mimic2

This is a fork of keithito/tacotron with changes specific to Mimic 2 applied.

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

Install Python 3.
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.
Install requirements:
```
pip install -r requirements.txt
```

Using a pre-trained model

Download and unpack a model:

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

Run the demo server:

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

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.

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.

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

Preprocess the data
```
python3 preprocess.py --dataset ljspeech
```
- Use --dataset blizzard for Blizzard data
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.
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.
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.