Search Results for "parallel"
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Showing 5 open source projects for "parallel"
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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gtechna: Digital Platform for Parking Enforcement & ManagementMeet the ever-evolving needs of parking rules, parkers, and curbside space with secure and efficient enforcement solutions that fulfill operational goals and provide helpful, convenient service. We consider every client a partner—and customer success and growth are our primary goals. That’s why we work alongside you to understand your needs, then deliver innovative solutions that work specifically for you. Private lots and garages come in all shapes, sizes, and forms. That’s why we provide a full range of solutions to help maximize compliance, eliminate congestion, and increase turnover, so your property is as efficient as possible. Off-street parking can require a system capable of handling sophisticated business rules. Our feature-rich off-street solutions provide you with the breadth of functionality you need to run an efficient parking operation.
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Parallel WaveGAN
Unofficial Parallel WaveGAN
Parallel WaveGAN is an unofficial PyTorch implementation of several state-of-the-art non-autoregressive neural vocoders, centered on Parallel WaveGAN but also including MelGAN, Multiband-MelGAN, HiFi-GAN, and StyleMelGAN. Its main goal is to provide a real-time neural vocoder that can turn mel spectrograms into high-quality speech audio efficiently. -
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vits_chinese
Best practice TTS based on BERT and VITS
vits_chinese is an implementation of the VITS end-to-end text-to-speech (TTS) architecture tailored for Chinese (and possibly multilingual) speech synthesis. VITS is a model combining variational autoencoders (VAEs), normalizing flows, adversarial learning, and a stochastic duration predictor — a design that enables generation of natural, expressive speech, capturing variations in rhythm and prosody. By customizing or porting VITS for Chinese, this project aims to produce high-quality TTS... -
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VITS
Conditional Variational Autoencoder with Adversarial Learning
...Unlike traditional two-stage systems that separately train an acoustic model and a vocoder, VITS trains an end-to-end model that maps text directly to waveform using a conditional variational autoencoder combined with normalizing flows and adversarial training. This architecture enables parallel generation (fast inference) while achieving speech quality that rivals or surpasses many two-stage systems. The repository provides training and inference pipelines for common datasets such as LJ Speech (single-speaker) and VCTK (multi-speaker), including filelists, configs, and preprocessing scripts. It also includes monotonic alignment search code and g2p preprocessing, which are crucial components for aligning text and speech in an end-to-end setup. -
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Transformer TTS
Implementation of a Transformer based neural network
...This design addresses common autoregressive issues such as repetition, skipped words, and unstable attention, and results in robust, fast synthesis where all frames are predicted in parallel. The repository ships with tooling to build datasets (especially LJSpeech) and create training data, plus scripts to train both the aligner and the TTS model, monitor training with TensorBoard, and resume or reset training runs. -
The first and only enterprise browser that solves both enterprise security and workforce productivityTraditional browsers were never designed for work. They're for internet browsing. Imagine a browser purpose-built for work: one that simultaneously supercharges enterprise security, workforce productivity and enterprise AI.
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
...The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as sentiment analysis. It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. The project comes with configuration-driven training scripts, documentation, and examples that demonstrate how to set up pipelines for tasks.
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