Ros2-voiceroid2 - ROS2 wrapper package of VOICEROID2

Overview

ros2_voiceroid2

ROS2 wrapper package of VOICEROID2

Windows Only

Installation

  1. Install VOICEROID2 (x64).
  2. Install Python 3.4 (x64) or later.
  3. Install ROS2 foxy or later.
  4. pip install simpleaudio
  5. pip install git+https://github.com/Nkyoku/pyvcroid2.git
  6. Clone this repository.
  7. Build this repository.
    colcon build --merge-install --packages-select voiceroid2

Usage

  1. Source this package.
    ./install/local_setup.ps1
  2. Run the publisher node.
    ros2 run voiceroid2 talker
  3. There are several parameters.
    • language : string
      Name of the language library.
      If language is not specified, default value will be used.
    • voice : string
      Name of the voice library.
      If voice is not specified, first found one will be used.
    • subscribe_topic_name : string
      Topic name that the talker node subscribes. The message type of the topic is std_msgs/String.
      Default : text
    • publish_topic_name : string
      Topic name that the talker node publishes speech data. The message type of the topic is std_msgs/ByteMultiArray.
      If publish_topic_name is not specified, the speech data will be played by local computer which the talker node runs on.
    • phrase_dictionary : string
      Path of the phrase dictionary.
      Default : <Documents folder>/VOICEROID2/フレーズ辞書/user.pdic
    • word_dictionary : string
      Path of the word dictionary.
      Default : <Documents folder>/VOICEROID2/単語辞書/user.wdic
    • symbol_dictionary : string
      Path of the symbol dictionary.
      Default : <Documents folder>/VOICEROID2/記号ポーズ辞書/user.sdic
    • play_mode : string
      Behavior of playing multiple sound.
      • stop : Stop previous sound.
      • wait : Wait for finishing previous sound.
      • overlap : Play simultaneously.
Owner
Nkyoku
Nkyoku
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Payphone 8 Nov 21, 2022
Fastshap: A fast, approximate shap kernel

fastshap: A fast, approximate shap kernel fastshap was designed to be: Fast Calculating shap values can take an extremely long time. fastshap utilizes

Samuel Wilson 22 Sep 24, 2022
Python Implementation of the CoronaWarnApp (CWA) Event Registration

Python implementation of the Corona-Warn-App (CWA) Event Registration This is an implementation of the Protocol used to generate event and location QR

MaZderMind 17 Oct 05, 2022
[NeurIPS 2020] Official Implementation: "SMYRF: Efficient Attention using Asymmetric Clustering".

SMYRF: Efficient attention using asymmetric clustering Get started: Abstract We propose a novel type of balanced clustering algorithm to approximate a

Giannis Daras 46 Dec 22, 2022
A system used to detect whether a person is wearing a medical mask or not.

Mask_Detection_System A system used to detect whether a person is wearing a medical mask or not. To open the program, please follow these steps: Make

Mohamed Emad 0 Nov 17, 2022
Fast and customizable reconnaissance workflow tool based on simple YAML based DSL.

Fast and customizable reconnaissance workflow tool based on simple YAML based DSL, with support of notifications and distributed workload of that work

Américo Júnior 3 Mar 11, 2022
Towards Understanding Quality Challenges of the Federated Learning: A First Look from the Lens of Robustness

FL Analysis This repository contains the code and results for the paper "Towards Understanding Quality Challenges of the Federated Learning: A First L

3 Oct 17, 2022
Hierarchical Uniform Manifold Approximation and Projection

HUMAP Hierarchical Manifold Approximation and Projection (HUMAP) is a technique based on UMAP for hierarchical non-linear dimensionality reduction. HU

Wilson Estécio Marcílio Júnior 160 Jan 06, 2023
Software for Multimodalty 2D+3D Facial Expression Recognition (FER) UI

EmotionUI Software for Multimodalty 2D+3D Facial Expression Recognition (FER) UI. demo screenshot (with RealSense) required packages Python = 3.6 num

Yang Jiao 2 Dec 23, 2021
Implementation of Axial attention - attending to multi-dimensional data efficiently

Axial Attention Implementation of Axial attention in Pytorch. A simple but powerful technique to attend to multi-dimensional data efficiently. It has

Phil Wang 250 Dec 25, 2022
GPT, but made only out of gMLPs

GPT - gMLP This repository will attempt to crack long context autoregressive language modeling (GPT) using variations of gMLPs. Specifically, it will

Phil Wang 80 Dec 01, 2022
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

JAX: Autograd and XLA Quickstart | Transformations | Install guide | Neural net libraries | Change logs | Reference docs | Code search News: JAX tops

Google 21.3k Jan 01, 2023
LBK 26 Dec 28, 2022
A production-ready, scalable Indexer for the Jina neural search framework, based on HNSW and PSQL

🌟 HNSW + PostgreSQL Indexer HNSWPostgreSQLIndexer Jina is a production-ready, scalable Indexer for the Jina neural search framework. It combines the

Jina AI 25 Oct 14, 2022
Hierarchical Metadata-Aware Document Categorization under Weak Supervision (WSDM'21)

Hierarchical Metadata-Aware Document Categorization under Weak Supervision This project provides a weakly supervised framework for hierarchical metada

Yu Zhang 53 Sep 17, 2022
NeurIPS 2021 Datasets and Benchmarks Track

AP-10K: A Benchmark for Animal Pose Estimation in the Wild Introduction | Updates | Overview | Download | Training Code | Key Questions | License Intr

AP-10K 82 Dec 11, 2022
ETMO: Evolutionary Transfer Multiobjective Optimization

ETMO: Evolutionary Transfer Multiobjective Optimization To promote the research on ETMO, benchmark problems are of great importance to ETMO algorithm

Songbai Liu 0 Mar 16, 2021
quantize aware training package for NCNN on pytorch

ncnnqat ncnnqat is a quantize aware training package for NCNN on pytorch. Table of Contents ncnnqat Table of Contents Installation Usage Code Examples

62 Nov 23, 2022
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

Felix Wimbauer 494 Jan 06, 2023
Unsupervised Foreground Extraction via Deep Region Competition

Unsupervised Foreground Extraction via Deep Region Competition [Paper] [Code] The official code repository for NeurIPS 2021 paper "Unsupervised Foregr

28 Nov 06, 2022