  The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide
a common sizable dataset as well as a fair testbed for the benchmarking of the
popular voice cloning task. Specifically, we formulate the challenge to adapt
an average TTS model to the stylistic target voice with limited data from
target speaker, evaluated by speaker identity and style similarity. The
challenge consists of two tracks, namely few-shot track and one-shot track,
where the participants are required to clone multiple target voices with 100
and 5 samples respectively. There are also two sub-tracks in each track. For
sub-track a, to fairly compare different strategies, the participants are
allowed to use only the training data provided by the organizer strictly. For
sub-track b, the participants are allowed to use any data publicly available.
In this paper, we present a detailed explanation on the tasks and data used in
the challenge, followed by a summary of submitted systems and evaluation
results.
