Learning the Beauty in Songs: Neural Singing Voice Beautifier; ACL 2022 (Main conference); Official code

Related tags

Deep Learninggansinging-voicesinging-synthesisacl2022
Overview

Learning the Beauty in Songs: Neural Singing Voice Beautifier

Jinglin Liu, Chengxi Li, Yi Ren, Zhiying Zhu, Zhou Zhao

Zhejiang University

ACL 2022 Main conference

arXiv GitHub Stars visitors

🚧 ⛏️ 🛠️ 👷

This repository is the official PyTorch implementation of our ACL-2022 paper. Now, we release the codes for SADTW algorithm in our paper. The current expected release time of the full version codes and data is at the ACL-2022 conference (before June. 2022). Please star us and stay tuned!

|--modules
    |--voice_conversion
        |--dtw
            |--enhance_sadtw.py  (Our algorithm)
|--tasks
    |--singing
        |--pitch_alignment_task.py  (Usage example)

🚀 News:

  • Feb.24, 2022: Our new work, NeuralSVB was accepted by ACL-2022. Demo Page.
  • Dec.01, 2021: Our recent work DiffSinger was accepted by AAAI-2022. downloads | .
  • Sep.29, 2021: Our recent work PortaSpeech was accepted by NeurIPS-2021. .
  • May.06, 2021: We submitted DiffSinger to Arxiv arXiv.

Abstract

We are interested in a novel task, singing voice beautifying (SVB). Given the singing voice of an amateur singer, SVB aims to improve the intonation and vocal tone of the voice, while keeping the content and vocal timbre. Current automatic pitch correction techniques are immature, and most of them are restricted to intonation but ignore the overall aesthetic quality. Hence, we introduce Neural Singing Voice Beautifier (NSVB), the first generative model to solve the SVB task, which adopts a conditional variational autoencoder as the backbone and learns the latent representations of vocal tone. In NSVB, we propose a novel time-warping approach for pitch correction: Shape-Aware Dynamic Time Warping (SADTW), which ameliorates the robustness of existing time-warping approaches, to synchronize the amateur recording with the template pitch curve. Furthermore, we propose a latent-mapping algorithm in the latent space to convert the amateur vocal tone to the professional one. Extensive experiments on both Chinese and English songs demonstrate the effectiveness of our methods in terms of both objective and subjective metrics.

Issues

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Comments
  • Problem with proper data loading

    Problem with proper data loading

    Hi, I'd like to run your model by myself, however I cannot find proper way to load the dataset with .mp3 files you provided. Is there a chance to share the dataloader you've used or give some hints how to process the .mp3 files to valid dataset which could be used in your usage examples? I'll be very grateful!

    opened by pstryczke 9
  • 关于NSVB

    关于NSVB

    听了demo后有些疑问， 1 如果实际使用来美化唱歌，那么Inference的时候是需要原唱的pitch curve对吧？ 2 虽然测试样例不在训练样本中，但是GT Professional和GT Amateur是同一个人录制的。Inference中GT Professional不可能是自己，这样泛化性有测试过吗？

    opened by suzhenghang 0
  • hi, request for datasets and source code.

    hi, request for datasets and source code.

    This work is very outstanding and we are insterested in it. Are there any plans to make the dataset and associated pretrained models public in the near future? Thank you

    opened by hertz-pj 0
Owner
Jinglin Liu
Major in NLP, Speech & Singing.
Jinglin Liu
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