NGC | Catalog
CatalogModelsRIVA Spanish ES Female Fastpitch

RIVA Spanish ES Female Fastpitch

For downloads and more information, please view on a desktop device.
Logo for RIVA Spanish ES Female Fastpitch


Riva Fastpitch IPA single speaker model



Latest Version



September 11, 2023


88.04 MB

Speech Synthesis: FastPitch 1.1 Model Card

Model overview

FastPitch is a mel-spectrogram generator, designed to be used as the first part of a neural text-to-speech system in conjunction with a neural vocoder. This model uses the International Phonetic Alphabet (IPA) for inference and training.

Model architecture

FastPitch is 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. FastPitch is based on a fully-parallel Transformer architecture, with much higher real-time factor than Tacotron2 for mel-spectrogram synthesis of a typical utterance.



This model is trained on a mix of public and proprietary data sampled at 22050Hz, and can be used to generate a Spanish (EU) voice. This model supports 1 female voice.

How to use this model

FastPitch is intended to be used as the first part of a two stage speech synthesis pipeline. FastPitch takes text and produces a mel spectrogram. The second stage takes the generated mel spectrogram and returns audio.


Spanish text strings


Mel spectrogram of shape (batch x mel_channels x time)


FastPitch paper:


By downloading and using the models and resources packaged with Riva Conversational AI, you would be accepting the terms of the Riva license

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.