Implementation of MelNet: A Generative Model for Audio in the Frequency Domain

• Tested with Python 3.6.8 & 3.7.4, PyTorch 1.2.0 & 1.3.0.
• pip install -r requirements.txt

• Blizzard, VoxCeleb2, and KSS have YAML files provided under config/. For other datasets, fill out your own YAML file according to the other provided ones.
• Unconditional training is possible for all kinds of datasets, provided that they have a consistent file extension specified by data.extension within the YAML file.
• Conditional training is currently only implemented for KSS and a subset of the Blizzard dataset.

• python trainer.py -c [config YAML file path] -n [name of run] -t [tier number] -b [batch size] -s [TTS]
  • Each tier can be trained separately. Since each tier is larger than the one before it (with the exception of tier 1), modify the batch size for each tier.
    • Tier 6 of the Blizzard dataset does not fit on a 16GB P100, even with a batch size of 1.
  • The -s flag is a boolean for determining whether to train a TTS tier. Since a TTS tier only differs at tier 1, this flag is ignored when [tier number] != 0 . Warning: this flag is toggled True no matter what follows the flag. Ignore it if you're not planning to use it.

• The checkpoints must be stored under chkpt/.
• A YAML file named inference.yaml must be provided under config/.
• inference.yaml must specify the number of tiers, the names of the checkpoints, and whether or not it is a conditional generation.

• python inference.py -c [config YAML file path] -p [inference YAML file path] -t [timestep of generated mel spectrogram] -n [name of sample] -i [input sentence for conditional generation]
  • Timestep refers to the length of the mel spectrogram. The ratio of timestep to seconds is roughly [sample rate] : [hop length of FFT].
  • The -i flag is optional, only needed for conditional generation. Surround the sentence with "" and end with ..
  • Both unconditional generation and conditional generation currently does not support primed generation (extrapolating from provided data).

• Implement upsampling procedure
• GMM sampling + loss function
• Unconditional audio generation
• TTS synthesis
• Tensorboard logging
• Multi-GPU training
• Primed generation

• Seungwon Park, June Young Yi, Yoonhyung Lee, Joowhan Song @ Deepest Season 6

MIT License