Papers › HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis

HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis

3 Sep 2020arXiv:2009.01776archive 2025-07-28

Jiawei Chen, Xu Tan, Jian Luan, Tao Qin, Tie-Yan Liu

High-fidelity singing voices usually require higher sampling rate (e.g., 48kHz) to convey expression and emotion. However, higher sampling rate causes the wider frequency band and longer waveform sequences and throws challenges for singing voice synthesis (SVS) in both frequency and time domains. Conventional SVS systems that adopt small sampling rate cannot well address the above challenges. In this paper, we develop HiFiSinger, an SVS system towards high-fidelity singing voice. HiFiSinger consists of a FastSpeech based acoustic model and a Parallel WaveGAN based vocoder to ensure fast training and inference and also high voice quality. To tackle the difficulty of singing modeling caused by high sampling rate (wider frequency band and longer waveform), we introduce multi-scale adversarial training in both the acoustic model and vocoder to improve singing modeling. Specifically, 1) To handle the larger range of frequencies caused by higher sampling rate, we propose a novel sub-frequency GAN (SF-GAN) on mel-spectrogram generation, which splits the full 80-dimensional mel-frequency into multiple sub-bands and models each sub-band with a separate discriminator. 2) To model longer waveform sequences caused by higher sampling rate, we propose a multi-length GAN (ML-GAN) for waveform generation to model different lengths of waveform sequences with separate discriminators. 3) We also introduce several additional designs and findings in HiFiSinger that are crucial for high-fidelity voices, such as adding F0 (pitch) and V/UV (voiced/unvoiced flag) as acoustic features, choosing an appropriate window/hop size for mel-spectrogram, and increasing the receptive field in vocoder for long vowel modeling. Experiment results show that HiFiSinger synthesizes high-fidelity singing voices with much higher quality: 0.32/0.44 MOS gain over 48kHz/24kHz baseline and 0.83 MOS gain over previous SVS systems.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2009.01776")

Code

Syntology Ran 0 of 10 code samples harvested from 1 repository linked to this paper; 10 have no recorded run.

By repository: community (archive-listed): 10 samples from 1 repository, 0 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

10 samples harvested; 0 ran; 0 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

10unverified

Licence: 0 of the 10 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from CODEJIN/HiFiSinger. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

Mel_Stack CODEJIN/HiFiSinger/Datasets.py community (archive-listed) unverified MIT (permissive) · 9f069d3d7349b7e4 · report
Preemphasis CODEJIN/HiFiSinger/Audio.py community (archive-listed) unverified MIT (permissive) · 494d58dba436fa48 · report
Recursive_Parse CODEJIN/HiFiSinger/Arg_Parser.py community (archive-listed) unverified MIT (permissive) · 564512b44009385c · report
Silence_Stack CODEJIN/HiFiSinger/Datasets.py community (archive-listed) unverified MIT (permissive) · a944e54ed971b982 · report
Text_to_Token CODEJIN/HiFiSinger/Datasets.py community (archive-listed) unverified MIT (permissive) · 436aa29055fdc19e · report
apply_gradient_allreduce CODEJIN/HiFiSinger/distributed.py community (archive-listed) unverified MIT (permissive) · 360725578b4657a7 · report
cumulativeMeanNormalizedDifferenceFunction CODEJIN/HiFiSinger/yin.py community (archive-listed) unverified MIT (permissive) · e507922638a3a56e · report
differenceFunction CODEJIN/HiFiSinger/yin.py community (archive-listed) unverified MIT (permissive) · 3cbbf9560f72cc60 · report
getPitch CODEJIN/HiFiSinger/yin.py community (archive-listed) unverified MIT (permissive) · 762aa23bfe00152c · report
reduce_tensor CODEJIN/HiFiSinger/distributed.py community (archive-listed) unverified MIT (permissive) · a9bfcbb1d3623ba1 · report

Tasks

Singing Voice SynthesisVocal Bursts Intensity Prediction

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

ConvolutionDense ConnectionsDropoutPhase ShuffleReLUTanh ActivationWGAN-GP LossWaveGAN

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections