tacotron-2-mandarin-griffin-lim

Tensorflow implementation of DeepMind's Tacotron-2. A deep neural network architecture described in this paper: Natural TTS synthesis by conditioning Wavenet on MEL spectogram predictions

Repo Structure

tacotron-2-mandarin-griffin-lim
|--- datasets
|--- logs-Tacotron
     |--- eval-dir
     |--- plots
     |--- taco_pretrained
     |--- wavs
|--- papers
|--- prepare
|--- tacotron
     |--- models
     |--- utils
|--- tacotron_output
     |--- eval
     |--- logs-eval
          |--- plots
          |--- wavs
|--- training_data
     |--- audio
     |--- linear
     |--- mels

Samples

There are some synthesis samples here.

Pretrained

you can get pretrained model here.

Quick Start

OS: Ubuntu 16.04

Step (0) - Git clone repository

git clone https://github.com/Joee1995/tacotron-2-mandarin-griffin-lim.git
cd tacotron-2-mandarin-griffin-lim/

Step (1) - Install dependencies

1. Install Python 3 (python-3.5.5 for me)

2. Install TensorFlow (tensorflow-1.10.0 for me)

3. Install other dependencies

   pip install -r requirements.txt
   

Step (2) - Prepare dataset

1. Download dataset BIAOBEI or THCHS-30

   After that, your doc tree should be:

   tacotron-2-mandarin-griffin-lim
   |--- ...
   |--- BZNSYP
        |--- ProsodyLabeling
             |--- 000001-010000.txt
        |--- Wave
   |--- ...
   

2. Prepare dataset (default is BIAOBEI)

   python prepare_dataset.py
   

   If preparing THCHS-30, you can use parameter --dataset=THCHS-30.

   After that, you can get a folder BIAOBEI as follow:

   tacotron-2-mandarin-griffin-lim
   |--- ...
   |--- BIAOBEI
        |--- biaobei_48000
   |--- ...
   

3. Preprocess dataset (default is BIAOBEI)

   python preprocess.py
   

   If prrprocessing THCHS-30, you can use parameter --dataset=THCHS-30.

   After that, you can get a folder training_data as follow:

   tacotron-2-mandarin-griffin-lim
   |--- ...
   |--- training_data
        |--- audio
        |--- linear
        |--- mels
        |--- train.txt
   |--- ...
   

Step (3) - Train tacotron model

python train.py

More parameters, please see train.py.

After that, you can get a folder logs-Tacotron as follow:

tacotron-2-mandarin-griffin-lim
|--- ...
|--- logs-Tacotron
     |--- eval-dir
     |--- plots
     |--- taco_pretrained
     |--- wavs
|--- ...

Step (4) - Synthesize audio

python synthesize.py

More parameters, please see synthesize.py.

After that, you can get a folder tacotron_output as follow:

tacotron-2-mandarin-griffin-lim
|--- ...
|--- tacotron_output
     |--- eval
     |--- logs-eval
          |--- plots
          |--- wavs
|--- ...

References & Resources

Rayhane-mamah/Tacotron-2