Flow2GAN: Hybrid Flow Matching and GAN with Multi-Resolution Network for Few-step High-Fidelity Audio Generation
Authors: Zengwei Yao
Wei Kang
Han Zhu
Liyong Guo
Lingxuan Ye
Fangjun Kuang
Weiji Zhuang
Zhaoqing Li
Zhifeng Han
Long Lin
Daniel Povey
Venue: ICLR 2026 Poster (2026)
Keywords: Flow2GAN
audio generation
Flow Matching
GAN
multi-resolution
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Existing dominant methods for audio generation include Generative Adversarial Networks (GANs) and diffusion-based methods like Flow Matching. GANs suffer from slow convergence during training, while diffusion methods require multi-step inference that introduces considerable computational overhead. In this work, we introduce Flow2GAN, a two-stage framework that combines Flow Matching training for learning generative capabilities with GAN fine-tuning for efficient few-step inference. Specifically, given audio's unique properties, we first improve Flow Matching for audio modeling through: 1) reformulating the objective as endpoint estimation, avoiding velocity estimation difficulties when involving empty regions; 2) applying spectral energy-based loss scaling to emphasize perceptually salient quieter regions. Building on these Flow Matching adaptations, we demonstrate that a further stage of lightweight GAN fine-tuning enables us to obtain few-step (e.g., 1/2/4 steps) generators that produce high-quality audio. In addition, we develop a multi-branch network architecture that processes Fourier coefficients at different time-frequency resolutions, which improves the modeling capabilities compared to prior single-resolution designs. Experimental results indicate that our Flow2GAN delivers high-fidelity audio generation from Mel-spectrograms or discrete audio tokens, achieving highly favorable quality-efficiency trade-offs compared to existing state-of-the-art GAN-based and Flow Matching-based methods. Online demo samples are available at \url{https://flow2gan.github.io}, and the source code is released at \url{https://github.com/k2-fsa/Flow2GAN}.

Published as a conference paper at ICLR 2026
FLOW2GAN: HYBRID FLOW MATCHING AND GAN WITH MULTI-RESOLUTION NETWORK FOR FEW-STEP HIGH-FIDELITY AUDIO GENERATION
Zengwei Yao, Wei Kang, Han Zhu, Liyong Guo, Lingxuan Ye, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Long Lin, Daniel Povey Xiaomi Corp., Beijing, China dpoveye x aomi. com
ABSTRACT
Existing dominant methods for audio generation include Generative Adversarial Networks (GANs) and diffusion- -based methods like Flow Matching. GANs suf- fer from slow convergence during training, while diffusion methods require multi- step inference that introduces considerable computational overhead. In this work, we introduce Flow2GAN, two-stage framework that combines Flow Matching training for learning generative capabilities with GAN fine-tuning for efficient few-step inference. Specifically, given audio's unique properties, we first improve Flow Matching for audio modeling through: 1) reformulating the objective as end- point estimation, avoiding velocity estimation difficulties when involving empty regions; 2) applying spectral energy-based loss scaling emphasize perceptually salient quieter regions. Building on these Flow Matching adaptations, we demon- strate that further stage of lightweight GAN fine-tuning enables us obtain few- step (e.g.. 1/2/4 steps) generators that produce high- high-quality audio. In addition, we develop a multi-branch network architecture that processes Fourier coefficients at different time-frequency resolutions, which improves the modeling capabilities compared prior single-resolution designs. Experimental results indicate that Flow2GAN delivers high-fidelity audio generation from Mel-spectrograms or dis- crete audio tokens, achieving highly favorable quality-efficiency trade-offs com- pared to existing state-of ofte-oo--hherrr GAN-based and Flow Matching based methods. Online demo samples are available athttts::// .itp:aaggn..gttui.u thub 1o and the source code is released at
com/k2 fsw/flow2GAI
INTRODUCTION
Audio generation, also known neural vocoding, aims to reconstruct high-resolution waveforms from compressed representations such as -ceestcctrs and discrete audio tokens. It serves as
crucial component in various applications, including text-to-speech (TTS) (Shen et al.) 2018 Kim
let al.
2021 Zhu et al.) 2025) and music synthesis (Engel et al., [0177] Donahue et al. 2018 Engel al. 2019) directly impacting the perceptual experience of end users.
Deep generative models have demonstrated remarkable progress in this task. Generative Adversarial Networks (GANs) (Goodfellow al. 2014 Arjovsky et al. (017) are the dominant approach (Ku-
mar et al.] 2019 Kong et al. 2020aa Lee et al., 2022; Siuzdak 2023), leveraging sophisticated
discriminators (Kumar et al. 2019 Kong et al. 2220aa Jang et al. 2021 Gu al. 2024) cap- ture multi-grained audio details and enabling high-fidlityy results with efficient one-step inference.
However, adversarial optimization in GANs often leads to slow convergence and the risk of mode collapse (Thanh-Tung Tung & Tran 2020). Another line of research explores the use of diffusion mod- els (Sohl-Dickstein et al.
2015 Ho et al. 2020 Song et al. 2020 for audio generation (Kong et al.) Lee et al. 2021| Nguyen al. 2024), offering stable training and strong generative performance. Recent diffusion based works that integrate multi- band waveform process- ing (San Roman et al. 2023] Liu al. 2024) further boost diffusion audio data, enabling them to generate high-quality audio. Nevertheless, the strong results of diffusion models come at the cost of multi-step sampling, resulting in high computational demands.
2020b Chen et al.| 2020

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In this work, propose Flow2GAN, which follows two-stage training strategy that combines the strengths of both GAN and diffusion paradigms. In the first stage, we train the model with a Flow Matching objective (Lipman et al.; 2022 Liu et al.. 2022a). a form of diffusion model (Kingmal & Gao 2023), to learn robust generative capabilities with stable training. In the second stage, we construct few-step generators from the trained model and utilize GAN fine-tuning for finer-grained generation through adversarial learning. To be specific, considering audio's 's distinctive properties, we improve Flow Matching for audio modeling by: 1) reformulating the training objective as direct endpoint prediction, circumventing the challenges of velocity estimation when involving empty re- gions: 2) incorporating spectral energy- based loss weighting to emphasize perceptually important low-energy regions. Building upon the adapted Flow Matching, the trained model is able to generate clearly improved audio with just 2 sampling steps (Figure 2) This provides strong foundation for the subsequent GAN fine-tuning stage, which effectively guides the initialized few-step models to- ward further refined audio quality. Moreover, we design a multi-branch Con-Nexttbased (Liu et al.) 2022b) network structure that operates multi-resolution Fourier coefficients, serving as a power- ful backbone with enhanced modeling capabilities compared to the previous single-brane version. Our experiments show that Flow2GAN generates high-quality audio across both Mel-spectrogram and audio token conditioning, while providing highly favorable quality-efficiency trade-offs com- pared to sfte-of-the-art GAN-based and Flow Matching-basee methods. Ablation studies further validate the effectiveness of each proposed component.
2 RELATED WORK
GAN-based audio generation. GAN-based models consist of two networks: a generator that pro- duces audio waveforms and a discriminator that classifies whether the generated audio real or synthetic. The key to these methods is designing sophisticated discriminators to capture audio fea- tures from different perspectives, thereby guiding the generator to produce more fine-grained and realistic audio through adversarial learning. MelGAN (Kumar et al. 2019) employs Multi-Scale Discriminator (MSD) that extracts features from waveforms at various downsampled resolutions. HiFi-GAN (Kong et al. (2000a) extends this approach by incorporating
Multi-Perioo Discriminator (MPD) that learns complementary structures by analyzing different periodic patterns. Univnet (Jang et al. 2021 utilizes Multi- Resoluionn Diseriminator (MRD) that operates in the time-frequency domain across multiple spectral resolutions. BigVGAN (Lee et al., 2022) introduces the Snake acti- vation function to provide periodic inductive bias and incorporates low-pass filters for anti-aliasing. Unlike previous works that rely on transposed convolutions for upsampling, Vocos (Siuzdak) 2023) models spectral coefficients using ConvNeXt model (Liu al., (202b)) and realizes upsampling to waveforms through the Inverse Short-Time Fourier Transform (ISTFT). Our network structure fol- lows Vocos by using ConvNeXt to operate on spectral coefficients, but we extend it to multiple time-frequency resolutions for a more powerful backbone.
Diffusion/Flow Matching-based audio generation. Diffusion-basse methods learn transition tra- jectories from noise distribution to data distribution, excelling at stable training and robust gener- ation. DiffWave (Kong et al. 2020b) and WaveGrad (Chen et al. 2020) first introduce diffusion models to audio synthesis and get GAN-competitive results. Prior Grad (Lee et al.] (02T) uses an adaptive prior derived from data statistics to structure the noise, thereby speeding up the diffusion sampling process. PeviodWave (Lee ct al. 2024a) utilizes a multi-period Flow Matching estimator to learn different periodic features of the waveform. Multi-Band Diffusion (MBD) (San Roman et al., 2023) divides the audio into different frequency bands and processes each band with distinct diffusion model. RFWave (Liu et al. 2024) also adopt this multi-band idea, employing Flow Match- ing model each band but processing all bands in parallel with a shared model. These multi-band processing approaches employ frequency equalization to reduce the energy mismatch between the Gaussian distribution and audio data distribution in different frequency bands. While this technique is effective for high-frequee -egh-frequency modeling, the equalization process relies on normalization statistics calculated on the dataset (San Roman et al. 2023) or accumulated during training (Liu al. 2024), which poses potential generalization risk when applied to distributions that differ significantly from those used to compute the original statistics. In this work, we improve Flow Matching for audio modeling by reformulating the objective to endpoint estimation and emphasizing loss in per- ceptually salient regions, and further combine a GAN fine-tuning stage for fast and finer- grained generation. Note that PriorGrad and RFWave also energy-based loss scaling (Liu et al.) 20244) but they operates at the per- per-frame level, whereas ours is based on both time and frequency dimen- sions, which is validated to outperform the per-frame version in our reformulated setting (Table 3).
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Diseriminators
Real/Fake
Paats
Matching endpoint estimator 8a(3, t(c)
Inicialize
generator G2 (g(gle
Met-spectrugramms
discrete andio tokens
Mel-specrograns discrete audio tokens
Xg - Paotre
1)xo tx,
Ip Pasice
X1 PAntn
Stage-1:Flow Matching training
Stage-2: GAN flae-tuning
Figure I: Overall framework of Flow2GAN.
Accelerating Flow Matching models audio generation. typical way to accelerate diffusion model inference is through distillation (Salimans & Ho 2022, Meng et al. 2023 Song al. (023),
using multi-step outputs to guide fewer-step outputs. WaveFM (Luo et al. (025) introduces a tai- lored method of consistency distillation (Song et al.) (2023) and can generate audio in 1 step without significant quality degradations. Another approach is to integrate GAN objectives with adversarial learning using real data (Huang al. [2023] Lee et 2024b) Zheng & Yang 2025). A recent work
in audio generation is PeriodWave- Turbo (Lee et al.) 2024b). which introduces GAN fine-tuning to Flow-Matching pre-trained models. Our Flow2GAN shares similar idea, but we build upon our enhanced Flow Matching specialized for audio modeling, providing stronger pretrained weights to initialize the GAN generators. This results in significantly superior one-step generator after GAN fine-tuning compared to building upon standard Flow Matching (Table (3). Notably, Period Turbo (Lee et al., 2024b) does not explore one-step results. Shortcut models (Frans et al.) 2024) condition on both noise level and step size, enabling better few-step generation than consistency models (Song et al. 2023). We also investigate this strategy for few-step audio generation and demonstrate that our approach surpasses it.
3
PRELIMINARY: FLOW MATCHING
As a popular formulation of diffusion models (Sohl-Dickstein et al. 2015 Ho et al. 2020 Song 2020 Rezende Mohamed 2015; Kingma & Gaol 2023). Flow Matching (Lipman et al. Liu et al. 2022a) addresses generative modeling by learning the velocity fields that trans- port the noise distribution Pnoise (0, I) to the data distribution Praza- Given a noise point a0 Proise data point
al.
2022
Paasas and Ld[0, 1], straight flow path can be defined by lin- ear interpolation: Zt = (1 1)zr + Lx1, with corresponding velocity V; = 21 To- The same intermediate point 2, can arise from different pairs of (æo, 21 ). each yielding distinct velocity et- Flow Matching thus trains a network fo (zt, tt to estimate the marginal velocity field (Lipman et al., 2022]. that is, the expected velocity conditioned on xzz v(22, t) Ex.... [My [v]] In practice, this is equivalently optimized in a simpler way by fitting the empirical velocity 2t obtained from randomly sampled pairs (Ir; .i) during training (Lipman al. 2022):
LFM Ee, [I (2,, t) v.
(1)
In inference, new data can be sampled by solving the ordinary differential equation (ODE) using the leamned flow model: de. = fo t). This process is typically approximated by numerical solver, such as Euler method, over discrete time steps:
Piams Dt, + (ti+1 t.)fo(ze,,)))
4 METHOD
As illustrated in Figure Flow2GAN follows two-stage training strategy: the model is first trained with improved Flow Matching tailored for audio modeling (Section [41); then few-step generators are built with the trained model and guided by GAN fine-tuning toward fine-grained generation (Section 4.2).. The model utilizes bulti-branch architecture that processes Fourier coefficients at different tume-frequency resolutions (Section 4.3)
3

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( Ground truth
(b) Noise
(c) Velocity V.
(0) Slandard Flow Matching, 2 (e) Improved Flow Matching, steps
(1 GAN line-luned 2-step model
Figure 2: Example illustration of audio generation. Flow Matching estimates velocity v, 21 20- The improved Flow Matching in Flow2GAN estimates endpoint 21, yielding cleaner result in silent regions with 2 sampling steps. GAN fine-tuning further enhances the result by filling in details.
4.1 IMPROVED FLOW MATCHING FOR AUDIO MODELING
When applying Flow Matching to conditional audio generation, the velocity field is parameterized as fo(re, & c). where с denotes the compressed acoustic representations, such as Mel-spectrograms or discrete audio tokens. However, the unique characteristics of audio data present two key challenges for Flow Matching in this context: 1) Velocity estimation difficulty. Audio signals often contain silent segments frequency bands with zero energy (iggure((a)) In these regions, the model must accurately estimate et to precisely cancel out the noise in to and recover the empty target æ This increases the learning difficulty as the model is required not only capture the differences 12 T1 in the active regions of the audio signal, but also to approximate -2g in these empty regions (Figure 2 (c)). 2) Loss-perception mismatch. The Flow Matching loss (Equation []] uses mean squared стror (MSE) criterion, treating the prediction errors uniformly across all signal regions. However, this does not align well with auditory perception principles, where errors in quieter spectral regions tend to be more perceptually noticeable than equivalent errors in louder regions.
Reformulation to endpoint estimation. To circumvent the difficulty of predicting the velocity Ut. we reformulate the Flow Matching problem so that the network predicts the endpoint ø1 instead: f (0(2,,t/c). Specifically, 2 -1-2% the loss in Equation can be rewritten as:
go (x f/c) - Ft 31 Ft
27
I190 (0,s,,////c tlc) - 1112
LFM B1
(3)
1 1
(1 t)2
This reformulation can be interpreted as training the network to reconstruct the clean audio from its noisy version F, at varying noise levels (1 t). This approach provides more consistent and stable learning objective tailored to audio data, allowing the network to focus on capturing audio-relevant patterns regardless of whether the region is active or empty. Empirically, we found that removing the weighting factor [I-t)= in Equation 3 yields slight improvements at both the Flow-Matching stage steps) and after GAN fine-tuning. The reason might be that without this weighting, the model can better emphasize small t values, which aligns well with our setting. For simplicity, therefore omit this factor, yielding:
CPM Er, (1,, llc) x
The sampling process in Equation
is consequently modified as follows:
Ftam = Tr, + (f:+1 -t,) 10 (T.. (:/c) dz,
(5)
1-
Spectral energy-adaptive loss scaling, To address the (oss-perception mismatch, we scale the Flow Matching loss inversely proportional to the energy of the reference spectrogram, thereby encourag- ing the model to emphasize the perceptually salient quieter regions. Specifically, the prediction error converted into frequency domain: (1), t|c) æ1). where S(z) (anFF(((TF()))) notes the power STFT followed by a linear filterbank transformation for energy smoothing, and is differentiable. The obtained result then scaled element-wise inversely the square root of the
de-

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reference spectrogram's 's energy, VS(m))+ redefined as:
with 1c 7. Therefore, the loss in Equation is
((g) x1)
CFM 20,
(6)
(
4.
We clamp the loss scale (s(a))+
between 0.01 and 100 for training stability. See Appendix Ta-
ble 10 for detailed configurations of S(z). Note that unlike the per-frame energy-based scaling in previous works (Lee et al,, 202T; Liu et al. 2024), our scaling strategy more comprehensive as also accounts for differences along the frequency dimension.
4.2 GAN FINE-TUNING FOR REFINEMENT AND FEW-STEP INFERENCE
Although the adaptations described in Section 4.1 improve the audio generation quality of the Flow Matching model with 2 sampling steps (see Figure (d) (e) and Table B. the inherent complex- ity of audio generation still leaves room for further refinement of details, especially in the high- frequency range. Moreover, even though we can obtain higher quality results through multi-step sampling (see Table 9 in Appendix), this would require considerable computational overhead. To this end, we employ a GAN fine-tuning strategy for two purposes: enhancing the fine-grained audio details by leveraging the existing carefully designed discriminators (Kong et al. 2020a Jang et al.) 202T) that capture diverse audio patterns from multiple perspectives, and enabling high-fidelity gen- eration with few-step (even only one-step) inference.
We construct the few-step GAN generators, referred to as G (Jooe)) by forwarding the trained Flow Matching model through N steps following Equation 5 Note that generators with different steps are trained and deployed as separate models rather than a single unified model. Specifically, backpropagated through both forward passes, enabling
for GØ (z. c) with N > 1, gradients end-to-end optimization of the intermediate output from earlier steps. While this approach can extend more steps, focus on one-, two-, and four-step variants for computational efficiency. Following (Siuzdak 2023), we adopt the multi-period discriminator (MPD) (Kong et al., 2020a) and the multi-resolution discriminator (MRD) (Jang al. 2021) for adversarial training. The generators are fine-tuned using combination of HingeGAN ad advessaiial loss (Lim & Ye, (017). L1 feature matching loss, and multi-scale L1 Mel-spectrogram reconstruction loss (Kumar et al.; 2023).
We found that fast stage of GAN fine-tuning is sufficient to yield rapid and substantial im- provement audio quality, while additional fine-tuning can provide further gains (see Table 4 Figure 2 (e) to (f) shows that the fine-grained details are recovered after GAN fine-tuning. Intu- itively, this effect arises because the pre-training stage with the improved Flow Matching objective already equips the model with strong generative modeling capabilities, while subsequent fine-tuning efficiently guides the model toward high-fidelity output by iteratively refining details through ad- versarial learning with real audio samples. As shown in Table 3 it is worth noting that when using standard Flow Matching for pre-training like (Lee et al.] (2024)). the one-step model achieves sig- nificantly lower performance after GAN mun-tuning, demonstrating the superiority of our improved Flow Matching objective. This strategy combines the strengths of both paradigms improved Flow Matching for robust generative capability learning, and GAN for detail enhancement ultimately en- abling high-fidelity audio generation with fast inference. Notably, compared to pure GAN training, it also requires significantly lower training cost.
4.3 NULTI-RESOLUTION NETWORK STRUCTURE
Inspired by Vocos (Siuzdak) 2023). our model processes Fourier coefficients rather than waveform samples for saving computational cost and memory usage. While Vocos operates single time- frequency resolution, we extend to multi-resolution modeling to better capture audio complexity, providing a more powerful backbone for generative learning.
As shown in Figure 3 our model comprises three branches, each processing Fourier coefficients at different resolutions. Specifically, in each branch, the input signal is first transformed via STFT to obtain complex Fourier coefficients, whose real and imaginary components the feature dimension and fed into a ConvNeXT model (Liu et al.; (0226))))) to produce output complex coefficients. These then converted back to waveform domain via Inverse STFT (ISTFT). The
concatenated along
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Z~ 21001 1]
16TFT
ISTFT;
ISTFT;
ConYNeXi
ConyNeXi
Neny
STFT:
STFT;
Melogretrogramssco discrete audio tokens
Figure 3: Multi-resolution network structure. Our model processes spectral coefficients from STFT at three different time-frequency resolutions, using larger embedding dimensions for shorter quences. A ConvNeXt-based encoder processes the conditional compressed representation.
final output is obtained by summing all branch outputs. Since both STFT and ISTFT operations are differentiable, the entire model is optimized end-to-end. Additionally, following (Lee et al. 2024a), we incorporate a ConvNeXt-bail -baeed condition encoder extract deeper features from input Mel- spectrograms or codec token embeddings. which serve as shared generation conditions across all branches. Note that Flow Matching inference, this condition encoder requires only one forward pass, with output features reused across multiple sampling steps. To better balance performance and efficiency, we use larger embedding dimensions for the low-frame-ratt branch, and smaller embedding dimensions for the two high-frame-rate branches. Detailed model configurations provided in Appendix 10
5 EXPERIMENTS
To evaluate the effectiveness of Flow2GAN, we conduct experiments conditioned on Mel- spectrograms and discrete audio tokens from Encodec (Défossez et al.) (022) (Section (.2). We also perform ablation studies with Mel-spectrogram condition to investigate the effect of each pro- posed technique (Section (53) and compare inference speed with other models (Section 5.4). Finally,
evaluate performance when serving TTS vocoder (Section 5.5
5.1 EXPERIMENTAL SETUP
Datasets and evaluation metrics. For Mel-spectrogram conditioning, we train models on the Lib- iITTS dataset (Zen al. 2019), which contains 585 hours of English speech at 24kHz sampling rate. We report results on the test set for model comparisons and on the dev set for ablation stud- ies. For objective evaluation,
Perceptual Evaluation of Speech Quality (PESQ) (Rix al.) 200T], ViSQOL (Chinen et al.) 2020), F1 score for voiced/unvoiced classification (V/UV FI), peri- odicity error (Periodicity). We also include Fréchet Speech Distance (FSD) (Le al. 2023), which measures the similarity between distributions of generated and real samples in a feature space from Wav2Vec2 encoder (Baevski et al. 2020). For subjective evaluation, we use Similarity MOS (SMOS) between generated and reference audios, with scores from (not at all similar) to (ex- tremely similar), and also 5-scale Mean Opinion Score (MOS) evaluate naturalness with scores from (completely unnatural) to 5 (completely natural). We compare against the following state- of-the-art models: BigVGAN (Lee et al., 2022), Vocos (Siuzdak) 2023). RFWave (Liu et al.) 2024), BeroodWave-Tuute (Lee et al. 2024b), and WaveFM (Luo et al., 2025 In ablation studies, we fo- cus on PESQ and ViSQOL. We use log-Mel spectrograms when comparing to other models and Mel spectrograms in ablation studies, with their results comparison presented in Appendix Table II
For audio token conditioning. following (San Roman et al., 2023 Liu et al. 2024), we train on a set of universal audio datasets including: Common Voice 7.0 (Ardila et al. 2019). clean speech data from DNS Challenge 4 (Dubey et al. 2024) (speech), MTG-Jamendo (Bogdanov et al. 2019) (music), and AudioSet (Gemmeke et al. (017)) and FSD50K (Fonseca al. (222]) (sound events). All audio files are resampled to 24kHz. Following (Liu et 2024), we evaluate on unified test datase ! which contains 900 test samples involving speech, vocals, and sound effect. For the
hhtps://gtthu..co//hti 18/ffavve.

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universal audios, report PESQ, ViSQOL and FSD objective metrics, as well SMOS and MOS as subjective metric. In this setting, we compare Flow2GAN against Encodec (Défossez et al.
2022
MBD (San Roman et al. 2023), RFWave (Liu et al. 2024). and PeriodWave-Tuubo (Lee
et al.
2024b).
To evaluate Flow2GAN as vocoder in Mel-based TTS systems, combine it with F5-TTS Base (Chen et al. 2025). recent zero-shot TTS model, and compare against various state-of-the vocoders. Note that F5-TTS provides Iwo official checkpoints corresponding to librosa and torchau- dio mel-spectrogram implementations 2
We evaluate zero-shot TTS performance on LibriSpeech- PC (Meister et al., 2023) test-clean subset. For intelligibillit, we report WER between the synthe- sized speech transcription and the input text. Speaker similarity is measured using cosine similarity between Wavl M-based (Chen et al. 2022) ECAPA-TINN (Desplanques et al. 2020) embeddings of the prompt and synthesized speech (denoted as SIM-o (Le et al. (023)). Naturalness is evaluated using network-basee MOS (UTMOS) (Saeki et al. 2022).
Implementation details. Our Flow2GAN is built with ConvNeXt (Liu et al., 2022b) layers. but we replace the normalization with BiasNorm (Yao et al. 2024) and use PreLU activation (He et al.) 2013). Detailed model configurations are provided in Appendix Table [T] In GAN fine-tuning stage, following (Kumar et al. 2023), we use multi-scale Mel-spectrogram reconstruction loss with window lengths of {32, 64, 128, 256, 512, 1024, 2048). hop lengths set window length / 4, and Mel bins of (5, 10, 20, 40, 80, 160, 320}. For Encodec audio token conditioning, during training we randomly choose bandwidth from (12.0, 6.0, 6.0,3.0, 1.5) khps. At inference, the unified model can support all these bandwidths. We train our models with ScaledAdam optimizer (Yao et al.) 2024). which offers faster convergence. Mel-spectrogram conditioned models are trained for 92k Flow Matching iterations and 110k GAN fine-tuning iterations on 2 Nvidia H20 GPUs. Audio token conditioned models are trained for 180k Flow Matching iterations H20 GPUs) and 190k GAN fine-tuning iterations (2 H20 GPUs). For the thtte-t models for comparison, we use their released checkpoints and recommended inference configurations.
5.2
COMPARISON TO STATE-OF- OF- METHODS
Mel-spectrogram conditioning. Table compares our Flow2GAN models with other state-of-the- art models with Mel-spectrogram conditioning. The one-step Flow2GAN outperforms Vocos (Siuz- dak, 2023). RFWave (Liu et al., 2024),, and WaveFM (Luo et al. 2025) across most metrics, except PESQ. The two-step and four-step variants outperform all other models except for FSD, SMOS, and MOS. The four-step version achieves the best results in PESQ, ViSQOL, V/UV FI, and Periodic- ity. As BigVGAN-v2 (Lee et al. 2022) is trained on larger dataset demonstrates the effectiveness of our models.
achieving results close to it
Table 1: Comparison to state-offthe-art methods on LibriTTS test set with Mel-spectrogram condi-
tioning. -BigVGAN-v2 is trained on large-scale dataset.
Model
Params
PESQ
ViSQOLt
V/UV
Periodicity
FSD.
SMOSt
MOSt
BigVGAN
112.4
4.241
4,964
0.969
0.071
0.022
4.47 - 0.10
4.33
+ 0.18
BigVGAN-v2"
112.4
4.379
4.971
0.978
0.055
0.014
4.65
: 0.11
4.59
1 0.10
Vocos
13.5
3.618
4.898
0.951
0.105
0.042
4,10
1 0.17
4,38
1. 0.16
RFWave (10 steps)
18.1
4.220
4,772
0.957
0.098
0.412
4.14+0016
4.29
0.13
PeriodWave (4 steps)
70.2
4.434
4,965
0.958
0.096
0.020
4,20 1 0.17
4,38
1 0.17
WaveFM (1 step)
19.5
3.540
4,894
0.943
0.124
0.098
3.72 0.18
3,76
+ 0.18
Flow2GAN, 1-step (ours)
78.9
4.189
4.957
0.975
0.063
0.028
4.44
4,39
+ 0.15
Flow2GAN, 2-step (ours)
78.9
4.440
4.979
0.983
0.044
0.023
4.53 1 0.13
4.56
1: 0.11
Flow2GAN, 4 step (ours)
78.9
0.037
0.016
4.60
Encodec audio token conditioning. Table 2 presents a comparison of Flow2GAN models with
other state-of-the-ard models conditioned on EnCodec audio tokens. At lower bandwidths of 1.5 and 3.0 kbps, all Flow2GAN variants outperform other models across most objective metrics, except that the step and two-step models underperform PeriodWave- 3.0 kbps. At higher bandwidths of 6.0 and 12.0 kbps, although PeriodWave Turbo achieves the best results in PESQ and ViSQOL, all metrics, with the four-step Flow2GAN achieving notably better FSD results than PeriodWave-Turee With increasing bandwidth, all Flow2GAN models can consistently achieve better SMOS and MOS.
(Lee al., (024b) in ViSQOL at
variants outperform other models across all objective

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Table Comparison state-of-the-art methods on universal evaluation dataset with Encodec audio
token conditioning.
Bandwidth (kbps) Model
Purams (M)
PESQ
ViSQOL1
FSD
SMOSt
MOS1
EnCodec
56.0
1.368
1,996
0.27
MBD
411.0
1.457
3,030
7.734
2.67 0.18
2.83 + 0.28
RFWave (10-step)
18.1
1.600
3.272
2.986
2.87 + 0.23
2.91 + 0.23
PeriodWave-Ture (4-step)
31.3
1.260
3.308
4.055
1.55 1.55 0.16
1.47 0
Flow2GAN, 1-step (ours)
78.9
1.739
3.582
1.210
=
2.83 0.21
How2(IAN, 2-step (ours)
78.9
1,803
3.609
1.152
3.04 + 0.20
3.86 0.14
Flow2GAN, 4-step (ours)
78.9
1.925
3.662
1.069
0.19 3.17
3.40 0.18
EnCodec
56.0
1.745
3.867
1,380
3.11 0.26
3.31 0.23
MBD
411.0
1,958
3.523
6,133
3.36
4.00 0.24
RFWave (10-step)
18.1
2.124
3.784
2.622
3.41 + 0.21
3.86 + 0.23
PeriodWave-TTume Turbo (4-step)
31.3
2.160
4,058
1,018
3.04 + 0.17
3.16 0.23
Flow2GAN, 1-step (ours)
78.9
2.353
4,026
0.867
3,94
4.00 +
Flow2GAN, 2-step (ours)
78.9
2.442
4.049
0,843
4.19 = 0.15
4.07 0.224
Flow2GAN, 4-step (ours)
78.9
2.550
4,091
0.804
0.16 4.03
4.08 0.22
6.0
EnCodec
56.0
2.259
4.156
1,239
3.77 + 0.19
3.47 0.21
MBD
411.0
2.094
3,690
6.177
3.15 0.19
4.47 0.21
RFWave (10-step)
18.1
2.663
4,120
2.574
3.68 = 0.22
4,00
PeriodWave Turbo (4-step)
31.3
3,229
4,424
0.712
4.00
0.17
4,40 0.21
Flow2GAN, 1-step (ours)
78.9
2.904
4.300
0.696
4.46
4,42 0,22
How2GIAN, 2-step (ours)
78.9
2.983
4.319
0.733
4.38 + 0.14
4.31 0.17
Flow2GAN, 4-step (ours)
78.9
3.089
4,351
0.678
4.19 = 0.12
4,38 0.13
12.0
EnCodec
56.0
2.776
4,347
1,204
3.76 0.15
4.15
MBD
RFWave (10-step)
18.1
3.117
4.348
2.152
3,75 = 0.18
4.13 0.23
PeriodWave Turbo (4-step)
31.3
3,579
4,544
0.776
4.32
4,56 4.56
Flow2GAN, 1-step (ours)
78.9
3.389
0.632
4.52
4.53 0.23
Flow2GAN, 2-step (ours)
78.9
3.457
4.505
0.600
4.39
4.44 0.28
Flow2GAN, 4-step (ours)
78.9
3.538
4,531
0,557
4.22 0.19
4.50 0.15
5.3
ABLATION STUDIES
Ablation of the improved Flow Matching. Table presents ablation results for the Flow Matching stage in Flow2GAN with Mel-spectrogram conditioning. Since we aim for few-step generation after GAN fine-tuning, we focus on the 2-step sampling results for the Flow Matching model. We don't 't compare one-step Flow Matching results as they tend to converge toward the dataset mean (Frans al. 2024). The results indicate that reformulating the standard Flow Matching objective to end- point prediction improves PESQ by 0.455 and also yields better results after GAN fine-tuning (from Equation to Equation 4. Additionally, the proposed spectral mergy-adaptinn loss scaling (Equa- tion (6) further improves PESQ and ViSQOL substantially and consistently both stages. Per-frame energy-based scaling, in (Lee al. 2021 Liu et al., (024), leads to clear performance drops in both stages, demonstrating the advantages of our energy-based scaling strategy across time and frequency dimensions. Maintaining the factor TT- in Equation yields worse results, em- phasizes larger values, which not suitable for our few-step goal. The consistent improvements across both stages confirm that our Flow Matching enhancements contribute to the final performance gains, distinguishing our approach from Pave Turbo (Lee et al. 2024b), which uses standard Flow Matching for pre-training.
Table 3: Ablation study of the improved Flow Matching on LibriTTS dev set with Mel-spectrogram conditioning.
Flow Matching trained
GAN fine-tuned
Method
steps)
1-step
2-step
PESQt
ViSQOL
PESQ
ViSQOL1
PESQ1
ViSQOL
Standard Flow Matching
2,351
3.691
3.730
4.853
4.257
4.933
Predict æ1, w/o loss scaling
2,806
3.691
4.173
4.912
4.332
4.937
Predict æi, w/ per-frame loss scaling
3,140
3.667
4.201
4.910
4.388
4.942
Predict æ1, w/ loss scaling, w/ loss factor 1y-
3,226
3.726
4.195
4.932
4.430
4.962
Predict Z1, w/ loss scaling (final)
3.469
3.749
4.303
4.942
4.471
4.969
Comparison to pure GAN training. Table ing paradigm. Pure GAN training converges slowly. whereas Flow2GAN achieves significantly better results with lower training costs by combining Flow Matching training with subsequent GAN
demonstrates A
the effectiveness of our two-stage train-
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fine-tuning. Notably, even fast GAN fine-tuning with 11k iterations yields high-quality generations for both the one-step and two-step generators, validating the benefits of this training strategy.
Table Comparison pure GAN training on LibriTTS dev set with Mel-spectrogram conditioning.
Method
Training iterations
Training hours
PESQT
ViSQOL
Pure GAN
110k
26
3.587
4.823
220k
53
3.770
4.838
330k
78
3,838
4,861
660k
156
3,919
4,888
Flow Matching trained (2 steps)
92k
3,469
3.749
GAN fine-tuned, 1-step
+11k
+2.6
4.195
4.887
+55k
+13
4.286
4.933
+110k
+26
4,303
4,942
GAN fine-tuned, 2-step
+IIk
+3.2
4.375
4,917
+55k
+16
4,457
4.961
+110k
+32
4,471
4.969
Comparison Shortcut Models. We also investigate Shortcut models (Frans al.) 2024) for few- step audio generation. Specifically, we allocate 75% of each batch to Flow Matching loss and 25% self-consistency loss. See Algorithms I, in (Frans al. 2024) for training and sampling details. As shown in Table Shortcut models improve few-step results over standard Flow Matching in both PESQ and ViSQOL. Our Flow2GAN achieves further performance improvements, demonstrating clear advantages.
Table 5: Comparison to Shortcut models on LibriTTS dev set with Mel-spectrogram conditioning.
Method
Steps
PESQT
ViSQOLt
Standard Flow Matching
1.132
1.801
2,351
3.691
3.220
4.173
Shortcut Models
3.181
4.426
3.841
4,660
4.122
4.781
Flow2GAN
4.303
4.471
4.969
4.498
4.967
Ablation of the multi-resolution network. As shown in Table 6 our multi calti-rrsooution network structure outperforms the single-resolution baseline (Siuzdak, 2023) with comparable parameters in the final two-step system, demonstrating the benefits of modeling multi-resolution spectral features. Removing the condition encoder in Flow2GAN causes performance drop, indicating that learning higher-level conditional features is helpful.
Table 6: Ablation study of model structure on LibriTTS dev with Mel-spectrogram conditioning.
Method (2-step)
Params (M)
PESQ1
ViSQOLT
Single-resolution, double layers
4.442
4,920
Multi-res,,ution, condition encoder
62.4
4.417
4.966
nulti-resolution (final)
78.9
4,471
4.969
5.4 INFERENCE SPEED
In table 7 we compare Flow2GAN with state-of-the-art models under Mel-spectrogram condition- ing in terms inference speed, measured by the speed factor relative to real time (xRT). Experi- ments are conducted
Intel Xeon Platinum 8457C CPU and NVIDIA H20 GPU with batch size of 16 and segment length of second. Except for Vocos, all versions of Flow2GAN achieve significantly faster inference than other models both CPU and GPU, while even running faster than real time on CPU. This demonstrates that Flow2GAN excels not only in quality but also in efficiency.

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Published as a conference paper at ICLR 2026
Table 7: Model generation speed comparison with batch size of 16 and length of second. BigVGAN-v2" use the specialized CUDA kernel.
xRTT
Model
Params (M)
CPU
GPU
BigVGAN
112.4
0.214
69.8
BigVGAN-v2
112.4
0.214
69.8
BigVGAN-v2
112.4
121.9
Vocos
13.5
387.57
6440,80
RFWave (10-step)
18.1
0.37
158.8
TahooWaave-Turbo (4-step)
70.24
0.12
43.70
WaveFM (I-step)
19.5
0.64
226.31
Flow2GAN, 1-step (ours)
78.9
4.85
851667
Flow2GAN, 2-step (ours)
78.9
2.46
449.26
Flow2GAN, 4-step (ours)
78.9
1.35
228.48
5.5 ZERO-SHOW TTS PERFORMANCE COMPARISON
As shown in Table our 4-step Flow2GAN achieves the same SIM-o and slightly better UTMOS than PeriodWave-Turie (Lee et al. 2024b) with significantly faster inference (Table 7p. though with higher WER. Notably, Big VGAN-22- performs potential overfitting to ground-truth Mel-spectrograms. We also find that adding small Gaussian noise 0.2 rand() x N (0, 1) the conditioning log-Mel during GAN fine-tuning improves both WER and UTMOS, possibly because it makes the model more robust to the imperfect spectrograms from the TTS diffusion model.
than VGAN (Lee al. 2022 suggesting
Table 8: Zero-shot TTS results on LibriSpeech-PCC test-clean with F5-TTS Base models (li- brosa/torchaudio Mels) and various vocoders. "BigVGAN-v2 is trained large-scale dataset. denotes adding noise to log-Mel during GAN fine-tuning.
Model
Params (M)
Mel-type
WER(
SIM-ot
UTMOS1
BigVGAN
112.4
librosa
1.78
0.67
4.05
BigVGAN-v2*
112.4
1.82
0.66
3,70
PeriodWave- Turbo (4 steps)
70.2
0.67
4.17
Vocos
13.5
torchaudio
0.65
3,90
RFWave (10 steps)
18.1
1.87
0.66
3,63
WaveHM (1 step)
19.5
2.01
0.65
3.15
Flow2GAN, 1-step (ours)
78.9
1.94
3.75
Flow2GAN, 2-step (ours)
78.9
1.91
4.11
Flow2GAN, 4-step (ours)
78.9
1.89
0.67
4,18
Flow2GAN, 1-step' (ours)
78.9
1.88
0.67
3.83
FlowIGAN, 2-step! (ours)
78.9
1.88
0.67
4.21
Flow2GAN, 4-step/ (ours)
78.9
1.87
4.26
CONCLUSION
In this work, we propose Flow2GAN, two-stage training framework. In the first stage, Flow Matching is used to learn the generative capability, while the second stage, GAN fine-tuning refines the details, ultimately enabling few-step generation. Specifically, we improve Flow Match- ing by reformulating to endpoint estimation mitigate the difficulties velocity prediction in empty regions, and by introducing spectral energy- adaytivee loss scaling to emphasize perceptu- ally salient quieter regions. Based on this, we construct few-step generators from the trained model, and demonstrate that incorporating results. Our back adopts a multi-branch network that processes spectral coefficients at different time-frequency resolutions, providing powerful modeling capabilities. Experimental results demon- strate that Flow2GAN achieves efficient high-quality audio generation within few steps (e.g.. 1, 2, and 4 steps), delivering highly favorable quality-speed trade-offs in few-step generation compared to existing methods.
additional GAN stage can efficiently enhance the
REPRODUCIBILITY STATEMENT
The datasets used in our experiments are publicly available. Implementation details are provided in Section [.I and model configurations described in Appendix Section A.3 To facilitate the reproduction our results, the source code and pretrained checkpoints are publicly available at https: // thub. com/k2-
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A APPENDIX
A.1 USE OF LARGE LANGUAGE MODELS (LLMs)
In this work, used LLM writing assistance tool improve phrasing, grammar, and sentence clarity.
A.2 MULTI-STEP SAMPLING RESULT
Table@presents the multi-step sampling results in terms PESQ and ViSQOL for the Flow Match- ing stage in Flow2GAN. The results show that increasing the number of sampling steps improves generation quality.
Table 9: Multi-step results on LibriTTS dev set for the Flow Matching stage of Flow
Inference steps
ViSQOL1 PESQT
1
2.279
2.222
2
3.469
3.749
4
3.788
4.156
8
4.013
4.392
16
4.166
4.553
A.3
MODEL CONFIGURATION
Table 10: Detailed model configurations of Flow2GAN with Mel-spectrogram and Encodec audio token conditioning.
Configuration
Mel-spectrogram
Encodec audio token
Input condition
Latent-dim= 12,
Time embedding dimension
512
512
Branch resolutions (N-FFT,HHop)
(512,256), (256,128) (128,64)
(640,320), (60), (160,80) 768.512.384
Branch embedding dimensions
768.512,384
Branch Layers
8,8,8
8.8,8
Feedforward hidden factor
3
3
Convolution kemnel size
Condition encoder embedding dimension
512
512
Condition encoder layers
Loss scaling S(x). (N-FFT.Hop.N) Cifloo.NN-ilteryy N-liters))
(1024.256,256)
(1280,320,320)
A.4
RESULTS COMPARISON OF MEL AND LOG-MEL CONDITIONING
Table
I1
compares Flow2GAN results with Mel and log-Mel conditioning on the LibriTTS test set.
Table 11: Comparison of Mel and log-Mel conditioning on LibriTTS test set.
Model
Condition
PESQI
ViSQOLt
V/UV FI
Periodicityl
FSD1
Flow2GAN, 1-step
Mel
4.288
4.939
0.976
0.058
0.027
Flow2GAN, 1-step
log-Mel
4.189
4.957
0.975
0.063
0.028
Flow2GAN, 2-step
Mel
4,454
4,966
0.981
0.045
0.019
Flow2GAN, 2-step
log-Mel Mel
4.440
4,979
0.983
0.044
0.023
Flow2GAN, 4-step
Mel
4,484
4.966
0,984
0.039
0.017
Flow2GAN, 4-step
log-Mel
4.484
4.986
0,985
0.037
0.016
15


Reviews:
[{"review_id": "dcM1zTtr4H", "reviewer": "Reviewer_KvbH", "summary": "Flow2GAN is a two-stage framework for high-fidelity audio generation that integrates Flow Matching and GAN. It first uses improved Flow Matching to learn robust generative capabilities, which reformulated as endpoint estimation to avoid velocity prediction issues in empty audio regions and enhanced with spectral energy-adaptive loss scaling to emphasize perceptually important quiet areas. Then, lightweight GAN fine-tuning refines details, enabling efficient one- or two-step inference. Equipped with a multi-resolution network processing Fourier coefficients at different time-frequency resolutions, it outperforms state-of-the-art GAN and Flow Matching-based methods in both quality and efficiency under Mel-spectrogram and Encodec audio token conditioning.", "questions": "1. What is the majoy difference between the proposed method and PeriodWave-Turbo? Is it just the improved Flow Matching model?\n2. Can this improved Flow Matching strategy be applied to text to speech/audio tasks?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The two-stage design effectively combines the stable training of Flow Matching and the efficient fine-grained generation of GAN, addressing the slow convergence/mode collapse of GANs and high computational cost of diffusion methods.\n2. For audio’s unique properties, the authors propose endpoint estimation and spectral energy-adaptive loss scaling to improve Flow Matching, significantly enhancing generation quality in silent regions and perceptual consistency.\n3. The multi-resolution network structure outperforms single-resolution designs in modeling audio complexity, providing a powerful backbone for generative learning.", "weaknesses": "1. Compared to BigVGAN-v2 trained on a larger dataset, it still has a slight gap in some metrics, suggesting limitations in generalization to larger-scale data.\n2. The one-step model’s performance at low bandwidth (1.5 kbps) is inferior to its two-step version and some competitors, leaving room for improvement in low-bandwidth audio generation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tsX8HF90Zl", "reviewer": "Reviewer_AhmV", "summary": "This paper introduces Flow2GAN, a two-stage hybrid framework for high-fidelity neural vocoding that aims to combine the training stability of Flow Matching with the detail-refinement capabilities of GANs. The authors first propose improvements to the Flow Matching stage tailored for audio, including reformulating the objective to direct endpoint prediction and introducing a spectral energy-adaptive loss scaling to focus on perceptually important regions. Subsequently, a pre-trained model is used to initialize one- and two-step generators, which are then fine-tuned using adversarial training. The architecture is a multi-resolution network that processes spectral coefficients at different time-frequency scales. Experiments on both mel-spectrogram and audio token conditioning show that Flow2GAN achieves state-of-the-art results in terms of both audio quality and inference efficiency.", "questions": "To help clarify the contributions and rigor of the paper, I would appreciate the authors' response to the following:\n1. Could you please elaborate on the relationship between your proposed spectral energy-adaptive loss scaling and the energy balanced loss from RFWave? How does your approach differ, and what are its specific advantages that lead to the observed performance gains?\n2. When you reformulate the training objective to endpoint prediction (Equation 4), the underlying ODE changes. Could you provide a more thorough derivation or explanation for the new sampling process?\n3. You claim that the Flow Matching pre-training mitigates the risk of GAN mode collapse. This is a significant claim. Could you provide any empirical evidence to support it, for instance, by analyzing the output diversity of Flow2GAN compared to a pure GAN baseline trained for a similar duration?\n4. The main appeal of the model is high-quality generation in one or two steps. Why were there no comparisons against other prominent few-step or single-step generative model sampling strategies, such as consistency models or recent \"shortcut models\" (e.g., as proposed by Frans et al., 2024), which are designed to solve the exact same problem?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper's core concept of a two-stage training paradigm is well-motivated and presents a clever solution to a known trade-off in generative modeling. The approach logically leverages Flow Matching for robust, global structure learning and then uses a fast GAN fine-tuning stage for refining high-frequency details, which is an effective strategy. The proposed modifications to the Flow Matching objective appear sound; the shift to endpoint prediction is an intuitive way to handle silent regions in audio, and the ablation studies convincingly demonstrate its benefits. The experimental results are another clear strength. The model achieves impressive scores across multiple metrics and tasks, outperforming several strong baselines while offering significantly faster inference than multi-step diffusion models.", "weaknesses": "Despite the strong results, the paper has several weaknesses in its positioning and methodological clarity that should be addressed. First, the proposed \"spectral energy-adaptive loss scaling\" is conceptually very similar to the \"energy balanced loss\" used in prior work like RFWave, yet the paper fails to discuss, or compare against it. This omission makes it difficult to assess the novelty of this specific contribution. Second, the reformulation of the prediction target from velocity to endpoint is a significant change to the underlying probability flow ODE. The paper presents a modified sampling equation but does not provide a clear derivation or justification for it, leaving a gap in the methodological explanation.\nFurthermore, a key concern with any GAN-based method is the risk of mode collapse. The paper claims that its two-stage approach mitigates this risk, but this assertion is made without any supporting evidence, either quantitative (e.g., diversity metrics) or qualitative. Finally, while the paper emphasizes its fast one/two-step inference, it critically lacks comparisons to other state-of-the-art fast sampling methods for flow and diffusion models, such as consistency models or recent shortcut models, which are the most relevant competitors for few-step generative performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SH462MR3hP", "reviewer": "Reviewer_Vxds", "summary": "Flow2GAN introduces a two-stage framework combining Flow Matching (FM) pre-training and GAN fine-tuning to balance audio generation quality and inference speed. Key innovations include endpoint-prediction-based FM (to handle silent regions), spectral energy-adaptive loss scaling (for perceptual alignment), and a multi-resolution ConvNeXt network. Experiments show competitive performance against SOTA models, though critical gaps exist in comparisons to key competitors and hybrid FM+GAN baselines.", "questions": "NA", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Hybrid Paradigm: Effectively merges FM’s stable training with GAN’s detail refinement, resolving FM’s slow inference and GAN’s mode collapse issues.\nAudio-Specific FM Improvements: Endpoint prediction and energy-adaptive loss significantly boost FM’s performance, validating their utility for audio synthesis.\nStrong Experimental Results: Outperforms Vocos, RFWave, and WaveFM on most metrics (Table1/2) for Mel-spectrogram and Encodec token conditioning.\nMulti-Resolution Network: Enhances frequency modeling compared to single-resolution designs (Table5), improving perceptual audio quality.", "weaknesses": "Training Complexity: Two-stage training (FM pre-training + GAN fine-tuning) increases the barrier to reproduction and deployment—users need to manage separate pipelines and hyperparameters for each stage.\n\nIncomplete and Unconvincing Comparison Landscape:\na. Lack of coverage of GAN-enhanced Flow Matching models: The paper claims novelty in its hybrid FM+GAN framework, but many existing models leverage GANs to accelerate Flow Matching. A detailed comparison to these models—including their design choices, performance, and efficiency—is missing, which obscures Flow2GAN’s unique contributions and relative standing.\nb. Performance does not exceed BigVGAN: Even accounting for dataset size differences (BigVGAN uses a larger dataset), the paper’s reported metrics (e.g., PESQ, audio quality scores) do not demonstrate that Flow2GAN outperforms BigVGAN. The claim of being \"state-of-the-art\" is thus unsubstantiated against this key competitor.\nc. Significant speed gap with Vocos: The paper emphasizes inference efficiency but fails to address that its speed is much slower than Vocos (a leading efficient audio generation model). This gap undermines the practical utility of Flow2GAN for real-world applications where low latency is critical.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zGjHJuOd8i", "reviewer": "Reviewer_X1ZZ", "summary": "This paper presents Flow2GAN, a framework that combines flow matching and GAN for efficient one- and two-step neural vocoder generation. It employs a robust flow matching training strategy based on end-point estimation and spectral energy–adaptive loss scaling to achieve high-fidelity waveform synthesis.", "questions": "[Q1. Multi-Resolution Network]\n\nAre the inverted outputs from different iSTFT branches simply added together or averaged?\n\n[Q2. Further Steps with GAN Fine-Tuning]\n\nHave you experimented with training four-step or more generators after GAN fine-tuning? It would be interesting to see how performance scales with additional sampling steps.\n\n[Q3. EnCodec Audio Token Experiments]\n\nCould you include PeriodWave-Turbo in Table 2? PeriodWave-Turbo released checkpoints for the same EnCodec-based experiments on their GitHub, so a direct comparison would strengthen your results.\n\n[Q4. TTS Experiments]\n\nPlease consider adding TTS results comparing various neural vocoders. Since some vocoders might overfit to ground-truth Mel-spectrograms, conducting two-stage TTS experiments (The generated Mel from TTS Models to waveform) would enhance the quality of the paper.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "[CFM Training Optimization for Robust Waveform Generation]\n\nThis paper replaces vector field estimation with end-point estimation to achieve more robust waveform generation. In addition, the loss is scaled by spectral energy. Table 3 demonstrates the effectiveness of the proposed optimization method for high-fidelity waveform synthesis.\n\nHowever, the analysis in Figure 2 is limited to the spectral domain. While end-point estimation combined with spectral energy–based scaling can improve training efficiency in the early stages and enhance spectral-domain performance, it would be beneficial to further evaluate the model using waveform-level metrics. I understand that standardized waveform-level metrics are lacking, but I recommend including MOS evaluations in Tables 1 and 2 to provide a more comprehensive assessment of perceptual quality.", "weaknesses": "While I like the concept of this paper, my primary concern lies in its lack of novelty.\n\n[End-Point Estimation]\n\nPrevious works such as PeriodWave and RFWave have already demonstrated the effectiveness of conditional flow matching for high-fidelity waveform generation. The proposed end-point estimation seems to be a relatively simple modification. Moreover, several recent models, including sCM, have adopted similar end-point estimation schemes for few-step generation.\n\n[Spectral Energy Scaling]\n\nMany works already adopt an energy-based scaling. PriorGrad uses a data-dependent prior based on energy, and they scale the loss with this energy. RFWave also adjusts the loss according to the target's standard deviation. \n\nPlease add further discussion with previous scaling methods. Also, I have a concern about that it only reflect the spectral domain information by removing other information. Did you encounter any issues related to this limitation?\n\n[GAN Fine-tuning]\n\nHonestly, I can not find any difference between your approach and PeriodWave-Turbo which has already shown the efficiency of GAN post-training for CFM-based waveform generation. The paper seems to overclaim novelty in Section 4.2 regarding this aspect.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)