MelGAN STFT: MelGAN With Multi Resolution STFT Loss

Based on the script train_melgan_stft.py.

Training MelGAN STFT from scratch with LJSpeech dataset.

This example code show you how to train MelGAN from scratch with Tensorflow 2 based on custom training loop and tf.function. The data used for this example is LJSpeech, you can download the dataset at link.

Step 1: Create Tensorflow based Dataloader (tf.dataset)

Please see detail at examples/melgan/

Step 2: Training from scratch

After you re-define your dataloader, pls modify an input arguments, train_dataset and valid_dataset from train_melgan_stft.py. Here is an example command line to training melgan-stft from scratch:

First, you need training generator with only stft loss:

CUDA_VISIBLE_DEVICES=0 python examples/melgan_stft/train_melgan_stft.py \
  --train-dir ./dump/train/ \
  --dev-dir ./dump/valid/ \
  --outdir ./examples/melgan_stft/exp/train.melgan_stft.v1/ \
  --config ./examples/melgan_stft/conf/melgan_stft.v1.yaml \
  --use-norm 1
  --generator_mixed_precision 1 \
  --resume ""

Then resume and start training generator + discriminator:

CUDA_VISIBLE_DEVICES=0 python examples/melgan_stft/train_melgan_stft.py \
  --train-dir ./dump/train/ \
  --dev-dir ./dump/valid/ \
  --outdir ./examples/melgan_stft/exp/train.melgan_stft.v1/ \
  --config ./examples/melgan_stft/conf/melgan_stft.v1.yaml \
  --use-norm 1
  --resume ./examples/melgan_stft/exp/train.melgan_stft.v1/checkpoints/ckpt-100000

IF you want to use MultiGPU to training you can replace CUDA_VISIBLE_DEVICES=0 by CUDA_VISIBLE_DEVICES=0,1,2,3 for example. You also need to tune the batch_size for each GPU (in config file) by yourself to maximize the performance. Note that MultiGPU now support for Training but not yet support for Decode.

In case you want to resume the training progress, please following below example command line:

--resume ./examples/melgan_stft/exp/train.melgan_stft.v1/checkpoints/ckpt-100000

If you want to finetune a model, use --pretrained like this with the filename of the generator

--pretrained ptgenerator.h5

IMPORTANT NOTES:

When training generator only, we enable mixed precision to speed-up training progress.
We don't apply mixed precision when training both generator and discriminator. (Discriminator include group-convolution, which cause discriminator slower when enable mixed precision).
100k here is a discriminator_train_start_steps parameters from melgan_stft.v1.yaml

Finetune MelGAN STFT with ljspeech pretrained on other languages

Just load pretrained model and training from scratch with other languages. DO NOT FORGET re-preprocessing on your dataset if needed. A hop_size should be 256 if you want to use our pretrained.

Learning Curves

Here is a learning curves of melgan based on this config melgan_stft.v1.yaml

Some important notes

We apply learning rate = 1e-3 when training generator only then apply lr = 1e-4 for both G and D.
See examples/melgan for more notes.

Pretrained Models and Audio samples

Model	Conf	Lang	Fs [Hz]	Mel range [Hz]	FFT / Hop / Win [pt]	# iters
melgan_stft.v1	link	EN	22.05k	80-7600	1024 / 256 / None	1900k

Spaces:

vishred18
/

Comparative-Analysis-of-Speech-Synthesis-Models

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