README.md update

README.md

@@ -1,7 +1,10 @@
 <p align="center"><img src="https://user-images.githubusercontent.com/1402048/52643646-c2102980-2edd-11e9-8c37-b72f3c89a640.png" data-canonical-src="![TTS banner](https://user-images.githubusercontent.com/1402048/52643646-c2102980-2edd-11e9-8c37-b72f3c89a640.png =250x250)
 " width="320" height="95" /></p>
 
+<center>
 <img src="https://travis-ci.org/mozilla/TTS.svg?branch=dev"/>
+[![Discourse](https://img.shields.io/badge/discourse-online-green.svg)](https://discourse.mozilla.org/c/tts)
+</center>
 
 This project is a part of [Mozilla Common Voice](https://voice.mozilla.org/en). TTS aims a deep learning based Text2Speech engine, low in cost and high in quality.
 
@@ -38,25 +41,26 @@ Vocoders:
 You can also help us implement more models. Some TTS related work can be found [here](https://github.com/erogol/TTS-papers).
 
 ## Features
-- High performance Deep Learning models for Text2Speech related tasks.
-    - Text2Speech models (Tacotron, Tacotron2).
+- High performance Deep Learning models for Text2Speech tasks.
+    - Text2Spec models (Tacotron, Tacotron2).
     - Speaker Encoder to compute speaker embeddings efficiently.
-    - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS)
-- Support for multi-speaker TTS training.
-- Support for Multi-GPUs training.
-- Ability to convert Torch models to Tensorflow 2.0 for inference.
-- Released pre-trained models.
+    - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN)
 - Fast and efficient model training.
 - Detailed training logs on console and Tensorboard.
+- Support for multi-speaker TTS.
+- Efficient Multi-GPUs training.
+- Ability to convert PyTorch models to Tensorflow 2.0 and TFLite for inference.
+- Released models in PyTorch, Tensorflow and TFLite.
 - Tools to curate Text2Speech datasets under```dataset_analysis```.
 - Demo server for model testing.
 - Notebooks for extensive model benchmarking.
 - Modular (but not too much) code base enabling easy testing for new ideas.
 
-## Requirements and Installation
+## Main Requirements and Installation
 Highly recommended to use [miniconda](https://conda.io/miniconda.html) for easier installation.
   * python>=3.6
-  * pytorch>=0.4.1
+  * pytorch>=1.4.1
+  * tensorflow>=2.2
   * librosa
   * tensorboard
   * tensorboardX
@@ -107,21 +111,7 @@ Audio examples: [soundcloud](https://soundcloud.com/user-565970875/pocket-articl
 
 <img src="images/example_model_output.png?raw=true" alt="example_output" width="400"/>
 
-## Runtime
-The most time-consuming part is the vocoder algorithm (Griffin-Lim) which runs on CPU. By setting its number of iterations lower, you might have faster execution with a small loss of quality. Some of the experimental values are below.
-
-Sentence: "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."
-
-Audio length is approximately 6 secs.
-
-| Time (secs) | System | # GL iters | Model
-| ---- |:-------|:-----------| ---- |
-|2.00|GTX1080Ti|30|Tacotron|
-|3.01|GTX1080Ti|60|Tacotron|
-|3.57|CPU|60|Tacotron|
-|5.27|GTX1080Ti|60|Tacotron2|
-|6.50|CPU|60|Tacotron2|
-
+## [Mozilla TTS Tutorials and Notebooks](https://github.com/mozilla/TTS/wiki/TTS-Notebooks-and-Tutorials)
 
 ## Datasets and Data-Loading
 TTS provides a generic dataloader easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.