  We propose SpeechPainter, a model for filling in gaps of up to one second in
speech samples by leveraging an auxiliary textual input. We demonstrate that
the model performs speech inpainting with the appropriate content, while
maintaining speaker identity, prosody and recording environment conditions, and
generalizing to unseen speakers. Our approach significantly outperforms
baselines constructed using adaptive TTS, as judged by human raters in
side-by-side preference and MOS tests.
