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Tacotron 2 MMI (without wavenet)

code for Maximizing Mutual Information for Tacotron.

An updated version of the method purposed in the avoue paper.

  • gradient adaptor factor for the CTC loss instead of an increasing weight.
  • an extremely simple CTC recognizer (1 linear layer with ReLU activation) is used to force the Tacotron decoder to learn a representation with plentiful linguistic information. The CTC recognizer is able to classify raw acoustic features if it employs a powerful structure.

New options in hparams:

  • use_mmi (use mmi training objective or not)
  • use_gaf (use gradient adaptive factor or not, to keep the max norm of gradients from the taco_loss and mi_loss approximately equal)
  • max_gaf (maximum value of gradient adaptive factor)
  • drop_frame_rate (drop teacher-frocing input frames to a certain rate)

This code can pick up alignment at much earlier steps than the original version.

Nvidia-Tacotron2 alignment refinement

NVIDIA-Tacotron2

This code

This code

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.

Alignment, Predicted Mel Spectrogram, Target Mel Spectrogram

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

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.