/gst-tacotron

Implementation of the Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

Primary LanguagePythonMIT LicenseMIT

GST Tacotron

An tensorflow implementation of the Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis.

gst-tacotron

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 or 1.4.

  3. Install requirements:

    pip install -r requirements.txt

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:

    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.

Reference

Other Implementations