<commit_before># A TensorFlow implementation of DeepMind's WaveNet paper

This is a TensorFlow implementation of the [WaveNet generative neural
network architecture](https://deepmind.com/blog/wavenet-generative-model-raw-audio/) for audio generation.

The WaveNet architecture directly produces a raw audio waveform as its output,
and shows excellent results in TTS and general audio generation (see the
DeepMind blog post and paper for examples).

The network is a model of the conditional probability to generate the next
sample in the audio waveform, given all previous samples and possibly
additional parameters.
It is constructed from a number of *causal dilated layers*, each of which is a
dilated convolution with a filter of width 2.

This implementation is currently still under :construction:

## Training the network

The [VCTK corpus](http://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html) is currently used.
In order to train the network, you need to download the corpus and unpack it in the same directory as the `run.py` script.

Then, execute
```python
python run.py
```
to train the network.

**Disclaimer:** This repository is not affiliated with DeepMind or Google.

<commit_msg>Adjust README.md<commit_after># A TensorFlow implementation of DeepMind's WaveNet paper

This is a TensorFlow implementation of the [WaveNet generative neural
network architecture](https://deepmind.com/blog/wavenet-generative-model-raw-audio/) for audio generation.

The WaveNet architecture directly generates a raw audio waveform,
and shows excellent results in TTS and general audio generation (see the
DeepMind blog post and paper for examples).

The network is a model of the conditional probability to generate the next
sample in the audio waveform, given all previous samples and possibly
additional parameters.
It is constructed from a stack of *causal dilated layers*, each of which is a
dilated convolution (convolution with holes) with a filter of width 2.

The network is implemented in the file [`wavenet.py`](./wavenet.py).

This implementation is currently still under :construction:

**TODO:**

 - Add step-by-step generation of audio samples (and make them available in TensorBoard)
 - Add conditioning on extra parameters (tagged speech, speaker, etc.)
 - Generate example outputs

## Training the network

The [VCTK corpus](http://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html) is currently used.
In order to train the network, you need to download the corpus and unpack it in the same directory as the `run.py` script.

Then, execute
```python
python main.py
```
to train the network.

**Disclaimer:** This repository is not affiliated with DeepMind or Google in any way.


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