diff --git a/examples/clarinet/README.md b/examples/clarinet/README.md
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+# Clarinet
+
+Paddle implementation of clarinet in dynamic graph, a convolutional network based vocoder. The implementation is based on the paper [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](arxiv.org/abs/1807.07281).
+
+
+## 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
+```
+
+## Project Structure
+
+```text
+├── data.py          data_processing
+├── configs/         (example) configuration file
+├── synthesis.py     script to synthesize waveform from mel_spectrogram
+├── 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] [--config CONFIG] [--device DEVICE] [--output OUTPUT]
+                [--data DATA] [--resume RESUME] [--conditioner CONDITIONER]
+                [--teacher TEACHER]
+
+train a clarinet model with LJspeech and a trained wavenet model.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --config CONFIG       path of the config file.
+  --device DEVICE       device to use.
+  --output OUTPUT       path to save student.
+  --data DATA           path of LJspeech dataset.
+  --resume RESUME       checkpoint to load from.
+  --conditioner CONDITIONER
+                        conditioner checkpoint to use.
+  --teacher TEACHER     teacher checkpoint to use.
+```
+
+1. `--config` is the configuration file to use. The provided configurations 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
+├── states           # audio files generated at validation
+└── log              # tensorboard log
+```
+
+5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
+6. `--conditioner` is the path of the checkpoint to load for the `conditioner` part of clarinet. if you do not specify `--resume`, then this must be provided.
+7. `--teacher` is the path of the checkpoint to load for the `teacher` part of clarinet. if you do not specify `--resume`, then this must be provided.
+
+Before you start training a clarinet model, you should have trained a wavenet model with single gaussian as output distribution. Make sure the config for teacher matches that for the trained model.
+
+example script:
+
+```bash
+python train.py --config=./configs/clarinet_ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0 --conditioner=wavenet_checkpoint/conditioner --conditioner=wavenet_checkpoint/teacher
+```
+
+You can monitor training log via tensorboard, using the script below.
+
+```bash
+cd experiment/log
+tensorboard --logdir=.
+```
+
+## Synthesis
+```text
+usage: synthesis.py [-h] [--config CONFIG] [--device DEVICE] [--data DATA]
+                    checkpoint output
+
+train a clarinet model with LJspeech and a trained wavenet model.
+
+positional arguments:
+  checkpoint       checkpoint to load from.
+  output           path to save student.
+
+optional arguments:
+  -h, --help       show this help message and exit
+  --config CONFIG  path of the config file.
+  --device DEVICE  device to use.
+  --data DATA      path of LJspeech dataset.
+```
+
+1. `--config` is the configuration file to use. You should use the same configuration with which you train you model.
+2.  `--data` is the path of the LJspeech dataset. A dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
+3. `checkpoint` is the checkpoint to load.
+4. `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`).
+5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
+
+example script:
+
+```bash
+python synthesis.py --config=./configs/wavenet_single_gaussian.yaml --data=./LJSpeech-1.1/ --device=0 experiment/checkpoints/step_500000 generated
+```
diff --git a/examples/clarinet/configs/clarinet_ljspeech.yaml b/examples/clarinet/configs/clarinet_ljspeech.yaml
index f689cca..7ceedcc 100644
--- a/examples/clarinet/configs/clarinet_ljspeech.yaml
+++ b/examples/clarinet/configs/clarinet_ljspeech.yaml
@@ -43,8 +43,8 @@ train:
   anneal_interval: 200000
   gradient_max_norm: 100.0
 
-  checkpoint_interval: 10
-  eval_interval: 10
+  checkpoint_interval: 1000
+  eval_interval: 1000
   
   max_iterations: 2000000