Skip to main content
ContainersNVIDIADeepSeek-R1NVIDIALlama-3.1-Nemotron-70B-InstructNVIDIAPyTorch
CollectionsNVIDIAOmniverse Kit (FB)NVIDIADeepStream SDKNVIDIAOmniverse Kit App Streaming
ModelsNVIDIAStyleGAN3 pretrained modelsNVIDIAPeopleNetNVIDIATrafficCamNet
ResourcesNVIDIARiva Skills Quick Start
Helm ChartsNVIDIAGPU OperatorNVIDIANVIDIA NIM Operator

Search

Search thousands of GPU-optimized Containers, pretrained Models, SDKs, and Helm charts—ready to accelerate AI, digital twins, and HPC from cloud to edge.
Accelerate custom generative AI app deployment using pre-built containers with optimized AI models.

Displaying 18 results

Riva TTS English US Auxiliary Files
NVIDIA
Contains files used in rmir creation
Model
nemo-speech.cpp
NVIDIA
A lightweight native C++ runtime for NVIDIA Nemotron Speech models built on ggml. Runs speech models in real time and batch mode across platforms and backends.
Container
WaveGlow LJS 256 Channels
NVIDIA
WaveGlow model weights pre-trained on the LJ Speech dataset to be used with https://github.com/NVIDIA/waveglow.
Model
Text to Speech Notebook
NVIDIA
End to End workflow for text to speech training with TAO Toolkit and deployment using Riva.
Resource
Speech Synthesis English FastPitch
NVIDIA
Mel-Spectrogram prediction conditioned on input text with LJSpeech voice.
Model
Speech Synthesis HiFi-GAN
NVIDIA
GAN-based waveform generator from mel-spectrograms.
Model
HiFi-GAN for PyTorch
NVIDIA Deep Learning Examples
HiFi-GAN model implements a spectrogram inversion model that allows to synthesize speech waveforms from mel-spectrograms.
Resource
Tacotron2 PyTorch checkpoint (AMP)
NVIDIA Deep Learning Examples
Tacotron2 PyTorch checkpoint trained with AMP
Model
Tacotron2 and Waveglow 2.0 for PyTorch
NVIDIA Deep Learning Examples
The Tacotron 2 and WaveGlow model form a text-to-speech system that enables user to synthesise a natural sounding speech from raw transcripts.
Resource
Speech Synthesis Waveglow
NVIDIA
Universal waveform generator from mel-spectrograms.
Model
Speech Synthesis English FastPitch
NVIDIA
Mel-Spectrogram prediction conditioned on input text with LJSpeech voice.
Model
FastPitch 1.0 for PyTorch
NVIDIA Deep Learning Examples
The FastPitch model generates mel-spectrograms from raw input text and allows to exert additional control over the synthesized utterances.
Resource
Waveglow PyTorch checkpoint
NVIDIA Deep Learning Examples
Waveglow PyTorch checkpoint trained with AMP
Model
RIVA EnglishUS Fastpitch
NVIDIA
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
Model
Speech Synthesis HiFi-GAN
NVIDIA
GAN-based waveform generator from mel-spectrograms.
Model
RIVA EnglishUS Hifigan
NVIDIA
HifiGAN is a neural vocoder model for text-to-speech applications. It is intended as the second part of a two-stage speech synthesis pipeline, with a mel-spectrogram generator such as FastPitch as the first stage.
Model
NeMo - Text to Speech
NVIDIA
This collection contains NeMo models for Text to Speech (TTS)
Collection
Speech Synthesis
NVIDIA
A collection of easy to use, highly optimized Deep Learning Models for Speech Synthesis. Deep Learning Examples provides Data Scientist and Software Engineers with recipes to Train, fine-tune, and deploy State-of-the-Art Models
Collection
1-18 of 18 items

Modal Content

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking "Accept All", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on "Manage Settings." By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.

Manage Settings