Ukrainian TTS (text-to-speech) using Coqui TTS

Related tags

Text Data & NLPtext-to-speechttsukrainianukrainian-languagecoqui-ai
Overview
title emoji colorFrom colorTo sdk app_file pinned
Ukrainian TTS
🐸
green
green
gradio
app.py
false

Ukrainian TTS 📢 🤖

Ukrainian TTS (text-to-speech) using Coqui TTS.

Trained on M-AILABS Ukrainian dataset using sumska voice.

Link to online demo -> https://huggingface.co/spaces/robinhad/ukrainian-tts

Support

If you like my work, please support -> SUPPORT LINK

Example

test.mp4

How to use :

  1. pip install -r requirements.txt.
  2. Download model from "Releases" tab.
  3. Launch as one-time command:
tts --text "Text for TTS" \
    --model_path path/to/model.pth.tar \
    --config_path path/to/config.json \
    --out_path folder/to/save/output.wav

or alternatively launch web server using:

tts-server --model_path path/to/model.pth.tar \
    --config_path path/to/config.json

How to train:

  1. Refer to "Nervous beginner guide" in Coqui TTS docs.
  2. Instead of provided config.json use one from this repo.

Attribution

Code for app.py taken from https://huggingface.co/spaces/julien-c/coqui

Comments
  • Error with file: speakers.pth

    Error with file: speakers.pth

    FileNotFoundError: [Errno 2] No such file or directory: '/home/user/Soft/Python/mamba1/TTS/vits_mykyta_latest-September-12-2022_12+38AM-829e2c24/speakers.pth'

    opened by akirsoft 4
  • doc: fix examples in README

    doc: fix examples in README

    Problem

    The one-time snippet does not work as is and complains that the speaker is not defined

     > initialization of speaker-embedding layers.
 > Text: Перевірка мікрофона
 > Text splitted to sentences.
['Перевірка мікрофона']
Traceback (most recent call last):
  File "/home/serg/.local/bin/tts", line 8, in <module>
    sys.exit(main())
  File "/home/serg/.local/lib/python3.8/site-packages/TTS/bin/synthesize.py", line 350, in main
    wav = synthesizer.tts(
  File "/home/serg/.local/lib/python3.8/site-packages/TTS/utils/synthesizer.py", line 228, in tts
    raise ValueError(
ValueError:  [!] Look like you use a multi-speaker model. You need to define either a `speaker_name` or a `speaker_wav` to use a multi-speaker model.

    Also it speakers.pth should be downloaded.

    Fix

    Just a few documentation changes:

    • make instructions on what to download from Releases more precise
    • add --speaker_id argument with one of the speakers
    opened by seriar 2
  • One vowel words in the end of the sentence aren't stressed

    One vowel words in the end of the sentence aren't stressed

    Input:

    
Бобер на березі з бобренятами бублики пік.

Боронила борона по боронованому полю.

Ішов Прокіп, кипів окріп, прийшов Прокіп - кипить окріп, як при Прокопі, так і при Прокопі і при Прокопенятах.

Сидить Прокоп — кипить окроп, Пішов Прокоп — кипить окроп. Як при Прокопові кипів окроп, Так і без Прокопа кипить окроп.

    Result:

    
Боб+ер н+а березі з бобрен+ятами б+ублики пік.

Борон+ила борон+а п+о борон+ованому п+олю.

Іш+ов Пр+окіп, кип+ів окр+іп, прийш+ов Пр+окіп - кип+ить окр+іп, +як пр+и Пр+окопі, т+ак +і пр+и Пр+окопі +і пр+и Прокопенятах.

Сид+ить Прок+оп — кип+ить окроп, Піш+ов Прок+оп — кип+ить окроп. +Як пр+и Пр+окопові кип+ів окроп, Т+ак +і б+ез Пр+окопа кип+ить окроп.```
    opened by robinhad 0
  • Error import StressOption

    Error import StressOption

    Traceback (most recent call last): File "/home/user/Soft/Python/mamba1/test.py", line 1, in from ukrainian_tts.tts import TTS, Voices, StressOption ImportError: cannot import name 'StressOption' from 'ukrainian_tts.tts'

    opened by akirsoft 0
  • Vits improvements

    Vits improvements 

    vitsArgs = VitsArgs(
    # hifi V3
    resblock_type_decoder = '2',
    upsample_rates_decoder = [8,8,4],
    upsample_kernel_sizes_decoder = [16,16,8],
    upsample_initial_channel_decoder = 256,
    resblock_kernel_sizes_decoder = [3,5,7],
    resblock_dilation_sizes_decoder = [[1,2], [2,6], [3,12]],
)
    opened by robinhad 0
  • Model improvement checklist

    Model improvement checklist

    • [x] Add Ukrainian accentor - https://github.com/egorsmkv/ukrainian-accentor
    • [ ] Fine-tune from existing checkpoint (e.g. VITS Ljspeech)
    • [ ] Try to increase fft_size, hop_length to match sample_rate accordingly
    • [ ] Include more dataset samples into model
    opened by robinhad 0
Releases(v4.0.0)

  • v4.0.0(Dec 10, 2022)

  • v3.0.0(Sep 14, 2022)

    This is a release of Ukrainian TTS model and checkpoint. License for this model is GNU GPL v3 License. This release has a stress support using + sign before vowels. Model was trained for 280 000 steps by @robinhad . Kudos to @egorsmkv for providing dataset for this model. Kudos to @proger for providing alignment scripts. Kudos to @dchaplinsky for Dmytro voice.

    Example:

    Test sentence:

    К+ам'ян+ець-Под+ільський - м+істо в Хмельн+ицькій +області Укра+їни, ц+ентр Кам'ян+ець-Под+ільської міськ+ої об'+єднаної територі+альної гром+ади +і Кам'ян+ець-Под+ільського рай+ону.

    Mykyta (male):

    https://user-images.githubusercontent.com/5759207/190852232-34956a1d-77a9-42b9-b96d-39d0091e3e34.mp4

    Olena (female):

    https://user-images.githubusercontent.com/5759207/190852238-366782c1-9472-45fc-8fea-31346242f927.mp4

    Dmytro (male):

    https://user-images.githubusercontent.com/5759207/190852251-db105567-52ba-47b5-8ec6-5053c3baac8c.mp4

    Olha (female):

    https://user-images.githubusercontent.com/5759207/190852259-c6746172-05c4-4918-8286-a459c654eef1.mp4

    Lada (female):

    https://user-images.githubusercontent.com/5759207/190852270-7aed2db9-dc08-4a9f-8775-07b745657ca1.mp4

    Source code(tar.gz)
    Source code(zip)
    config.json(12.07 KB)
    model-inference.pth(329.95 MB)
    model.pth(989.97 MB)
    speakers.pth(495 bytes)

  • v3.0.0-alpha(Sep 8, 2022)

  • v2.0.0(Jul 10, 2022)

    This is a release of Ukrainian TTS model and checkpoint using voice (7 hours) from Mykyta dataset. License for this model is GNU GPL v3 License. This release has a stress support using + sign before vowels. Model was trained for 140 000 steps by @robinhad . Kudos to @egorsmkv for providing Mykyta and Olena dataset.

    Example:

    Test sentence:

    К+ам'ян+ець-Под+ільський - м+істо в Хмельн+ицькій +області Укра+їни, ц+ентр Кам'ян+ець-Под+ільської міськ+ої об'+єднаної територі+альної гром+ади +і Кам'ян+ець-Под+ільського рай+ону.

    Mykyta (male):

    https://user-images.githubusercontent.com/5759207/178158485-29a5d496-7eeb-4938-8ea7-c345bc9fed57.mp4

    Olena (female):

    https://user-images.githubusercontent.com/5759207/178158492-8504080e-2f13-43f1-83f0-489b1f9cd66b.mp4

    Source code(tar.gz)
    Source code(zip)
    config.json(9.97 KB)
    model-inference.pth(329.95 MB)
    model.pth(989.72 MB)
    optimized.pth(329.95 MB)
    speakers.pth(431 bytes)

  • v2.0.0-beta(May 8, 2022)

    This is a beta release of Ukrainian TTS model and checkpoint using voice (7 hours) from Mykyta dataset. License for this model is GNU GPL v3 License. This release has a stress support using + sign before vowels. Model was trained for 150 000 steps by @robinhad . Kudos to @egorsmkv for providing Mykyta dataset.

    Example:

    https://user-images.githubusercontent.com/5759207/167305810-2b023da7-0657-44ac-961f-5abf1aa6ea7d.mp4

    :

    Source code(tar.gz)
    Source code(zip)
    config.json(8.85 KB)
    LICENSE(34.32 KB)
    model-inference.pth(317.15 MB)
    model.pth(951.32 MB)
    tts_output.wav(1.11 MB)

  • v1.0.0(Jan 14, 2022)

  • v0.0.1(Oct 14, 2021)

Owner
Yurii Paniv
AI and stuff
Yurii Paniv
GitHub Repository https://huggingface.co/spaces/robinhad/ukrainian-tts
PyTorch implementation of Microsoft's text-to-speech system FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.

An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"

Chung-Ming Chien 1k Dec 30, 2022
NLP command-line assistant powered by OpenAI

NLP command-line assistant powered by OpenAI

Axel 16 Dec 09, 2022
ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体

ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体，包括上市公司所属行业关系、行业上级关系、产品上游原材料关系、产品下游产品关系、公司主营产品、产品小类共6大类。 上市公司4,654家，行业511个，产品95,559条、上游材料56,824条，上级行业480条，下游产品390条，产品小类52,937条，所属行业3,946条。

liuhuanyong 415 Jan 06, 2023
Language-Agnostic SEntence Representations

LASER Language-Agnostic SEntence Representations LASER is a library to calculate and use multilingual sentence embeddings. NEWS 2019/11/08 CCMatrix is

Facebook Research 3.2k Jan 04, 2023
An open source framework for seq2seq models in PyTorch.

pytorch-seq2seq Documentation This is a framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. The framework has modularized and

International Business Machines 1.4k Jan 02, 2023
天池中药说明书实体识别挑战冠军方案；中文命名实体识别；NER; BERT-CRF & BERT-SPAN & BERT-MRC；Pytorch

天池中药说明书实体识别挑战冠军方案；中文命名实体识别；NER; BERT-CRF & BERT-SPAN & BERT-MRC；Pytorch

zxx飞翔的鱼 751 Dec 30, 2022
Framework for fine-tuning pretrained transformers for Named-Entity Recognition (NER) tasks

NERDA Not only is NERDA a mesmerizing muppet-like character. NERDA is also a python package, that offers a slick easy-to-use interface for fine-tuning

Ekstra Bladet 141 Dec 30, 2022
A Paper List for Speech Translation

Keyword: Speech Translation, Spoken Language Processing, Natural Language Processing

138 Dec 24, 2022
glow-speak is a fast, local, neural text to speech system that uses eSpeak-ng as a text/phoneme front-end.

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://g

Rhasspy 8 Dec 25, 2022
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

LancoPKU 105 Jan 03, 2023
Code for using and evaluating SpanBERT.

SpanBERT This repository contains code and models for the paper: SpanBERT: Improving Pre-training by Representing and Predicting Spans. If you prefer

Meta Research 798 Dec 30, 2022
💫 Industrial-strength Natural Language Processing (NLP) in Python

spaCy: Industrial-strength NLP spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest researc

Explosion 24.9k Jan 02, 2023
使用pytorch+transformers复现了SimCSE论文中的有监督训练和无监督训练方法

SimCSE复现 项目描述 SimCSE是一种简单但是很巧妙的NLP对比学习方法，创新性地引入Dropout的方式，对样本添加噪声，从而达到对正样本增强的目的。 该框架的训练目的为：对于batch中的每个样本，拉近其与正样本之间的距离，拉远其与负样本之间的距离，使得模型能够在大规模无监督语料（也可以

58 Dec 20, 2022
Voilà turns Jupyter notebooks into standalone web applications

Rendering of live Jupyter notebooks with interactive widgets. Introduction Voilà turns Jupyter notebooks into standalone web applications. Unlike the

Voilà Dashboards 4.5k Jan 03, 2023
Snips Python library to extract meaning from text

Snips NLU Snips NLU (Natural Language Understanding) is a Python library that allows to extract structured information from sentences written in natur

Snips 3.7k Dec 30, 2022
A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis

WaveGlow A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis Quick Start: Install requirements: pip install

Yuchao Zhang 204 Jul 14, 2022
Code to reproduce the results of the paper 'Towards Realistic Few-Shot Relation Extraction' (EMNLP 2021)

Realistic Few-Shot Relation Extraction This repository contains code to reproduce the results in the paper "Towards Realistic Few-Shot Relation Extrac

Bloomberg 8 Nov 09, 2022
:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.

(Framework for Adapting Representation Models) What is it? FARM makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built u

deepset 1.6k Dec 27, 2022
This github repo is for Neurips 2021 paper, NORESQA A Framework for Speech Quality Assessment using Non-Matching References.

NORESQA: Speech Quality Assessment using Non-Matching References This is a Pytorch implementation for using NORESQA. It contains minimal code to predi

Meta Research 36 Dec 08, 2022
Contains analysis of trends from Fitbit Dataset (source: Kaggle) to see how the trends can be applied to Bellabeat customers and Bellabeat products

Contains analysis of trends from Fitbit Dataset (source: Kaggle) to see how the trends can be applied to Bellabeat customers and Bellabeat products.

Leah Pathan Khan 2 Jan 12, 2022