# TTS (Work in Progress...)

Here we have pytorch implementation of: 
- Tacotron: [A Fully End-to-End Text-To-Speech Synthesis Model](https://arxiv.org/abs/1703.10135).
- Tacotron2 (TODO): [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/pdf/1712.05884.pdf)

At the end, it should be easy to add new models and try different architectures.

You can find [here](https://www.evernote.com/shard/s146/sh/9544e7e9-d372-4610-a7b7-3ddcb63d5dac/d01d33837dab625229dec3cfb4cfb887) a brief note about possible TTS architectures and their comparisons. 

## Requirements
Highly recommended to use [miniconda](https://conda.io/miniconda.html) for easier installation.
  * python 3.6
  * pytorch > 0.2.0
  * TODO

## Data
Currently TTS provides data loaders for
- [LJ Speech](https://keithito.com/LJ-Speech-Dataset/)

## Training the network
To run your own training, you need to define a ```config.json``` file (simple template below) and call with the command.

```train.py --config_path config.json```

If you like to use specific set of GPUs.

```CUDA_VISIBLE_DEVICES="0,1,4" train.py --config_path config.json```

Each run creates an experiment folder with the corresponfing date and time, under the folder you set in ```config.json```. And if there is no checkpoint yet under that folder, it is going to be removed when you press Ctrl+C.

Example ```config.json```:
```
{
  // Data loading parameters
  "num_mels": 80,
  "num_freq": 1024,
  "sample_rate": 20000,
  "frame_length_ms": 50.0,
  "frame_shift_ms": 12.5,
  "preemphasis": 0.97,
  "min_level_db": -100,
  "ref_level_db": 20,
  "hidden_size": 128,
  "embedding_size": 256,
  "text_cleaner": "english_cleaners",

  // Training parameters
  "epochs": 2000,
  "lr": 0.001,
  "batch_size": 256,
  "griffinf_lim_iters": 60,
  "power": 1.5,
  "r": 5,            // number of decoder outputs for Tacotron

  // Number of data loader processes
  "num_loader_workers": 8,

  // Experiment logging parameters
  "checkpoint": true,  // if save checkpoint per save_step
  "save_step": 200,
  "data_path": "/path/to/KeithIto/LJSpeech-1.0",
  "output_path": "/path/to/my_experiment",
  "log_dir": "/path/to/my/tensorboard/logs/"
}
```