<commit_before># Tacotron

An implementation of Tacotron speech synthesis in TensorFlow.


### Audio Samples

  * **[Audio Samples](https://keithito.github.io/audio-samples/)** from models trained using this repo.
    * The first set was trained for 441K steps on the [LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/)
      * Speech started to become intelligble around 20K steps.
    * The second set was trained by [@MXGray](https://github.com/MXGray) for 140K steps on the [Nancy Corpus](http://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/).


### Recent Updates

1. @npuichigo [fixed](https://github.com/keithito/tacotron/pull/205) a bug where dropout was not being applied in the prenet.

2. @begeekmyfriend created a [fork](https://github.com/begeekmyfriend/tacotron) that adds location-sensitive attention and the stop token from the [Tacotron 2](https://arxiv.org/abs/1712.05884) paper. This can greatly reduce the amount of data required to train a model.


## Background

In April 2017, Google published a paper, [Tacotron: Towards End-to-End Speech Synthesis](https://arxiv.org/pdf/1703.10135.pdf),
where they present a neural text-to-speech model that learns to synthesize speech directly from
(text, audio) pairs. However, they didn't release their source code or training data. This is an
independent attempt to provide an open-source implementation of the model described in their paper.

The quality isn't as good as Google's demo yet, but hopefully it will get there someday :-).
Pull requests are welcome!



## Quick Start<commit_msg>Syntax issue on string #10<commit_after># Tacotron

An implementation of Tacotron speech synthesis in TensorFlow.


### Audio Samples

  * **[Audio Samples](https://keithito.github.io/audio-samples/)** from models trained using this repo.
    * The first set was trained for 441K steps on the [LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/)
      * Speech started to become intelligible around 20K steps.
    * The second set was trained by [@MXGray](https://github.com/MXGray) for 140K steps on the [Nancy Corpus](http://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/).


### Recent Updates

1. @npuichigo [fixed](https://github.com/keithito/tacotron/pull/205) a bug where dropout was not being applied in the prenet.

2. @begeekmyfriend created a [fork](https://github.com/begeekmyfriend/tacotron) that adds location-sensitive attention and the stop token from the [Tacotron 2](https://arxiv.org/abs/1712.05884) paper. This can greatly reduce the amount of data required to train a model.


## Background

In April 2017, Google published a paper, [Tacotron: Towards End-to-End Speech Synthesis](https://arxiv.org/pdf/1703.10135.pdf),
where they present a neural text-to-speech model that learns to synthesize speech directly from
(text, audio) pairs. However, they didn't release their source code or training data. This is an
independent attempt to provide an open-source implementation of the model described in their paper.

The quality isn't as good as Google's demo yet, but hopefully it will get there someday :-).
Pull requests are welcome!



## Quick Start
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{'dir': 'git-commits-cleaned', 'id': '845790', 'n_tokens_mistral': 1043, 'n_tokens_neox': 901, 'n_words': 365}