add README of TransformerTTS

examples/transformer_tts/README.md

@@ -2,3 +2,145 @@
 Paddle fluid implementation of TransformerTTS, a neural TTS with Transformer. The implementation is based on [Neural Speech Synthesis with Transformer Network](https://arxiv.org/abs/1809.08895).
 
 We implement TransformerTTS model in paddle fluid with dynamic graph, which is convenient for flexible network architectures.
+
+## Installation
+
+### Install paddlepaddle
+This implementation requires the latest develop version of paddlepaddle. You can either download the compiled package or build paddle from source.
+1. Install the compiled package, via pip, conda or docker. See [**Installation Mannuals**](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/install/index_en.html) for more details.
+
+2. Build paddlepaddle from source. See [**Compile From Source Code**](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/install/compile/fromsource_en.html) for more details. Note that if you want to enable data parallel training for multiple GPUs, you should set `-DWITH_DISTRIBUTE=ON` with cmake.
+
+### Install parakeet
+You can choose to install via pypi or clone the repository and install manually.
+
+1. Install via pypi.
+   ```bash
+   pip install parakeet
+   ```
+
+2. Install manually.
+   ```bash
+   git clone <url>
+   cd Parakeet/
+   pip install -e .
+
+### Download cmudict for nltk
+You also need to download cmudict for nltk, because convert text into phonemes with `cmudict`.
+
+```python
+import nltk
+nltk.download("punkt")
+nltk.download("cmudict")
+```
+
+If you have completed all the above installations, but still report an error at runtime：
+
+``` OSError: sndfile library not found ```
+
+You need to install ```libsndfile``` using your distribution’s package manager. e.g. install via:
+
+``` sudo apt-get install libsndfile1 ```
+
+## Dataset
+
+We experiment with the LJSpeech dataset. Download and unzip [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
+
+```bash
+wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
+tar xjvf LJSpeech-1.1.tar.bz2
+```
+## Model Architecture
+
+![TransformerTTS model architecture](./images/model_architecture.jpg)
+The model adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in [Tacotron2](https://arxiv.org/abs/1712.05884). The model consists of two main parts, encoder and decoder. We also implemented CBHG model of tacotron as a vocoder part and converted the spectrogram into raw wave using griffin-lim algorithm.
+
+## Project Structure
+```text
+├── config                 # yaml configuration files
+├── data.py                # dataset and dataloader settings for LJSpeech
+├── synthesis.py           # script to synthesize waveform from text
+├── train_transformer.py   # script for transformer model training
+├── train_vocoder.py       # script for vocoder model training
+```
+
+## Train Transformer
+
+TransformerTTS model can train with ``train_transformer.py``.
+```bash
+python train_trasformer.py \
+--use_gpu=1 \
+--use_data_parallel=0 \
+--data_path=${DATAPATH} \
+--config_path='config/train_transformer.yaml' \
+```
+or you can run the script file directly.
+```bash
+sh train_transformer.sh
+```
+If you want to train on multiple GPUs, you must set ``--use_data_parallel=1``, and then start training as follow:
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog train_transformer.py \
+--use_gpu=1 \
+--use_data_parallel=0 \
+--data_path=${DATAPATH} \
+--config_path='config/train_transformer.yaml' \
+```
+
+if you wish to resume from an exists model, please set ``--checkpoint_path`` and ``--transformer_step``
+
+For more help on arguments: 
+``python train_transformer.py --help``.
+
+## Train Vocoder
+Vocoder model can train with ``train_vocoder.py``.
+```bash
+python train_vocoder.py \
+--use_gpu=1 \
+--use_data_parallel=0 \
+--data_path=${DATAPATH} \
+--config_path='config/train_vocoder.yaml' \
+```
+or you can run the script file directly.
+```bash
+sh train_vocoder.sh
+```
+If you want to train on multiple GPUs, you must set ``--use_data_parallel=1``, and then start training as follow:
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3
+python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog train_vocoder.py \
+--use_gpu=1 \
+--use_data_parallel=0 \
+--data_path=${DATAPATH} \
+--config_path='config/train_vocoder.yaml' \
+```
+if you wish to resume from an exists model, please set ``--checkpoint_path`` and ``--vocoder_step``
+
+For more help on arguments: 
+``python train_vocoder.py --help``.
+
+## Synthesis
+After training the transformerTTS and vocoder model, audio can be synthesized with ``synthesis.py``.
+```bash
+python synthesis.py \
+--max_len=50 \
+--transformer_step=160000 \
+--vocoder_step=70000 \
+--use_gpu=1
+--checkpoint_path='./checkpoint' \
+--sample_path='./sample' \
+--config_path='config/synthesis.yaml' \
+```
+
+or you can run the script file directly.
+```bash
+sh synthesis.sh
+```
+
+And the audio file will be saved in ``--sample_path``.
+
+For more help on arguments: 
+``python synthesis.py --help``.

examples/transformer_tts/synthesis.sh

@@ -1,10 +1,10 @@
 
 # train model
-#CUDA_VISIBLE_DEVICES=0,1,2,3 \
+CUDA_VISIBLE_DEVICES=0 \
 python -u synthesis.py \
 --max_len=50 \
 --transformer_step=160000 \
---postnet_step=70000 \
+--vocoder_step=70000 \
 --use_gpu=1
 --checkpoint_path='./checkpoint' \
 --log_dir='./log' \

examples/transformer_tts/train_transformer.sh

@@ -1,7 +1,7 @@
 
 # train model
 # if you wish to resume from an exists model, uncomment --checkpoint_path and --transformer_step
-#CUDA_VISIBLE_DEVICES=0,1,2,3 \
+CUDA_VISIBLE_DEVICES=0 \
 python -u train_transformer.py \
 --batch_size=32 \
 --epochs=10000 \

examples/transformer_tts/train_vocoder.sh

@@ -1,7 +1,7 @@
 
 # train model
-# if you wish to resume from an exists model, uncomment --checkpoint_path and --transformer_step
-#CUDA_VISIBLE_DEVICES=0,1,2,3 \
+# if you wish to resume from an exists model, uncomment --checkpoint_path and --vocoder_step
+CUDA_VISIBLE_DEVICES=0 \
 python -u train_vocoder.py \
 --batch_size=32 \
 --epochs=10000 \