Implementation (PyTorch) of Google Brain's WaveGrad vocoder (paper: https://arxiv.org/pdf/2009.00713.pdf).

• Model training is stable and supports multi-iteration inference (6 iterations also work perfect and produce high-fidelity samples).
• Model training runs on a single 12GB GPU machine.
• Model produces high-fidelity 22KHz generated samples. Uploaded samples for a different number of iterations.
• Estimated the real-time factor (RTF) for the model (see the table below). 100- and lower-iteration inference is faster than real-time on NVIDIA RTX 2080 Ti. 6-iteration model is faster than the model reported in the paper.
• Updated the code with new grid search utils for finding the best noise schedules.
• Uploaded pretrained on 22KHz LJSpeech model.

Number of parameters: 15810401

WaveGrad is a conditional model for waveform generation through estimating gradients of the data density. The main concept of vocoder is its relatedness to diffusion models based on Langevin dynamics and score matching frameworks. w.r.t. Langevin dynamics WaveGrad achieves super-fast convergence (6 iterations).

1. Clone this repo:

git clone https://github.com/ivanvovk/WaveGrad.git
cd WaveGrad

2. Install requirements pip install -r requirements.txt

1. Make filelists of your audio data like ones included into filelists folder.
2. Setup a configuration in configs folder.
3. Change config path in train.sh and run the script by sh train.sh.
4. To track training process run tensorboard by tensorboard --logdir=logs/YOUR_LOG_FOLDER.
5. Once model is trained, grid search the best schedule for a needed number of iterations in notebooks/inference.ipynb.

In order to make inference of your model follow the instructions provided in Jupyter Notebook notebooks/inference.ipynb. Also there is the code to estimate RTF in your runtime environment and to run best schedule grid search.

Current generated audios are provided in generated_samples folder. Quality degradation between 1000-iteration and 6-iteration inferences is not noticeable if found the best schedule for the latter.

You can find a pretrained model on LJSpeech (22KHz) via this link. Note: checkpoint is a dict with a single key 'model'.

• During training WaveGrad uses a default noise schedule with 1000 iterations and linear scale betas from range (1e-6, 0.01). For inference you can set another schedule with less iterations (6 iterations reported in paper are ok for this implementation!). Tune betas carefully, the output quality really highly depends on them.
• The best practice is to run grid search function iters_grid_search(...) from benchmark.py to find the best schedule for your number of iterations.
• Model training succesfully runs on a single 12GB GPU machine. Batch size is modified compared to the paper (256 -> 48, as authors trained their model on TPU). After ~10k iterations (1-2 hours) model performs good generation for 50-iteration inference. Total training time is about 1-2 days (for absolute convergence).
• At some point training might start to behave very weird and crazy (loss explodes), so I have introduced learning rate (LR) scheduling and gradient clipping.
• Hop length of your STFT is equal 300 by default (thus total upsampling factor). Other cases are not tested, but you can try. Change hop_length in your config and factors in config.model_config (their product should give your new hop length).
• It is crucial to have LINEAR_SCALE=5000 (already set by default) flag in model/linear_modulation.py. It rescales positional embeddings to have absolute amplitude 1/LINEAR_SCALE. It is improtant since continuous noise level and sinusoidal positional encodings both have equal range (-1, 1). No rescaling results that the model cannot properly extract noise info from positionally embedded continuous noise level and thus cannot extrapolate on longer mel-spectrogram sequences (longer than training segment: 7200 timepoints by default).

• (NEW) New RTF info for NVIDIA Tesla K80 GPU card (popular in Google Colab service) and CPU Intel Xeon 2.3GHz.
• Huge update. New 6-iteration well generated sample example. New noise schedule setting API. Added the best schedule grid search code.
• Improved training by introducing smarter learning rate scheduler. Obtained high-fidelity synthesis.
• Stable training and multi-iteration inference. 6-iteration noise scheduling is supported.
• Stable training and fixed-iteration inference with significant background static noise left. All positional encoding issues are solved.
• Stable training of 25-, 50- and 1000-fixed-iteration models. Found no linear scaling (C=5000 from paper) of positional encoding (bug).
• Stable training of 25-, 50- and 1000-fixed-iteration models. Fixed positional encoding downscaling. Parallel segment sampling is replaced by full-mel sampling.
• (RELEASE, first on GitHub). Parallel segment sampling and broken positional encoding downscaling. Bad quality with clicks from concatenation from parallel-segment generation.

• Nanxin Chen et al., WaveGrad: Estimating Gradients for Waveform Generation
• Jonathan Ho et al., Denoising Diffusion Probabilistic Models
• Denoising Diffusion Probabilistic Models repository, from which diffusion calculations have been adopted