July 30, 2019

325 words 2 mins read



Tacotron 2 - PyTorch implementation with faster-than-realtime inference

repo name NVIDIA/tacotron2
repo link https://github.com/NVIDIA/tacotron2
language Jupyter Notebook
size (curr.) 2749 kB
stars (curr.) 1465
created 2018-05-03
license BSD 3-Clause “New” or “Revised” License

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.

Alignment, Predicted Mel Spectrogram, Target Mel Spectrogram




  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


  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= --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.

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

nv-wavenet Faster than real time WaveNet.


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.

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