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README.md

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+# WaveNet-like vocoder models
+
+Basic implementations of WaveNet and modified FFTNet in PyTorch. The project structre is brought from [pytorch-template].
+
+## Requirements
+* NumPy
+* SciPy
+* PyTorch >= 0.4.1
+* tqdm
+* librosa
+
+## Quick Start
+
+The code in this repo by default will train a WaveNet (or FFTNet) using 80-dimension mel-spectrogram with linear interpolation.
+
+### Preprocess
+
+Use `preprocess.py` to convert your wave files into mel-spectrograms.
+
+```
+python preprocess.py wave/files/folder -c config.json --out data
+```
+
+The preprocessed data will be stored in `./data`.
+You can change the configurations of "feature" in the `.json` file.
+
+### Train
+
+```
+python train.py -c config.json
+```
+
+### Test
+
+Use `preprocess.py` to convert a single wave file into mel-spectrogram feature.
+
+```
+python preprocess.py example.wav -c config.json --out test
+```
+
+The result is stored in `test.npz`.
+
+Then use the latest checkpoint file in the `./saved` folder to decoded `test.npz` back to waveform.
+The generating process will run on gpu if you add `--cuda`.
+
+```
+python test.py test.npz outfile.wav -r saved/your-model-name/XXXX_XXXXXX/checkpoint-stepXXXXX.pth --cuda
+```
+
+That's it. Other instructions and advanced usage can be found in [pytorch-template], I didn't change too much of the whole structure. 
+
+## Customization
+I add a new folder `feature` which is different from [pytorch-template].
+To use other feature like mfcc instead of mel-spectrogram, you can add your own function in `./feature/features.py` with similar arguments style of `get_logmel()`.
+
+Other customization method can be found in [pytorch-template].
+
+
+[pytorch-template]: https://github.com/victoresque/pytorch-template
+
+
+## Fast inference
+
+In `test.py` I implement fast-wavenet generation process in a very naive way. Use `fast_inference.py` you can get a huge speed up (CPU only).
+The speed is around 1500 samples/s on FFTNet and 300 samples/s on WaveNet.