We present FastPitch, a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference , and generates speech that could be further controlled with predicted contours. FastPitch can thus change the perceived emotional state of the speaker or put emphasis on certain lexical units. We find that uniformly increasing or decreasing the pitch with FastPitch generates speech that resembles the voluntary modulation of voice. Conditioning on frequency contours improves the  quality of synthesized speech, making it comparable to state-of-the-art. It does not introduce an overhead, and FastPitch retains the favorable, fully-parallel Transformer architecture of FastSpeech with a similar speed of mel-scale spectrogram synthesis , orders of magnitude faster than real-time  .
We present FastPitch, a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference . By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. Uniformly increasing or decreasing pitch with FastPitch generates speech that resembles the voluntary modulation of voice. Conditioning on frequency contours improves the overall quality of synthesized speech, making it comparable to state-of-the-art. It does not introduce an overhead, and FastPitch retains the favorable, fully-parallel Transformer architecture , with over 900x real-time factor for mel-spectrogram synthesis of a typical utterance .
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, and generates speech that could be further controlled with predicted contours. FastPitch can thus change the perceived emotional state of the speaker or put emphasis on certain lexical units. We find that uniformly
. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. Uniformly
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of FastSpeech with a similar speed of mel-scale spectrogram synthesis , orders of magnitude faster than
, with over 900x
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factor for mel-spectrogram synthesis of a typical utterance