Tacotron

An implementation of Tacotron speech synthesis in TensorFlow.

Audio Samples

• Audio Samples from models trained using this repo.
  • The first set was trained for 877K steps on the LJ Speech Dataset
    • Speech started to become intelligble around 20K steps.
    • Although loss continued to decrease, there wasn't much noticable improvement after ~250K steps.
  • The second set was trained by @MXGray for 140K steps on the Nancy Corpus.

Background

Earlier this year, Google published a paper, Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model, where they present a neural text-to-speech model that learns to synthesize speech directly from (text, audio) pairs. However, they didn't release their source code or training data. This is an attempt to provide an open-source implementation of the model described in their paper.

The quality isn't as good as Google's demo yet, but hopefully it will get there someday :-). Pull requests are welcome!

Quick Start

Installing dependencies

1. Install Python 3.

2. Install TensorFlow 1.3. Install with GPU support if it's available for your platform.

3. Install requirements:

   pip install -r requirements.txt
   

Using a pre-trained model

1. Download and unpack a model:

   curl http://data.keithito.com/data/speech/tacotron-20170720.tar.bz2 | tar xjC /tmp
   

2. Run the demo server:

   python3 demo_server.py --checkpoint /tmp/tacotron-20170720/model.ckpt
   

3. Point your browser at localhost:9000

   • Type what you want to synthesize

Training

Note: you need at least 40GB of free disk space to train a model.

1. Download a speech dataset.

   The following are supported out of the box:

   • LJ Speech (Public Domain)
   • Blizzard 2012 (Creative Commons Attribution Share-Alike)

   You can use other datasets if you convert them to the right format. See TRAINING_DATA.md for more info.

2. Unpack the dataset into ~/tacotron

   After unpacking, your tree should look like this for LJ Speech:

   tacotron
     |- LJSpeech-1.0
         |- metadata.csv
         |- wavs
   

   or like this for Blizzard 2012:

   tacotron
     |- Blizzard2012
         |- ATrampAbroad
         |   |- sentence_index.txt
         |   |- lab
         |   |- wav
         |- TheManThatCorruptedHadleyburg
             |- sentence_index.txt
             |- lab
             |- wav
   

3. Preprocess the data

   python3 preprocess.py --dataset ljspeech
   

   • Use --dataset blizzard for Blizzard data

4. Train a model

   python3 train.py
   

   Tunable hyperparameters are found in hparams.py. You can adjust these at the command line using the --hparams flag, for example --hparams="batch_size=16,outputs_per_step=2". Hyperparameters should generally be set to the same values at both training and eval time.

5. Monitor with Tensorboard (optional)

   tensorboard --logdir ~/tacotron/logs-tacotron
   

   The trainer dumps audio and alignments every 1000 steps. You can find these in ~/tacotron/logs-tacotron.

6. Synthesize from a checkpoint

   python3 demo_server.py --checkpoint ~/tacotron/logs-tacotron/model.ckpt-185000
   

   Replace "185000" with the checkpoint number that you want to use, then open a browser to localhost:9000 and type what you want to speak. Alternately, you can run eval.py at the command line:

   python3 eval.py --checkpoint ~/tacotron/logs-tacotron/model.ckpt-185000
   

   If you set the --hparams flag when training, set the same value here.

Miscellaneous Notes

• TCMalloc seems to improve training speed and avoids occasional slowdowns seen with the default allocator. You can enable it by installing it and setting LD_PRELOAD=/usr/lib/libtcmalloc.so.

• You can train with CMUDict by downloading the dictionary to ~/tacotron/training and then passing the flag --hparams="use_cmudict=True" to train.py. This will allow you to pass ARPAbet phonemes enclosed in curly braces at eval time to force a particular pronunciation, e.g. Turn left on {HH AW1 S S T AH0 N} Street.

• If you pass a Slack incoming webhook URL as the --slack_url flag to train.py, it will send you progress updates every 1000 steps.

• Occasionally, you may see a spike in loss and the model will forget how to attend (the alignments will no longer make sense). Although it will recover eventually, it may save time to restart at a checkpoint prior to the spike by passing the --restore_step=150000 flag to train.py (replacing 150000 with a step number prior to the spike). Update: a recent fix to gradient clipping by @candlewill may have fixed this.

Other Implementations

• By Alex Barron: https://github.com/barronalex/Tacotron
• By Kyubyong Park: https://github.com/Kyubyong/tacotron