  Conversion of Chinese graphemes to phonemes (G2P) is an essential component
in Mandarin Chinese Text-To-Speech (TTS) systems. One of the biggest challenges
in Chinese G2P conversion is how to disambiguate the pronunciation of
polyphones - characters having multiple pronunciations. Although many academic
efforts have been made to address it, there has been no open dataset that can
serve as a standard benchmark for fair comparison to date. In addition, most of
the reported systems are hard to employ for researchers or practitioners who
want to convert Chinese text into pinyin at their convenience. Motivated by
these, in this work, we introduce a new benchmark dataset that consists of
99,000+ sentences for Chinese polyphone disambiguation. We train a simple
neural network model on it, and find that it outperforms other preexisting G2P
systems. Finally, we package our project and share it on PyPi.
