Tacotron-2-keras (Without Wavenet vocoder)

Keras implementations of Deep mind's Tacotron-2. A deep neural network architecture described in this paper: Natural TTS synthesis by conditioning Wavenet on MEL spectogram predictions

Current state:

• write a Keras inpimentation of Tacotron-2 (in progress)
• achieve a high quality human-like text to speech synthesizer based on DeepMind's paper
• achieve a high speed of training and work ofr multi-GPU systems.
• provide a pre-trained Tacotron-2 model
• provide compatibility with Mozilla LPCNet project (Optional)

Note:

Our preprocessing only supports Ljspeech and Ljspeech-like datasets (M-AILABS speech data)! If running on datasets stored differently, you will probably need to make your own preprocessing script.

Model Architecture:

The model described by the authors can be divided in two parts:

• Spectrogram prediction network
• Vocoder (e.g. Wavenet vocoder)

To have an in-depth exploration of the model architecture, training procedure and preprocessing logic, refer to our wiki

Ussage:

0. Clone a repository

$ git clone https://github.com/Stevel705/Tacotron-2-keras.git

1. Download LJ-like dataset (e.g. english Speech Dataset)
2. Extract dataset to Tacotron-2-keras\data folder
3. Run $ python3 1_create_audio_dataset.py to process an audio
4. Run $ python3 2_create_text_dataset.py to create a text data
5. Train tacotron $ python3 3_train.py
6. Test pretrained model $ python3 4_test.py (optional)
7. Synthesize mels and speech $ python3 5_syntezer.py (in progress)

Lisense:

MIT Lisense