Speech Synthesis: English-US Multispeaker - RAD-TTS Model Overview

Description:

The English-US Multispeaker RAD-TTS-HifiGAN model transcribes text into audio representations using two model components: RAD-TTS and HifiGAN. This model is ready for commercial use.

RAD-TTS 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, and it can output a female or a male voice for US English.

This RAD-TTS model was trained to be used with a corresponding HifiGAN model available here. HifiGAN is a neural vocoder model for text-to-speech applications. It is the second part of a two-stage speech synthesis pipeline.

References:

RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis

HifiGAN paper: https://arxiv.org/abs/2010.05646

Model Architecture:

Network Architecture: RAD-TTS + HifiGAN

RAD-TTS is a parallel flow-based generative network for text-to-speech synthesis. It extends prior parallel approaches by additionally modeling speech rhythm as a separate generative distribution to facilitate variable token duration during inference.

HifiGAN is a neural vocoder based on a generative adversarial network framework. During training, the model uses a powerful discriminator consisting of small sub-discriminators, each one focusing on specific periodic parts of a raw waveform.

Input:

For RAD-TTS (1st Stage): Text Strings in English

Other Properties Related to Input: 400 Character Text String Limit

Output:

For HifiGAN (2nd Stage): Audio of shape (batch x time) in wav format

Other Parameters Related to Output: Mono, Encoded 16 bit audio; 20 Second Maximum Length; Depending on input, this model can output a female or a male voice for American English with six (6) emotions for the female voice and four (4) emotions for male voice. The female voice is classified as "neutral," "calm," "happy," "angry," "fearful," and "sad." The male voice is classified as "neutral," “calm," "happy," and "angry."

Software Integration:

Runtime Engine(s): Riva 2.13.0 or greater

Supported Hardware Platform(s):

• NVIDIA Volta V100
  
• NVIDIA Turing T4
  
• NVIDIA A100 GPU
  
• NVIDIA A30 GPU
  
• NVIDIA A10 GPU
  
• NVIDIA H100 GPU
  
• NVIDIA L4 GPU
  
• NVIDIA L40 GPU
  
• NVIDIA Jetson Orin
  
• NVIDIA Jetson AGX Xavier
  
• NVIDIA Jetson NX Xavier
  

Supported Operating System(s):

• Linux
  
• Linux 4 Tegra
  

Model Version(s):

RADTTS_44k_EnglishUS_IPA v2.10.0

Training & Evaluation:

Training Dataset:

** Data Collection Method by dataset

• Human
  Properties (Quantity, Dataset Descriptions, Sensor(s)): This model is trained on a proprietary dataset of audio-text pairs sampled at 44100 Hz, which contains one Female and one Male voice speaking US English. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.
  

Evaluation Dataset:

** Data Collection Method by dataset

• Human
  Properties (Quantity, Dataset Descriptions, Sensor(s)): This model is trained on a proprietary dataset sampled at 44100 Hz, which contains one Female and one Male voice speaking US English. While both genders are trained for all emotions, this dataset only releases those that passed the evaluation standard for expressiveness and quality. The dataset also contains a subset of sentences with different words emphasized.
  

Inference:

Engine: Triton
Test Hardware:

• NVIDIA Volta V100
• NVIDIA Turing T4
• NVIDIA A100 GPU
• NVIDIA A30 GPU
• NVIDIA A10 GPU
• NVIDIA H100 GPU
• NVIDIA L4 GPU
• NVIDIA L40 GPU
• NVIDIA Jetson Orin
• NVIDIA Jetson AGX Xavier
• NVIDIA Jetson NX Xavier

Ethical Considerations:

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License

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