  We propose TalkNet, a non-autoregressive convolutional neural model for
speech synthesis with explicit pitch and duration prediction. The model
consists of three 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 predicts pitch value
for every mel frame. The third network generates a mel-spectrogram from the
expanded text conditioned on predicted pitch. All networks are based on 1D
depth-wise separable convolutional architecture. The explicit duration
prediction eliminates word skipping and repeating. The quality of the generated
speech nearly matches the best auto-regressive models - TalkNet trained on the
LJSpeech dataset got MOS 4.08. The model has only 13.2M parameters, almost 2x
less than the present state-of-the-art text-to-speech models. The
non-autoregressive architecture allows for fast training and inference. The
small model size and fast inference make the TalkNet an attractive candidate
for embedded speech synthesis.
