In this paper, we develop DeepSinger, a multi-lingual multi-singer singing
voice synthesis (SVS) system, which is built from scratch using singing
training data mined from music websites. The pipeline of DeepSinger consists of
several steps, including data crawling, singing and accompaniment separation,
lyrics-to-singing alignment, data filtration, and singing modeling.
Specifically, we design a lyrics-to-singing alignment model to automatically
extract the duration of each phoneme in lyrics starting from coarse-grained
sentence level to fine-grained phoneme level, and further design a
multi-lingual multi-singer singing model based on a feed-forward Transformer to
directly generate linear-spectrograms from lyrics, and synthesize voices using
Griffin-Lim. DeepSinger has several advantages over previous SVS systems: 1) to
the best of our knowledge, it is the first SVS system that directly mines
training data from music websites, 2) the lyrics-to-singing alignment model
further avoids any human efforts for alignment labeling and greatly reduces
labeling cost, 3) the singing model based on a feed-forward Transformer is
simple and efficient, by removing the complicated acoustic feature modeling in
parametric synthesis and leveraging a reference encoder to capture the timbre
of a singer from noisy singing data, and 4) it can synthesize singing voices in
multiple languages and multiple singers. We evaluate DeepSinger on our mined
singing dataset that consists of about 92 hours data from 89 singers on three
languages (Chinese, Cantonese and English). The results demonstrate that with
the singing data purely mined from the Web, DeepSinger can synthesize
high-quality singing voices in terms of both pitch accuracy and voice
naturalness (footnote: Our audio samples are shown in
this https URL)

本文介绍了 DeepSinger，这是一个从音乐网站中抓取数据训练的多语言、多歌手的歌唱声音合成系统，其特点包括自动化的对齐模型、基于 Transformer 的合成模型以及多语言、多歌手的支持