  We propose TalkNet, a convolutional non-autoregressive neural model for
speech synthesis. The model consists of two feed-forward convolutional
networks. The first network predicts grapheme durations. An input text is
expanded by repeating each symbol according to the predicted duration. The
second network generates a mel-spectrogram from the expanded text. To train a
grapheme duration predictor, we add the grapheme duration to the training
dataset using a pre-trained Connectionist Temporal Classification (CTC)-based
speech recognition model. The explicit duration prediction eliminates word
skipping and repeating. Experiments on the LJSpeech dataset show that the
speech quality nearly matches auto-regressive models. The model is very compact
-- it has 10.8M parameters, almost 3x less than the present state-of-the-art
text-to-speech models. The non-autoregressive architecture allows for fast
training and inference.
