# S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations

arXiv: https://arxiv.org/abs/2110.06280 · HTML: https://mlsanity.com/paper/2110.06280/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [Exploring wav2vec 2.0 on speaker verification and language identification](https://mlsanity.com/paper/2012.06185/) (2020)
- [Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion](https://mlsanity.com/paper/2008.12527/) (2020)
- [FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention](https://mlsanity.com/paper/2010.14150/) (2020)
- [An Open source Implementation of ITU-T Recommendation P.808 with Validation](https://mlsanity.com/paper/2005.08138/) (2020)
- [Predictions of Subjective Ratings and Spoofing Assessments of Voice Conversion Challenge 2020 Submissions](https://mlsanity.com/paper/2009.03554/) (2020)
- [Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations](https://mlsanity.com/paper/2010.12231/) (2020)
- [S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations](https://mlsanity.com/paper/2104.02901/) (2021)
- [The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS](https://mlsanity.com/paper/2010.02434/) (2020)
- [Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer](https://mlsanity.com/paper/2009.01475/) (2020)
- [On Prosody Modeling for ASR+TTS based Voice Conversion](https://mlsanity.com/paper/2107.09477/) (2021)

## Built on by (most cited first, 23 of 23)

- [FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion](https://mlsanity.com/paper/2210.15418/) (2022)
- [SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities](https://mlsanity.com/paper/2203.06849/) (2022)
- [A Large-Scale Evaluation of Speech Foundation Models](https://mlsanity.com/paper/2404.09385/) (2024)
- [DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior Mixup for Verified Robust Voice Conversion](https://mlsanity.com/paper/2305.15816/) (2023)
- [From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion](https://mlsanity.com/paper/2308.02560/) (2023)
- [Improving Robustness of LLM-based Speech Synthesis by Learning Monotonic Alignment](https://mlsanity.com/paper/2406.17957/) (2024)
- [Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units](https://mlsanity.com/paper/2212.09730/) (2022)
- [ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations](https://mlsanity.com/paper/2302.08137/) (2023)
- [Efficient Domain Adaptation for Speech Foundation Models](https://mlsanity.com/paper/2302.01496/) (2023)
- [Disentangled Feature Learning for Real-Time Neural Speech Coding](https://mlsanity.com/paper/2211.11960/) (2022)
- [A Comparative Study of Self-supervised Speech Representation Based Voice Conversion](https://mlsanity.com/paper/2207.04356/) (2022)
- [Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2407.13782/) (2024)
- [GenVC: Self-Supervised Zero-Shot Voice Conversion](https://mlsanity.com/paper/2502.04519/) (2025)
- [Towards General-Purpose Text-Instruction-Guided Voice Conversion](https://mlsanity.com/paper/2309.14324/) (2023)
- [vec2wav 2.0: Advancing Voice Conversion via Discrete Token Vocoders](https://mlsanity.com/paper/2409.01995/) (2024)
- [SelfVC: Voice Conversion With Iterative Refinement using Self Transformations](https://mlsanity.com/paper/2310.09653/) (2023)
- [Evaluating Methods for Ground-Truth-Free Foreign Accent Conversion](https://mlsanity.com/paper/2309.02133/) (2023)
- [Cross-lingual Text-To-Speech with Flow-based Voice Conversion for Improved Pronunciation](https://mlsanity.com/paper/2210.17264/) (2022)
- [A unified one-shot prosody and speaker conversion system with self-supervised discrete speech units](https://mlsanity.com/paper/2211.06535/) (2022)
- [Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices](https://mlsanity.com/paper/2204.01677/) (2022)
- [Enhancing Speech-to-Speech Translation with Multiple TTS Targets](https://mlsanity.com/paper/2304.04618/) (2023)
- [From A to B to A: Palindromic Zero-Shot Voice Conversion with Non-Parallel Data](https://mlsanity.com/paper/2606.08843/) (2026)
- [Personalized Automatic Speech Recognition for a Dysarthric and Tracheostomic Speaker using Artificial Conversations](https://mlsanity.com/paper/2610.03017/) (2026)

## Abstract (arXiv)

This paper introduces S3PRL-VC, an open-source voice conversion (VC) framework based on the S3PRL toolkit. In the context of recognition-synthesis VC, self-supervised speech representation (S3R) is valuable in its potential to replace the expensive supervised representation adopted by state-of-the-art VC systems. Moreover, we claim that VC is a good probing task for S3R analysis. In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting. We also provide comparisons between not only different S3Rs but also top systems in VCC2020 with supervised representations. Systematic objective and subjective evaluation were conducted, and we show that S3R is comparable with VCC2020 top systems in the A2O setting in terms of similarity, and achieves state-of-the-art in S3R-based A2A VC. We believe the extensive analysis, as well as the toolkit itself, contribute to not only the S3R community but also the VC community. The codebase is now open-sourced.