TTS FastPitch speaker adapter pretrain IPA

TTS FastPitch speaker adapter pretrain IPA

Logo for TTS FastPitch speaker adapter pretrain IPA
Description
Pretrained FastPitch base checkpoint for speaker adapter with IPA
Publisher
NVIDIA
Latest Version
trainable_v1.0
Modified
November 9, 2023
Size
197.92 MB

Speech Synthesis: FastPitch_Speaker_Adapter 1.1 Model

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 was trained with adapters and is good for finetuning a custom voice, it also uses the International Phonetic Alphabet (IPA) for inference and training instead of ARPABET.

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 proprietary dataset sampled at 44100Hz, and can be used to generate English voices with an American accent. This model supports 1 male voice and 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.

Input

English text strings

Output

Mel spectrogram of shape (batch x mel_channels x time)

References

FastPitch with adapters paper: https://arxiv.org/pdf/2211.00585.pdf

License

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.