adding readme and license

LICENSE

@@ -0,0 +1,25 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

@@ -0,0 +1,53 @@
+# Tacotron 2 (without wavenet)
+
+Tacotron 2 PyTorch implementation of [Natural TTS Synthesis By Conditioning
+Wavenet On Mel Spectrogram Predictions](https://arxiv.org/pdf/1712.05884.pdf). 
+
+This implementation includes **distributed** and **fp16** support
+and uses the [LJSpeech dataset](https://keithito.com/LJ-Speech-Dataset/).
+
+Distributed and FP16 support relies on work by Christian Sarofeen and NVIDIA's
+frameworks team.
+
+![Alignment, Predicted Mel Spectrogram, Target Mel Spectrogram](tensorboard.png)
+
+
+## Pre-requisites
+1. NVIDIA GPU + CUDA cuDNN
+
+## Setup
+1. Download and extract the [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/)
+2. Clone this repo: `git clone https://github.com/NVIDIA/tacotron2.git`
+3. CD into this repo: `cd tacotron2`
+4. Update .wav paths: `sed -i -- 's,DUMMY,ljs_dataset_folder/wavs,g' *.txt`
+5. Install [pytorch 0.4](https://github.com/pytorch/pytorch)
+6. Install python requirements or use docker container (tbd)
+    - Install python requirements: `pip install requirements.txt`
+    - **OR**
+    - Docker container `(tbd)` 
+
+## Training
+1. `python train.py --output_directory=outdir --log_directory=logdir`
+2. (OPTIONAL) `tensorboard --logdir=outdir/logdir`
+
+## Multi-GPU (distributed) and FP16 Training
+1. `python -m multiproc train.py --output_directory=/outdir --log_directory=/logdir --hparams=distributed_run=True`
+
+## Inference
+1. `jupyter notebook --ip=127.0.0.1 --port=31337`
+2. load inference.ipynb 
+
+## Related repos
+[nv-wavenet](https://github.com/NVIDIA/nv-wavenet/): Faster than real-time
+wavenet inference
+
+## Acknowledgements
+This implementation is inspired or uses code from the following repos:
+[Ryuchi Yamamoto](github.com/r9y9/tacotron_pytorch), [Keith
+Ito](https://github.com/keithito/tacotron/), [Prem Seetharaman](Prem
+Seetharaman's https://github.com/pseeth/pytorch-stft). 
+
+We are thankful to the Tacotron 2 paper authors, specially Jonathan Shen,
+Yuxuan Wang and Zongheng Yang.
+
+