Giter Site home page Giter Site logo

tts's Introduction

TTS (Work in Progress...)

TTS targets a Text2Speech engine lightweight in computation with hight quality speech construction.

Here we have pytorch implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model as the start point. We plan to improve the model by the recent updated at the field.

You can find here a brief note pointing possible TTS architectures and their comparisons.

Requirements

Highly recommended to use miniconda for easier installation.

  • python 3.6
  • pytorch > 0.2.0
  • TODO

Data

Currently TTS provides data loaders for

Training the network

To run your own training, you need to define a config.json file (simple template below) and call with the command.

train.py --config_path config.json

If you like to use specific set of GPUs.

CUDA_VISIBLE_DEVICES="0,1,4" train.py --config_path config.json

Each run creates an experiment folder with the corresponfing date and time, under the folder you set in config.json. And if there is no checkpoint yet under that folder, it is going to be removed when you press Ctrl+C.

You can also enjoy Tensorboard with couple of good training logs, if you point --logdir the experiment folder.

Example config.json:

{
  // Data loading parameters
  "num_mels": 80,
  "num_freq": 1024,
  "sample_rate": 20000,
  "frame_length_ms": 50.0,
  "frame_shift_ms": 12.5,
  "preemphasis": 0.97,
  "min_level_db": -100,
  "ref_level_db": 20,
  "hidden_size": 128,
  "embedding_size": 256,
  "text_cleaner": "english_cleaners",

  // Training parameters
  "epochs": 2000,
  "lr": 0.001,
  "batch_size": 256,
  "griffinf_lim_iters": 60,
  "power": 1.5,
  "r": 5,            // number of decoder outputs for Tacotron

  // Number of data loader processes
  "num_loader_workers": 8,

  // Experiment logging parameters
  "checkpoint": true,  // if save checkpoint per save_step
  "save_step": 200,
  "data_path": "/path/to/KeithIto/LJSpeech-1.0",
  "output_path": "/path/to/my_experiment",
  "log_dir": "/path/to/my/tensorboard/logs/"
}

Testing

Best way to test your pretrained network is to use the Notebook under notebooks folder.

Contribution

Any kind of contribution is highly welcome as we are propelled by the open-source spirit. If you like to add or edit things in code, please also consider to write tests to verify your segment so that we can be sure things are on the track as this repo gets bigger.

TODO

tts's People

Contributors

erogol avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.