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We propose TalkNet, a non-autoregressive convolutional neural model forspeech synthesis with explicit pitch and duration prediction. The modelconsists of three feed-forward convolutional networks. The first networkpredicts grapheme durations. An input text is expanded by repeating each symbolaccording to the predicted duration. The second network predicts pitch valuefor every mel frame. The third network generates a mel-spectrogram from theexpanded text conditioned on predicted pitch. All networks are based on 1Ddepth-wise separable convolutional architecture. The explicit durationprediction eliminates word skipping and repeating. The quality of the generatedspeech nearly matches the best auto-regressive models - TalkNet trained on theLJSpeech dataset got MOS 4.08. The model has only 13.2M parameters, almost 2xless than the present state-of-the-art text-to-speech models. Thenon-autoregressive architecture allows for fast training and inference. Thesmall model size and fast inference make the TalkNet an attractive candidatefor embedded speech synthesis.

TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction