MelNet

Prerequisites

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).

MIT License