Tacotron

An implementation of VAE Tacotron speech synthesis in TensorFlow. (https://arxiv.org/abs/1812.04342)

Quick Start

Installing dependencies

1. Install Python 3.

2. Install the latest version of TensorFlow for your platform. For better performance, install with GPU support if it's available. This code works with TensorFlow 1.3 and later.

3. Install requirements:

   pip install -r requirements.txt
   

4. Run the demo server:

   python3 demo_server.py --checkpoint /tmp/tacotron-20180906/model.ckpt
   

5. Point your browser at localhost:9000

   • Type what you want to synthesize

Training

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-1.1
         |- 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. The default hyperparameters are recommended for LJ Speech and other English-language data. See TRAINING_DATA.md for other languages.

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 --reference_audio='test.wav'
   
   

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