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CatalogModelsFastPitch checkpoint (PyTorch, AMP, LJSpeech-1.1)

FastPitch checkpoint (PyTorch, AMP, LJSpeech-1.1)

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FastPitch PyTorch checkpoint trained on LJSpeech-1.1


NVIDIA Deep Learning Examples

Use Case

Speech Synthesis



Latest Version



October 29, 2021


170.81 MB

Model Overview

The FastPitch model generates mel-spectrograms from raw input text and allows to exert additional control over the synthesized utterances.

Model Architecture

FastPitch is a fully feedforward Transformer model that predicts mel-spectrograms from raw text (Figure 1). The entire process is parallel, which means that all input letters are processed simultaneously to produce a full mel-spectrogram in a single forward pass.

FastPitch model architecture

Figure 1. Architecture of FastPitch (source). The model is composed of a bidirectional Transformer backbone (also known as a Transformer encoder), a pitch predictor, and a duration predictor. After passing through the first *N* Transformer blocks, encoding, the signal is augmented with pitch information and discretely upsampled. Then it goes through another set of *N* Transformer blocks, with the goal of smoothing out the upsampled signal, and constructing a mel-spectrogram.


This model was trained using script available on NGC and in GitHub repo


The following datasets were used to train this model:

  • LJSpeech-1.1 - Dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.


Performance numbers for this model are available in NGC



This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.