<commit_before># tacotron (Working)
A TensorFlow Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model

https://www.dropbox.com/sh/13cw9l912w3y1lw/AAD4cxviFVgD-1pgUfB8I26Ca?dl=0<commit_msg>First draft<commit_after># A (Heavily Documented) TensorFlow Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model

## **Warning**

As of May 17, 2017, this is still a 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 >= 1.0
  * librosa

## Data
Since the [original paper](https://arxiv.org/abs/1703.10135) was based on their internal data, I use a freely available one, instead.

[The World English Bible](https://en.wikipedia.org/wiki/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 avaiable [here](http://www.audiotreasure.com/webindex.htm). 
Unfortunately, however, each of the audio files matches a chapter, not a verse, so is too long in most cases. I sliced them by verse. 
You can get them on [my dropbox](https://dl.dropboxusercontent.com/u/42868014/WEB.zip)


## Work Flow
  * STEP 1. Adjust hyper parameters in `hyperparams.py` if necessary.
  * STEP 2. Download [the data](https://dl.dropboxusercontent.com/u/42868014/WEB.zip) and extract it. 
  * STEP 3. Run `train.py`.
  * STEP 4. Run `eval.py` to get samples.
{'fraction_non_alphanumeric': 0.10261312938177183, 'fraction_numerical': 0.03760356915232632, 'mean_word_length': 4.304054054054054, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 5, 'https://': 5, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3761959', 'n_tokens_mistral': 557, 'n_tokens_neox': 495, 'n_words': 185}