diffwave 0.1.3

diffwave

DiffWave

DiffWave is a fast, high-quality neural vocoder and waveform synthesizer. It starts with Gaussian noise and converts it into speech via iterative refinement. The speech can be controlled by providing a conditioning signal (e.g. log-scaled Mel spectrogram). The model and architecture details are described in DiffWave: A Versatile Diffusion Model for Audio Synthesis.

Status (2020-09-23)

• stable training
• high-quality synthesis
• mixed-precision training
• multi-GPU training
• command-line inference
• programmatic inference API
• PyPI package
• audio samples
• pretrained models

Big thanks to Zhifeng Kong (lead author of DiffWave) for pointers and bug fixes.

Audio samples

...coming soon...

Pretrained models

...coming soon...

Install

Install using pip:

pip install diffwave

or from GitHub:

git clone https://github.com/lmnt-com/diffwave.git
cd diffwave
pip install .

Training

Before you start training, you'll need to prepare a training dataset. The dataset can have any directory structure as long as the contained .wav files are 16-bit mono (e.g. LJSpeech, VCTK). By default, this implementation assumes a sample rate of 22.05 kHz. If you need to change this value, edit params.py.

python -m diffwave.preprocess /path/to/dir/containing/wavs
python -m diffwave /path/to/model/dir /path/to/dir/containing/wavs

# in another shell to monitor training progress:
tensorboard --logdir /path/to/model/dir --bind_all

You should expect to hear intelligible (but noisy) speech by ~8k steps (~1.5h on a 2080 Ti).

Inference API

Basic usage:

from diffwave.inference import predict as diffwave_predict

model_dir = '/path/to/model/dir'
spectrogram = # get your hands on a spectrogram in [N,C,W] format
audio, sample_rate = diffwave_predict(spectrogram, model_dir)

# audio is a GPU tensor in [N,T] format.

Inference CLI

python -m diffwave.inference /path/to/model /path/to/spectrogram -o output.wav

References

• DiffWave: A Versatile Diffusion Model for Audio Synthesis
• Denoising Diffusion Probabilistic Models
• Code for Denoising Diffusion Probabilistic Models