README.md: updating requirements and inference demo

README.md

@@ -1,6 +1,6 @@
 # Tacotron 2 (without wavenet)
 
-Tacotron 2 PyTorch implementation of [Natural TTS Synthesis By Conditioning
+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
@@ -11,9 +11,7 @@ Distributed and FP16 support relies on work by Christian Sarofeen and NVIDIA's
 
 ![Alignment, Predicted Mel Spectrogram, Target Mel Spectrogram](tensorboard.png)
 
-[Download demo audio](https://github.com/NVIDIA/tacotron2/blob/master/demo.wav) trained on LJS and using Ryuchi Yamamoto's [pre-trained Mixture of Logistics
-wavenet](https://github.com/r9y9/wavenet_vocoder/)  
-"Scientists at the CERN laboratory say they have discovered a new particle."
+Visit our [website] for audio samples.
 
 ## Pre-requisites
 1. NVIDIA GPU + CUDA cuDNN
@@ -24,11 +22,9 @@ wavenet](https://github.com/r9y9/wavenet_vocoder/)
 3. CD into this repo: `cd tacotron2`
 4. Update .wav paths: `sed -i -- 's,DUMMY,ljs_dataset_folder/wavs,g' filelists/*.txt`
     - Alternatively, set `load_mel_from_disk=True` in `hparams.py` and update mel-spectrogram paths 
-5. Install [pytorch 0.4](https://github.com/pytorch/pytorch)
+5. Install [PyTorch 1.0]
 6. Install python requirements or build docker image 
     - Install python requirements: `pip install -r requirements.txt`
-    - **OR**
-    - Build docker image: `docker build --tag tacotron2 .` 
 
 ## Training
 1. `python train.py --output_directory=outdir --log_directory=logdir`
@@ -37,17 +33,22 @@ wavenet](https://github.com/r9y9/wavenet_vocoder/)
 ## Multi-GPU (distributed) and FP16 Training
 1. `python -m multiproc train.py --output_directory=outdir --log_directory=logdir --hparams=distributed_run=True,fp16_run=True`
 
-## Inference
-When performing Mel-Spectrogram to Audio synthesis with a WaveNet model, make sure Tacotron 2 and WaveNet were trained on the same mel-spectrogram representation. Follow these steps to use a a simple inference pipeline using griffin-lim:
-
-1. `jupyter notebook --ip=127.0.0.1 --port=31337`
-2. load inference.ipynb 
+## Inference demo
+1. Download our published [Tacotron 2] model
+2. Download our published [WaveGlow] model
+3. `jupyter notebook --ip=127.0.0.1 --port=31337`
+4. Load inference.ipynb 
 
+N.b.  When performing Mel-Spectrogram to Audio synthesis, make sure Tacotron 2
+and the Mel decoder were trained on the same mel-spectrogram representation. 
 
 
 ## Related repos
-[nv-wavenet](https://github.com/NVIDIA/nv-wavenet/): Faster than real-time
-wavenet inference
+[WaveGlow](https://github.com/NVIDIA/WaveGlow) Faster than real time Flow-based
+Generative Network for Speech Synthesis
+
+[nv-wavenet](https://github.com/NVIDIA/nv-wavenet/) Faster than real time
+WaveNet.
 
 ## Acknowledgements
 This implementation uses code from the following repos: [Keith
@@ -61,3 +62,7 @@ We are thankful to the Tacotron 2 paper authors, specially Jonathan Shen, Yuxuan
 Wang and Zongheng Yang.
 
 
+[WaveGlow]: https://drive.google.com/file/d/1cjKPHbtAMh_4HTHmuIGNkbOkPBD9qwhj/view?usp=sharing
+[Tacotron 2]: https://drive.google.com/file/d/1c5ZTuT7J08wLUoVZ2KkUs_VdZuJ86ZqA/view?usp=sharing
+[pytorch 1.0]: https://github.com/pytorch/pytorch#installation
+[website]: https://nv-adlr.github.io/WaveGlow