.SDeess.AS
keywords flow-matchingrelativef5r-ttsgrouplearningoptimizationpolicyreinforcement
verification ladder T0 review T1 audit T2 compute T3 formal
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We present F5R-TTS, a novel text-to-speech (TTS) system that integrates Group Relative Policy Optimization (GRPO) into a flow-matching based architecture. By reformulating the deterministic outputs of flow-matching TTS into probabilistic Gaussian distributions, our approach enables seamless integration of reinforcement learning algorithms. During pretraining, we train a probabilistically reformulated flow-matching based model which is derived from F5-TTS with an open-source dataset. In the subsequent reinforcement learning (RL) phase, we employ a GRPO-driven enhancement stage that leverages dual reward metrics: word error rate (WER) computed via automatic speech recognition and speaker similarity (SIM) assessed by verification models. Experimental results on zero-shot voice cloning demonstrate that F5R-TTS achieves significant improvements in both speech intelligibility (a 29.5% relative reduction in WER) and speaker similarity (a 4.6% relative increase in SIM score) compared to conventional flow-matching based TTS systems. Audio samples are available at https://frontierlabs.github.io/F5R.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FlowTTS-GRPO: Online Reinforcement Learning with Multi-Objective Reward Optimization for Flow-Matching Based Text-to-Speech

    eess.AS 2026-06 unverdicted novelty 6.0 of 10

    FlowTTS-GRPO fine-tunes open-source flow-matching TTS models with multi-objective online RL via ODE-to-SDE conversion, improving speaker similarity and quality on CosyVoice 3.0 and F5-TTS.

  2. DMOSpeech 2: Reinforcement Learning for Duration Prediction in Metric-Optimized Speech Synthesis

    eess.AS 2025-07 conditional novelty 6.0 of 10

    Reinforcement learning on duration prediction improves intelligibility and speaker similarity in a 4-step distilled text-to-speech model, and teacher-guided sampling recovers prosodic diversity.

  3. Qwen-Audio-3.0-TTS: Freely Controllable and Highly Robust Speech Synthesis with Multi-Stage Training Paradigm

    eess.AS 2026-07 conditional novelty 5.0 of 10

    Qwen-Audio-3.0-TTS claims state-of-the-art controllable multilingual text-to-speech across 16 languages and 20 Chinese dialects, using a 12.5 Hz tokenizer and multi-stage RL.

  4. Group Relative Policy Optimization for Speech Recognition

    eess.AS 2025-09 conditional novelty 5.0 of 10

    Applying GRPO with rule-based rewards to LLM-based ASR improves WER by up to 18.4% relative and reduces hallucination errors on unseen acoustic conditions.

  5. Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance

    cs.LG 2025-08 conditional novelty 4.0 of 10

    Blending a base diffusion model with its RL-finetuned version at sampling time lets users dial alignment strength, with the blend weight corresponding to the KL-regularization coefficient beta/w.

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