From 20d3bc5e0d54a953a945bbed4cbed7b9aae4000d Mon Sep 17 00:00:00 2001 From: root Date: Wed, 13 Jan 2021 10:11:23 +0000 Subject: [PATCH] fix README --- README.md | 45 --------------------------------------------- 1 file changed, 45 deletions(-) diff --git a/README.md b/README.md index 06274985..300bb405 100644 --- a/README.md +++ b/README.md @@ -3,27 +3,15 @@ # TTS: Text-to-Speech for all. -<<<<<<< HEAD -

- mozilla - - -

-======= TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to be achive the best trade-off among ease-of-training, speed and quality. TTS comes with [pretrained models](https://github.com/mozilla/TTS/wiki/Released-Models), tools for measuring dataset quality and already used in **20+ languages** for products and research projects. ->>>>>>> dev [![CircleCI]()]() [![License]()](https://opensource.org/licenses/MPL-2.0) -<<<<<<< HEAD -TTS is a deep learning based Text2Speech project, low in cost and high in quality. -======= :loudspeaker: [English Voice Samples](https://erogol.github.io/ddc-samples/) and [SoundCloud playlist](https://soundcloud.com/user-565970875/pocket-article-wavernn-and-tacotron2) :man_cook: [TTS training recipes](https://github.com/erogol/TTS_recipes) ->>>>>>> dev :page_facing_up: [Text-to-Speech paper collection](https://github.com/erogol/TTS-papers) @@ -57,34 +45,6 @@ Please use our dedicated channels for questions and discussion. Help is much mor "Mozilla*" and "Judy*" are our models. [Details...](https://github.com/mozilla/TTS/wiki/Mean-Opinion-Score-Results) -<<<<<<< HEAD -## Provided Models and Methods -Text-to-Spectrogram: -- Tacotron: [paper](https://arxiv.org/abs/1703.10135) -- Tacotron2: [paper](https://arxiv.org/abs/1712.05884) -- Glow-TTS: [paper](https://arxiv.org/abs/2005.11129) - -Attention Methods: -- Guided Attention: [paper](https://arxiv.org/abs/1710.08969) -- Forward Backward Decoding: [paper](https://arxiv.org/abs/1907.09006) -- Graves Attention: [paper](https://arxiv.org/abs/1907.09006) -- Double Decoder Consistency: [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) - -Speaker Encoder: -- GE2E: [paper](https://arxiv.org/abs/1710.10467) - -Vocoders: -- MelGAN: [paper](https://arxiv.org/abs/1710.10467) -- MultiBandMelGAN: [paper](https://arxiv.org/abs/2005.05106) -- ParallelWaveGAN: [paper](https://arxiv.org/abs/1910.11480) -- GAN-TTS discriminators: [paper](https://arxiv.org/abs/1909.11646) -- WaveRNN: [origin](https://github.com/fatchord/WaveRNN/) -- WaveGrad: [paper](https://arxiv.org/abs/2009.00713) - -You can also help us implement more models. Some TTS related work can be found [here](https://github.com/erogol/TTS-papers). - -======= ->>>>>>> dev ## Features - High performance Deep Learning models for Text2Speech tasks. - Text2Spec models (Tacotron, Tacotron2). @@ -163,11 +123,6 @@ Audio examples: [soundcloud](https://soundcloud.com/user-565970875/pocket-articl example_output -<<<<<<< HEAD -## [TTS Tutorials and Notebooks](https://github.com/mozilla/TTS/wiki/TTS-Notebooks-and-Tutorials) - -======= ->>>>>>> dev ## Datasets and Data-Loading TTS provides a generic dataloader easy to use for your custom dataset. You just need to write a simple function to format the dataset. Check ```datasets/preprocess.py``` to see some examples.