tensorflow-wavenet

GitHub describes it as A TensorFlow implementation of DeepMind's WaveNet paper. The repository metadata lists Python as its primary language. The metadata lists the MIT license. This article stays within the project description and details documented in the GitHub repository README.

GitHub describes it as A TensorFlow implementation of DeepMind's WaveNet paper. The repository metadata lists Python as its primary language. The metadata lists the MIT license. The README describes the project this way: This is a TensorFlow implementation of the WaveNet generative neural network architecture for audio generation.

The README section "A TensorFlow implementation of DeepMind's WaveNet paper" states: The WaveNet neural network architecture directly generates a raw audio waveform, showing excellent results in text-to-speech and general audio generation (see the DeepMind blog post and paper for details). The network models the conditional probability to generate the next sample in the audio waveform, given all previous samples and possibly additional parameters. After an audio preprocessing step, the input waveform is quantized to a fixed integer range. The integer amplitudes are then one-hot encoded to produce a tensor of shape (num samples, num channels) . A convolutional layer that only accesses the current and previous inputs then reduces the channel dimension. The core of the network is constructed as a stack of causal dilated layers , each of which is a dilated convolution (convolution with holes), which only accesses the current and past audio samples. The outputs of all layers are combined and extended back to the original number of channels by a series of dense postprocessing layers, followed by a softmax function to transform the outputs into a categorical distribution. The loss function is the cross-entropy between the output for each timestep and the input at the next timestep. In this repository, the network implementation can be found in model.py .

The README section "Requirements" states: TensorFlow needs to be installed before running the training script. Code is tested on TensorFlow version 1.0.1 for Python 2.7 and Python 3.5.

The README section "Training the network" states: You can use any corpus containing .wav files. We've mainly used the VCTK corpus (around 10.4GB, Alternative host) so far.

The repository README is the source for this review. It does not replace a local installation or an independent test.

• Source 1 · Project repository
• Source 2 · README