文件列表:

app.py (4432, 2020-03-05)
constants (0, 2020-03-05)
constants\hparams.py (706, 2020-03-05)
model (0, 2020-03-05)
model\__init__.py (0, 2020-03-05)
model\feeder.py (6223, 2020-03-05)
model\helpers.py (2771, 2020-03-05)
model\modules.py (4885, 2020-03-05)
model\networks.py (6150, 2020-03-05)
model\tacotron.py (3930, 2020-03-05)
preprocess.py (4219, 2020-03-05)
requirements.txt (94, 2020-03-05)
signal_proc (0, 2020-03-05)
signal_proc\__init__.py (0, 2020-03-05)
signal_proc\audio.py (3894, 2020-03-05)
signal_proc\synthesizer.py (1768, 2020-03-05)
test.py (2164, 2020-03-05)
text (0, 2020-03-05)
text\__init__.py (0, 2020-03-05)
text\character_set.py (910, 2020-03-05)
text\mm_num2word.py (3509, 2020-03-05)
text\tokenizer.py (2135, 2020-03-05)
train.py (5619, 2020-03-05)
utils (0, 2020-03-05)
utils\__init__.py (444, 2020-03-05)
utils\logger.py (661, 2020-03-05)
utils\plotter.py (473, 2020-03-05)

# Myanmar End-to-End Text-to-Speech This is the development of a Myanmar Text-to-Speech system with the famous End-to-End Speech Synthesis Model, Tacotron. It is a part of a thesis for B.E. Degree that I've been assigned at Yangon Technological University. My supervisor was **Dr. Yuzana Win** and she guided me throughout this development. ## License This work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 4.0 International (CC BY-NC-SA 4.0) License. View detailed info of the [license](http://creativecommons.org/licenses/by-nc-sa/4.0/). myanmar-tts educational purpose commercial use case ## Corpus **[Base Technology, Expa.Ai (Myanmar)](https://expa.ai)** kindly provided Myanmar text corpus and their amazing tool for creating speech corpus. Speech corpus (mmSpeech as I call it) is created solely on my own with a recorder tool (as previously mentioned) and it currently contains over 5,000 recorded `` pairs. I intend to upload the created corpus on some channel in future. ## Instructions ### Installing dependencies 1. Install Python 3 2. Install [TensorFlow](https://www.tensorflow.org/install/) 3. Install a number of modules ``` pip install -r requirements.txt ``` ### Preparing Text and Audio Dataset 1. First of all, the corpus should reside in `~/mm-tts`, although it is not a **must** and can easily be changed by a command line argument. ``` mm-tts | mmSpeech | metadata.csv | wavs ``` 2. **Preprocess the data** ``` python3 preprocess.py ``` After it is done, you should see the outputs in `~/mm-tts/training/` ### Training ``` python3 train.py ``` If you want to restore the step from a checkpoint ``` python3 train.py --restore_step Number ``` ### Evaluation There are some sentences defined in test.py, you may test them out with the trained model to see how good the current model is. ``` python3 test.py --checkpoint /path/to/checkpoint ``` ### Testing with Custom Inputs There is a simple app implemented to try out the trained models for their performance. ``` python3 app.py --checkpoint /path/to/checkpoint ``` This will create a simple web app listening at port 4000 unless you specify. Open up your browser and go to `http://localhost:4000`, you should see a simple interface with a text input to get the text from the user. ### Pretrained Model * [mmSpeech 150K Steps](https://drive.google.com/open?id=1P3JQYjGNoPbNykOg4-45LPUsZGuWkAd7) ### Generated Audio Samples Generated Samples are available on SoundCloud * [mmSpeech 150K Steps](https://soundcloud.com/htoo-pyae-466960846/sets/mmspeech-outputs) ### Notes * Google Colab which gives excellent GPU access was used for training this model. * On average, each step tooks about 1.6 seconds and at peak, each step took about 1.2 and sometimes 1.1 seconds. * For my thesis, I have trained this model for 150,000 steps (took me about a week). ### Loss Curve Below is the produced loss curves from training mmSpeech for 150,000 Steps.  ### Alignment Plot  ### References * [Tacotron: Towards End-to-End Speech Synthesis](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwiBjuL828vkAhWh6nMBHYccCdYQFjABegQIABAB&url=https%3A%2F%2Farxiv.org%2Fabs%2F1703.10135&usg=AOvVaw0_KT-Hbe9h_egPMynMsJOM) * [keithito/tacotron](https://github.com/keithito/tacotron)

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