add README for examples/deepvoice3

examples/deepvoice3/README.md

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+# Deepvoice 3 
+
+Paddle implementation of deepvoice 3 in dynamic graph, a convolutional network based text-to-speech synthesis model. The implementation is based on [Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://arxiv.org/abs/1710.07654).
+
+We implement Deepvoice 3 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 .
+   ```
+
+### cmudict
+You also need to download cmudict for nltk, because convert text into phonemes with `cmudict`.
+
+```python
+import nltk
+nltk.download("punkt")
+nltk.download("cmudict")
+```
+
+## 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
+
+![DeepVoice3 model architecture](./_images/model_architecture.png)
+
+The model consists of an encoder, a decoder and a converter (and a speaker embedding for multispeaker models). The encoder, together with the decoder forms the seq2seq part of the model, and the converter forms the postnet part.
+
+## Project Structure
+
+├── data.py          data_processing 
+├── ljspeech.yaml    (example) configuration file
+├── sentences.txt    sample sentences
+├── synthesis.py     script to synthesize waveform from text
+├── train.py         script to train a model
+└── utils.py         utility functions
+
+## train
+
+Train the model using train.py, follow the usage displayed by `python train.py --help`.
+
+```text
+usage: train.py [-h] [-c CONFIG] [-s DATA] [-r RESUME] [-o OUTPUT] [-g DEVICE]
+
+Train a deepvoice 3 model with LJSpeech dataset.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -c CONFIG, --config CONFIG
+                        experimrnt config
+  -s DATA, --data DATA  The path of the LJSpeech dataset.
+  -r RESUME, --resume RESUME
+                        checkpoint to load
+  -o OUTPUT, --output OUTPUT
+                        The directory to save result.
+  -g DEVICE, --device DEVICE
+                        device to use
+``` 
+
+1. `--config` is the configuration file to use. The provided `ljspeech.yaml` can be used directly. And you can change some values in the configuration file and train the model with a different config.
+2. `--data` is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains metadata.txt).
+3. `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before trainig.
+4. `--output` is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.
+
+```text
+├── checkpoints      # checkpoint
+├── log              # tensorboard log
+└── states           # train and evaluation results
+    ├── alignments   # attention 
+    ├── lin_spec     # linear spectrogram
+    ├── mel_spec     # mel spectrogram
+    └── waveform     # waveform (.wav files)
+```
+
+5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
+
+## synthesis
+```text
+usage: synthesis.py [-h] [-c CONFIG] [-g DEVICE] checkpoint text output_path
+
+Synthsize waveform with a checkpoint.
+
+positional arguments:
+  checkpoint            checkpoint to load.
+  text                  text file to synthesize
+  output_path           path to save results
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -c CONFIG, --config CONFIG
+                        experiment config.
+  -g DEVICE, --device DEVICE
+                        device to use
+```
+
+1. `--config` is the configuration file to use. You should use the same configuration with which you train you model.
+2. `checkpoint` is the checkpoint to load.
+3. `text`is the text file to synthesize.
+4. `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`) and attention plots (*.png) for each sentence.
+5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
+