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)

Simple, hackable offline speech to text - using the VOSK-API.

Simple, hackable offline speech to text - using the VOSK-API.

Campbell Barton 844 Jan 07, 2023
Repo for Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization

ESACL: Enhanced Seq2Seq Autoencoder via Contrastive Learning for AbstractiveText Summarization This repo is for our paper "Enhanced Seq2Seq Autoencode

Rachel Zheng 14 Nov 01, 2022
BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions

BERTopic BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable

Maarten Grootendorst 3.6k Jan 07, 2023
Converts text into a PDF of handwritten notes

Text To Handwritten Notes Converts text into a PDF of handwritten notes Explore the docs » · Report Bug · Request Feature · Steps: $ git clone https:/

UVSinghK 63 Oct 09, 2022
Mastering Transformers, published by Packt

Mastering Transformers This is the code repository for Mastering Transformers, published by Packt. Build state-of-the-art models from scratch with adv

Packt 195 Jan 01, 2023
"Investigating the Limitations of Transformers with Simple Arithmetic Tasks", 2021

transformers-arithmetic This repository contains the code to reproduce the experiments from the paper: Nogueira, Jiang, Lin "Investigating the Limitat

Castorini 33 Nov 16, 2022
Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles

Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles (TASLP 2022)

Zhuosheng Zhang 3 Apr 14, 2022
BiNE: Bipartite Network Embedding

BiNE: Bipartite Network Embedding This repository contains the demo code of the paper: BiNE: Bipartite Network Embedding. Ming Gao, Leihui Chen, Xiang

leihuichen 214 Nov 24, 2022
DeepPavlov Tutorials

DeepPavlov tutorials DeepPavlov: Sentence Classification with Word Embeddings DeepPavlov: Transfer Learning with BERT. Classification, Tagging, QA, Ze

Neural Networks and Deep Learning lab, MIPT 28 Sep 13, 2022
A flask application to predict the speech emotion of any .wav file.

This is a speech emotion recognition app. It will allow you to train a modular MLP model with the RAVDESS dataset, and then use that model with a flask application to predict the speech emotion of an

Aryan Vijaywargia 2 Dec 15, 2021
Code for ACL 2020 paper "Rigid Formats Controlled Text Generation"

SongNet SongNet: SongCi + Song (Lyrics) + Sonnet + etc. @inproceedings{li-etal-2020-rigid, title = "Rigid Formats Controlled Text Generation",

Piji Li 212 Dec 17, 2022
Search-Engine - 📖 AI based search engine

Search Engine AI based search engine that was trained on 25000 samples, feel free to train on up to 1.2M sample from kaggle dataset, link below StackS

Vladislav Kruglikov 2 Nov 29, 2022
A number of methods in order to perform Natural Language Processing on live data derived from Twitter

A number of methods in order to perform Natural Language Processing on live data derived from Twitter

1 Nov 24, 2021
Repository for Project Insight: NLP as a Service

Project Insight NLP as a Service Contents Introduction Features Installation Setup and Documentation Project Details Demonstration Directory Details H

Abhishek Kumar Mishra 286 Dec 06, 2022
A Flask Sentiment Analysis API, with visual implementation

The Sentiment Analysis Api was created using python flask module,it allows users to parse a text or sentence throught the (?text) arguement, then view the sentiment analysis of that sentence. It can

Ifechukwudeni Oweh 10 Jul 17, 2022
Code for the paper PermuteFormer

PermuteFormer This repo includes codes for the paper PermuteFormer: Efficient Relative Position Encoding for Long Sequences. Directory long_range_aren

Peng Chen 42 Mar 16, 2022
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers

beyond masking Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers The code is coming Figure 1: Pipeline of token-based pre-

Yunjie Tian 23 Sep 27, 2022
PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis

PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis

YangHeng 567 Jan 07, 2023
Chinese NER with albert/electra or other bert descendable model (keras)

Chinese NLP (albert/electra with Keras) Named Entity Recognization Project Structure ./ ├── NER │   ├── __init__.py │   ├── log

2 Nov 20, 2022
GCRC: A Gaokao Chinese Reading Comprehension dataset for interpretable Evaluation

GCRC GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Eva

Yunxiao Zhao 5 Nov 04, 2022