RIVA Italian IT Female Fastpitch

RIVA Italian IT Female Fastpitch

Logo for RIVA Italian IT Female Fastpitch
Description
Riva Fastpitch IPA single speaker model
Publisher
NVIDIA
Latest Version
deployable_v1.0
Modified
October 25, 2023
Size
87.5 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.

Training

Dataset

This model is trained on a mix of public and proprietary data sampled at 22050Hz, and can be used to generate an Italian 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.

The encryption key for this model is R62srgxeXBgVxg

Input

Italian text strings

Output

Mel-spectrogram of shape (batch x mel_channels x time)

References

[1] FastPitch: Parallel Text-to-speech with Pitch Prediction

Suggested Reading

Refer to the Riva documentation for more information.

License

By downloading and using the models and resources packaged with Riva Conversational AI, you accept 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.