# Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion

arXiv: https://arxiv.org/abs/1906.00794 · HTML: https://mlsanity.com/paper/1906.00794/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 29 of 29)

- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Principled Weight Initialization for Hypernetworks](https://mlsanity.com/paper/2312.08399/) (2023)
- [HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks](https://mlsanity.com/paper/2103.09439/) (2021)
- [Example-based Hypernetworks for Out-of-Distribution Generalization](https://mlsanity.com/paper/2203.14276/) (2022)
- [Magnitude Invariant Parametrizations Improve Hypernetwork Learning](https://mlsanity.com/paper/2304.07645/) (2023)
- ["Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World](https://mlsanity.com/paper/2109.09598/) (2021)
- [Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques](https://mlsanity.com/paper/2104.00931/) (2021)
- [Learning the Beauty in Songs: Neural Singing Voice Beautifier](https://mlsanity.com/paper/2202.13277/) (2022)
- [NVC-Net: End-to-End Adversarial Voice Conversion](https://mlsanity.com/paper/2106.00992/) (2021)
- [FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation](https://mlsanity.com/paper/2409.02245/) (2024)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [Global Rhythm Style Transfer Without Text Transcriptions](https://mlsanity.com/paper/2106.08519/) (2021)
- [Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning](https://mlsanity.com/paper/2110.14422/) (2021)
- [NoiseVC: Towards High Quality Zero-Shot Voice Conversion](https://mlsanity.com/paper/2104.06074/) (2021)
- [Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments](https://mlsanity.com/paper/2106.08873/) (2021)
- [Distilling the Knowledge from Conditional Normalizing Flows](https://mlsanity.com/paper/2106.12699/) (2021)
- [TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training](https://mlsanity.com/paper/2208.04035/) (2022)
- [Speak Like a Dog: Human to Non-human creature Voice Conversion](https://mlsanity.com/paper/2206.04780/) (2022)
- [GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion](https://mlsanity.com/paper/2207.01454/) (2022)
- [AE-Flow: AutoEncoder Normalizing Flow](https://mlsanity.com/paper/2312.16552/) (2023)
- [FasterVoiceGrad: Faster One-step Diffusion-Based Voice Conversion with Adversarial Diffusion Conversion Distillation](https://mlsanity.com/paper/2508.17868/) (2025)
- [LatentVoiceGrad: Nonparallel Voice Conversion with Latent Diffusion/Flow-Matching Models](https://mlsanity.com/paper/2509.08379/) (2025)
- [Creating New Voices using Normalizing Flows](https://mlsanity.com/paper/2312.14569/) (2023)
- [A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features](https://mlsanity.com/paper/2403.01046/) (2024)
- [MeanVoiceFlow: One-step Nonparallel Voice Conversion with Mean Flows](https://mlsanity.com/paper/2602.18104/) (2026)
- [Invertible Voice Conversion](https://mlsanity.com/paper/2201.10687/) (2022)
- [DisC-VC: Disentangled and F0-Controllable Neural Voice Conversion](https://mlsanity.com/paper/2210.11059/) (2022)
- [Scale-Space Hypernetworks for Efficient Biomedical Imaging](https://mlsanity.com/paper/2304.05448/) (2023)
- [MeanVoiceFlow2: Joint Optimization of Mean Flow and Content Encoder for Fast One-Step Zero-Shot Voice Conversion](https://mlsanity.com/paper/2609.40087/) (2026)

## Abstract (arXiv)

End-to-end models for raw audio generation are a challenge, specially if they have to work with non-parallel data, which is a desirable setup in many situations. Voice conversion, in which a model has to impersonate a speaker in a recording, is one of those situations. In this paper, we propose Blow, a single-scale normalizing flow using hypernetwork conditioning to perform many-to-many voice conversion between raw audio. Blow is trained end-to-end, with non-parallel data, on a frame-by-frame basis using a single speaker identifier. We show that Blow compares favorably to existing flow-based architectures and other competitive baselines, obtaining equal or better performance in both objective and subjective evaluations. We further assess the impact of its main components with an ablation study, and quantify a number of properties such as the necessary amount of training data or the preference for source or target speakers.