glow-speak is a fast, local, neural text to speech system that uses eSpeak-ng as a text/phoneme front-end.

Related tags

Text Data & NLPglow-speak
Overview

Glow-Speak

glow-speak is a fast, local, neural text to speech system that uses eSpeak-ng as a text/phoneme front-end.

Installation

git clone https://github.com/rhasspy/glow-speak.git
cd glow-speak/

python3 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install --upgrade setuptools wheel
pip3 install -f 'https://synesthesiam.github.io/prebuilt-apps/' -r requirements.txt

python3 setup.py develop
glow-speak --version

Voices

The following languages/voices are supported:

  • German
    • de_thorsten
  • Chinese
    • cmn_jing_li
  • Greek
    • el_rapunzelina
  • English
    • en-us_ljspeech
    • en-us_mary_ann
  • Spanish
    • es_tux
  • Finnish
    • fi_harri_tapani_ylilammi
  • French
    • fr_siwis
  • Hungarian
    • hu_diana_majlinger
  • Italian
    • it_riccardo_fasol
  • Korean
    • ko_kss
  • Dutch
    • nl_rdh
  • Russian
    • ru_nikolaev
  • Swedish
    • sv_talesyntese
  • Swahili
    • sw_biblia_takatifu
  • Vietnamese
    • vi_vais1000

Usage

Download Voices

glow-speak-download de_thorsten

Command-Line Synthesis

glow-speak -v en-us_mary_ann 'This is a test.' --output-file test.wav

HTTP Server

glow-speak-http-server --debug

Visit http://localhost:5002

Socket Server

Start the server:

glow-speak-socket-server --voice en-us_mary_ann --socket /tmp/glow-speak.sock

From a separate terminal:

echo 'This is a test.' | bin/glow-speak-socket-client --socket /tmp/glow-speak.sock | xargs aplay

Lines from client to server are synthesized, and the path to the WAV file is returned (usually in /tmp).

You might also like...
End-to-End Speech Processing Toolkit
End-to-End Speech Processing Toolkit

ESPnet: end-to-end speech processing toolkit system/pytorch ver. 1.0.1 1.1.0 1.2.0 1.3.1 1.4.0 1.5.1 1.6.0 1.7.1 1.8.1 ubuntu18/python3.8/pip ubuntu18

ESPnet 5.9k Jan 3, 2023
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

OpenSpeech provides reference implementations of various ASR modeling papers and three languages recipe to perform tasks on automatic speech recogniti

Soohwan Kim 26 Dec 14, 2022
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

OpenSpeech provides reference implementations of various ASR modeling papers and three languages recipe to perform tasks on automatic speech recogniti

Soohwan Kim 86 Jun 11, 2021
Athena is an open-source implementation of end-to-end speech processing engine.

Athena is an open-source implementation of end-to-end speech processing engine. Our vision is to empower both industrial application and academic research on end-to-end models for speech processing. To make speech processing available to everyone, we're also releasing example implementation and recipe on some opensource dataset for various tasks (Automatic Speech Recognition, Speech Synthesis, Voice Conversion, Speaker Recognition, etc).

Ke Technologies 34 Sep 8, 2022
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

🤗 Contributing to OpenSpeech 🤗 OpenSpeech provides reference implementations of various ASR modeling papers and three languages recipe to perform ta

Openspeech TEAM 513 Jan 3, 2023
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation

SHAS: Approaching optimal Segmentation for End-to-End Speech Translation In this repo you can find the code of the Supervised Hybrid Audio Segmentatio

Machine Translation @ UPC 21 Dec 20, 2022
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

CRNN paper：An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition 1. create your ow

Tsukinousag1 3 Apr 2, 2022
Official PyTorch code for ClipBERT, an efficient framework for end-to-end learning on image-text and video-text tasks

Official PyTorch code for ClipBERT, an efficient framework for end-to-end learning on image-text and video-text tasks. It takes raw videos/images + text as inputs, and outputs task predictions. ClipBERT is designed based on 2D CNNs and transformers, and uses a sparse sampling strategy to enable efficient end-to-end video-and-language learning.

Jie Lei 雷杰 612 Jan 4, 2023
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge

Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge This is an implementation of the paper,

Mutian He 19 Oct 14, 2022
Comments
  • AssertionError on web interface (only) - and Raspberry Pi Bullseye test

    AssertionError on web interface (only) - and Raspberry Pi Bullseye test

    Hi Micheal,

    great work again! :smiley:

    I just saw this repository and thought I'd give it a try on my freshly installed Raspberry Pi 4 with 32bit Raspberry Pi OS Bullseye (Debian 11). Installation almost finished without errors! :partying_face: ... I just had to fix one thing: sudo apt-get install libatlas-base-dev After 15min I was already generating audio :grin: :+1:

    When I tested en mary_ann and thorsten_de via the web interface I got this error as soon as my test sentence ended with a question mark:

    DEBUG:glow-speak:ɪ_z ð_ɪ_s ɐ_n_ˈʌ_ð_ɚ t_ˈɛ_s_t? .
ERROR:glow_speak.http_server:
Traceback (most recent call last):
  File "/home/pi/glow-speak/.venv/lib/python3.9/site-packages/quart/app.py", line 1490, in full_dispatch_request
    result = await self.dispatch_request(request_context)
  File "/home/pi/glow-speak/.venv/lib/python3.9/site-packages/quart/app.py", line 1536, in dispatch_request
    return await self.ensure_async(handler)(**request_.view_args)
  File "/home/pi/glow-speak/glow_speak/http_server.py", line 484, in app_say
    wav_bytes = await text_to_wav(text, voice, **tts_args)
  File "/home/pi/glow-speak/glow_speak/http_server.py", line 323, in text_to_wav
    text_ids = text_to_ids(
  File "/home/pi/glow-speak/glow_speak/__init__.py", line 110, in text_to_ids
    text_ids = phonemes2ids(
  File "/home/pi/glow-speak/.venv/lib/python3.9/site-packages/phonemes2ids/__init__.py", line 190, in phonemes2ids
    maybe_extend_ids(sub_phoneme, word_ids, append_list=False)
  File "/home/pi/glow-speak/.venv/lib/python3.9/site-packages/phonemes2ids/__init__.py", line 108, in maybe_extend_ids
    maybe_ids = missing_func(phoneme)
  File "/home/pi/glow-speak/glow_speak/__init__.py", line 59, in guess_ids
    typing.List[Phoneme], guess_phonemes(phoneme, self.to_phonemes)
  File "/home/pi/glow-speak/.venv/lib/python3.9/site-packages/gruut_ipa/accent.py", line 159, in guess_phonemes
    assert dist_split is not None
AssertionError

    Maybe some encoding error when reading the web input?

    Speed seems pretty good, comparable to Larynx I'd say :+1: and I noticed the pronunciations have been improved for German :clap: :sunglasses:

    opened by fquirin 0
Owner
Rhasspy
Offline voice assistant
Rhasspy
GitHub Repository
Download videos from YouTube/Twitch/Twitter right in the Windows Explorer, without installing any shady shareware apps

youtube-dl and ffmpeg Windows Explorer Integration Download videos from YouTube/Twitch/Twitter and more (any platform that is supported by youtube-dl)

Wolfgang 226 Dec 30, 2022
Implementation of ProteinBERT in Pytorch

ProteinBERT - Pytorch (wip) Implementation of ProteinBERT in Pytorch. Original Repository Install $ pip install protein-bert-pytorch Usage import torc

Phil Wang 92 Dec 25, 2022
Wrapper to display a script output or a text file content on the desktop in sway or other wlroots-based compositors

nwg-wrapper This program is a part of the nwg-shell project. This program is a GTK3-based wrapper to display a script output, or a text file content o

Piotr Miller 94 Dec 27, 2022
Skipgram Negative Sampling in PyTorch

PyTorch SGNS Word2Vec's SkipGramNegativeSampling in Python. Yet another but quite general negative sampling loss implemented in PyTorch. It can be use

Jamie J. Seol 287 Dec 14, 2022
TPlinker for NER 中文/英文命名实体识别

本项目是参考 TPLinker 中HandshakingTagging思想，将TPLinker由原来的关系抽取(RE)模型修改为命名实体识别(NER)模型。

GodK 113 Dec 28, 2022
Korean Sentence Embedding Repository

Korean-Sentence-Embedding 🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides

80 Jan 02, 2023
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN

artificial intelligence cosmic love and attention fire in the sky a pyramid made of ice a lonely house in the woods marriage in the mountains lantern

Phil Wang 2.3k Jan 01, 2023
Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.

Multilabel time series classification with LSTM Tensorflow implementation of model discussed in the following paper: Learning to Diagnose with LSTM Re

Aaqib 552 Nov 28, 2022
Wind Speed Prediction using LSTMs in PyTorch

Implementation of Deep-Forecast using PyTorch Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting Adapted from original implementation Setu

Onur Kaplan 151 Dec 14, 2022
Translates basic English sentences into the Huna language (hoo-NAH)

huna-translator The Huna Language Translates basic English sentences into the Huna language (hoo-NAH). The Huna constructed language was developed in

Miles Smith 0 Jan 20, 2022
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing

Trankit: A Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing Trankit is a light-weight Transformer-based Pyth

652 Jan 06, 2023
Generating Korean Slogans with phonetic and structural repetition

LexPOS_ko Generating Korean Slogans with phonetic and structural repetition Generating Slogans with Linguistic Features LexPOS is a sequence-to-sequen

Yeoun Yi 3 May 23, 2022
Code examples for my Write Better Python Code series on YouTube.

Write Better Python Code This repository contains the code examples used in my Write Better Python Code series published on YouTube: https:/

858 Dec 29, 2022
Dé op-de-vlucht Pieton vertaler. Wereldwijd gebruikt door meer dan 1.000+ succesvolle bedrijven!

Dé op-de-vlucht Pieton vertaler. Wereldwijd gebruikt door meer dan 1.000+ succesvolle bedrijven!

Lau 1 Dec 17, 2021
Natural Language Processing Tasks and Examples.

Natural Language Processing Tasks and Examples With the advancement of A.I. technology in recent years, natural language processing technology has bee

Soohwan Kim 53 Dec 20, 2022
edge-SR: Super-Resolution For The Masses

edge-SR: Super Resolution For The Masses Citation Pablo Navarrete Michelini, Yunhua Lu and Xingqun Jiang. "edge-SR: Super-Resolution For The Masses",

Pablo 40 Nov 10, 2022
A simple Speech Emotion Recognition (SER) API created using Flask and running in a Docker container.

keyword_searching Steps to use this Python scripts： (1)Paste this script into the file folder containing the PDF files you need to search from; (2)Thi

2 Nov 11, 2022
Deep learning for NLP crash course at ABBYY.

Deep NLP Course at ABBYY Deep learning for NLP crash course at ABBYY. Suggested textbook: Neural Network Methods in Natural Language Processing by Yoa

Dan Anastasyev 597 Dec 18, 2022
This repository serves as a place to document a toy attempt on how to create a generative text model in Catalan, based on GPT-2

GPT-2 Catalan playground and scripts to train a GPT-2 model either from scrath or from another pretrained model.

Laura 1 Jan 28, 2022
小布助手对话短文本语义匹配的一个baseline

oppo-text-match 小布助手对话短文本语义匹配的一个baseline 模型 参考：https://kexue.fm/archives/8213 base版本线下大概0.952，线上0.866（单模型，没做K-flod融合）。 训练 测试环境：tensorflow 1.15 + keras

苏剑林(Jianlin Su) 132 Dec 14, 2022