fireae / tacotron

A TensorFlow Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model

[ggsonic:] i use instance normalization instead of batch_norms. I can get clear voices after 100-200 ecpoches. the demo voice you can hear in 10.mp3 file. and the graph is illustrated in graph.png.

A (Heavily Documented) TensorFlow Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model

Major History

• June 2, 2017. Added train_multiple_gpus.py for multiple gpus.
• June 1, 2017. Second draft. I corrected some mistakes with the help of several contributors (THANKS!), and re-factored source codes so that they are more readable and modular. So far, I couldn't get any promising results.
• May 17, 2017. First draft. You can run it following the steps below, but probably you should get poor results. I'll be working on debugging this weekend. (Code reviews and/or contributions are more than welcome!)

Requirements

• NumPy >= 1.11.1
• TensorFlow >= 1.1
• librosa
• scipy

Data

Since the original paper was based on their internal data, I use a freely available one, instead.

The World English Bible is a public domain update of the American Standard Version of 1901 into modern English. Its text and audio recordings are freely available here. Unfortunately, however, each of the audio files matches a chapter, not a verse, so is too long for many machine learning tasks. I had someone slice them by verse manually. You can download the audio data and its text from my dropbox.

Content

• hyperparams.py: includes all hyper parameters that are needed.
• prepro.py: loads vocabulary, training/evaluation data.
• data_load.py: loads data and put them in queues so multiple mini-bach data are generated in parallel.
• utils.py: has several custom operational functions.
• modules.py: contains building blocks for encoding/decoding networks.
• networks.py: has three core networks, that is, encoding, decoding, and postprocessing network.
• train.py: is in charge of training.
• eval.py: is in charge of sample synthesis.

Training

• STEP 1. Adjust hyper parameters in hyperparams.py if necessary.
• STEP 2. Download and extract the audio data and its text.
• STEP 3. Run train.py. or train_multiple_gpus.py if you have more than one gpu.

Sample Synthesis

• Run eval.py to get samples.

Acknowledgements

I would like to show my respect to Dave, the host of www.audiotreasure.com and the reader of the audio files.