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

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)
  • 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)

Baseline code for Korean open domain question answering(ODQA)

Open-Domain Question Answering(ODQA)는 다양한 주제에 대한 문서 집합으로부터 자연어 질의에 대한 답변을 찾아오는 task입니다. 이때 사용자 질의에 답변하기 위해 주어지는 지문이 따로 존재하지 않습니다. 따라서 사전에 구축되어있는 Knowl

VUMBLEB 69 Nov 04, 2022
Utilize Korean BERT model in sentence-transformers library

ko-sentence-transformers 이 프로젝트는 KoBERT 모델을 sentence-transformers 에서 보다 쉽게 사용하기 위해 만들어졌습니다. Ko-Sentence-BERT-SKTBERT 프로젝트에서는 KoBERT 모델을 sentence-trans

Junghyun 40 Dec 20, 2022
BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia.

BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia. Its intended use is as input for neural models in natural languag

Benjamin Heinzerling 1.1k Jan 03, 2023
Reproduction process of BERT on SST2 dataset

BERT-SST2-Prod Reproduction process of BERT on SST2 dataset 安装说明 下载代码库 git clone https://github.com/JunnYu/BERT-SST2-Prod 进入文件夹,安装requirements pip ins

yujun 1 Nov 18, 2021
A look-ahead multi-entity Transformer for modeling coordinated agents.

baller2vec++ This is the repository for the paper: Michael A. Alcorn and Anh Nguyen. baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling

Michael A. Alcorn 30 Dec 16, 2022
Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

This is a fork of Fairseq(-py) with implementations of the following models: Pervasive Attention - 2D Convolutional Neural Networks for Sequence-to-Se

Maha 490 Dec 15, 2022
Trains an OpenNMT PyTorch model and SentencePiece tokenizer.

Trains an OpenNMT PyTorch model and SentencePiece tokenizer. Designed for use with Argos Translate and LibreTranslate.

Argos Open Tech 61 Dec 13, 2022
BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network)

BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network) BERTAC is a framework that combines a

6 Jan 24, 2022
A Python 3.6+ package to run .many files, where many programs written in many languages may exist in one file.

RunMany Intro | Installation | VSCode Extension | Usage | Syntax | Settings | About A tool to run many programs written in many languages from one fil

6 May 22, 2022
Continuously update some NLP practice based on different tasks.

NLP_practice We will continuously update some NLP practice based on different tasks. prerequisites Software pytorch = 1.10 torchtext = 0.11.0 sklear

0 Jan 05, 2022
This repository contains helper functions which can help you generate additional data points depending on your NLP task.

NLP Albumentations For Data Augmentation This repository contains helper functions which can help you generate additional data points depending on you

Aflah 6 May 22, 2022
Paddlespeech Streaming ASR GUI

Paddlespeech-Streaming-ASR-GUI Introduction A paddlespeech Streaming ASR GUI. Us

Niek Zhen 3 Jan 05, 2022
This is a general repo that helps you develop fast/effective NLP classifiers using Huggingface

NLP Classifier Introduction This project trains a bert model on any NLP classifcation model. And uses the model in make predictions on new data using

Abdullah Tarek 3 Mar 11, 2022
Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models

Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model.

Prithivida 681 Jan 01, 2023
A paper list for aspect based sentiment analysis.

Aspect-Based-Sentiment-Analysis A paper list for aspect based sentiment analysis. Survey [IEEE-TAC-20]: Issues and Challenges of Aspect-based Sentimen

jiangqn 419 Dec 20, 2022
Easy Language Model Pretraining leveraging Huggingface's Transformers and Datasets

Easy Language Model Pretraining leveraging Huggingface's Transformers and Datasets What is LASSL • How to Use What is LASSL LASSL은 LAnguage Semi-Super

LASSL: LAnguage Self-Supervised Learning 116 Dec 27, 2022
Code for text augmentation method leveraging large-scale language models

HyperMix Code for our paper GPT3Mix and conducting classification experiments using GPT-3 prompt-based data augmentation. Getting Started Installing P

NAVER AI 47 Dec 20, 2022
Stand-alone language identification system

langid.py readme Introduction langid.py is a standalone Language Identification (LangID) tool. The design principles are as follows: Fast Pre-trained

2k Jan 04, 2023
A programming language with logic of Python, and syntax of all languages.

Pytov The idea was to take all well known syntaxes, and combine them into one programming language with many posabilities. Installation Install using

Yuval Rosen 14 Dec 07, 2022
Unlimited Call - Text Bombing Tool

FastBomber Unlimited Call - Text Bombing Tool Installation On Termux

Aryan 6 Nov 10, 2022