Tacotron 2 (without wavenet)

PyTorch implementation of Natural TTS Synthesis By Conditioning Wavenet On Mel Spectrogram Predictions.

This implementation includes distributed and automatic mixed precision support and uses the LJSpeech dataset.

Distributed and Automatic Mixed Precision support relies on NVIDIA's Apex and AMP.

Visit our website for audio samples using our published Tacotron 2 and WaveGlow models.

Pre-requisites

1. NVIDIA GPU + CUDA cuDNN

Setup

1. Download and extract the LJ Speech dataset
2. Clone this repo: git clone https://github.com/NVIDIA/tacotron2.git
3. CD into this repo: cd tacotron2
4. Initialize submodule: git submodule init; git submodule update
5. Update .wav paths: sed -i -- 's,DUMMY,ljs_dataset_folder/wavs,g' filelists/*.txt
   • Alternatively, set load_mel_from_disk=True in hparams.py and update mel-spectrogram paths
6. Install PyTorch 1.0
7. Install Apex
8. Install python requirements or build docker image
   • Install python requirements: pip install -r requirements.txt

Training

1. python train.py --output_directory=outdir --log_directory=logdir
2. (OPTIONAL) tensorboard --logdir=outdir/logdir

Training using a pre-trained model

Training using a pre-trained model can lead to faster convergence
By default, the dataset dependent text embedding layers are ignored

1. Download our published Tacotron 2 model
2. python train.py --output_directory=outdir --log_directory=logdir -c tacotron2_statedict.pt --warm_start

Multi-GPU (distributed) and Automatic Mixed Precision Training

1. python -m multiproc train.py --output_directory=outdir --log_directory=logdir --hparams=distributed_run=True,fp16_run=True

Multi-GPU (distributed) Supervised learning

1. python -m multiproc train.py --output_directory=outdir_fulltacotron --log_directory=logdir --hparams=distributed_run=True,fp16_run=True,training_files=filelists/david/labelled/train.txt,validation_files=filelists/david/labelled/val.txt

Multi-GPU (distributed) Supervised learning with encoder conditioning

1. python -m multiproc train.py --output_directory=outdir --log_directory=logdir --hparams=distributed_run=True,fp16_run=True,training_files=filelists/david/labelled/val.txt,validation_files=filelists/david/labelled/val.txt,encoder_conditioning=True
2. python -m multiproc train.py --output_directory=outdir_full_tacotron_ed --log_directory=logdir --hparams=distributed_run=True,fp16_run=True,training_files=filelists/david/labelled/train.txt,validation_files=filelists/david/labelled/val.txt,encoder_conditioning=True,make_new_encoder=True -c outdir_unsupervised/checkpoint_10000 --warm_start

Multi-GPU Unsupervised learning

1. python -m multiproc train.py --output_directory=outdir_unsupervised --log_directory=logdir --hparams=distributed_run=True,fp16_run=True,unsupervised=True,training_files=filelists/david/unlabelled/train_list.txt,validation_files=filelists/david/unlabelled/val_list.txt

Multi-GPU Supervised encoder validation

1. python -m multiproc validate.py --output_directory=outdir_val --log_directory=logdir -c outdir_full_tacotron_ed/checkpoint_4500 --hparams=distributed_run=True,fp16_run=True,validation_files=filelists/david/labelled/val.txt,encoder_conditioning=True

Multi-GPU Unsupervised validation

1. python -m multiproc validate.py --output_directory=outdir_val --log_directory=logdir -c outdir_unsupervised/checkpoint_10200 --hparams=distributed_run=True,fp16_run=True,unsupervised=True,training_files=filelists/david/unlabelled/train_list.txt,validation_files=filelists/david/unlabelled/val_list.txt

Inference

1. out = inference.do_full_inference("outdir_full_tacotron_ed/checkpoint_3000", "hello my name is david", True)
2. audio, mel_outputs, mel_outputs_postnet, alignments, model, glove, glow = inference.do_full_audio("hello my name is david",1)
3. python -m multiproc inference.py -c outdir_full_tacotron_ed/checkpoint_3000 -t "hello my name is david" -e

Main Files for Training

filelists/david/labelled/val.txt filelists/david/labelled/train.txt

Inference demo

1. Download our published Tacotron 2 model
2. Download our published WaveGlow model
3. jupyter notebook --ip=127.0.0.1 --port=31337
4. Load inference.ipynb

N.b. When performing Mel-Spectrogram to Audio synthesis, make sure Tacotron 2 and the Mel decoder were trained on the same mel-spectrogram representation.

Related repos

WaveGlow Faster than real time Flow-based Generative Network for Speech Synthesis

nv-wavenet Faster than real time WaveNet.

Acknowledgements

This implementation uses code from the following repos: Keith Ito, Prem Seetharaman as described in our code.

We are inspired by Ryuchi Yamamoto's Tacotron PyTorch implementation.

We are thankful to the Tacotron 2 paper authors, specially Jonathan Shen, Yuxuan Wang and Zongheng Yang.