Medix-AI/coqui-tts
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coqui-tts
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Eren Golge 2019-03-07 02:01:41 +01:00
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README.md
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This project is a part of [Mozilla Common Voice](https://voice.mozilla.org/en). TTS aims a deep learning based Text2Speech engine, low in cost and high in quality. To begin with, you can hear a sample generated voice from [here](https://soundcloud.com/user-565970875/commonvoice-loc-sens-attn).
The model architecture is highly inspired by Tacotron: [A Fully End-to-End Text-To-Speech Synthesis Model](https://arxiv.org/abs/1703.10135). However, it has many important updates that make training faster and computationally very efficient. Feel free to experiment with new ideas and propose changes.
TTS includes two different model implementations which are based on [Tacotron](https://arxiv.org/abs/1703.10135) and [Tacotron2](https://arxiv.org/abs/1712.05884). Tacotron is smaller, efficient and easier to train but Tacotron2 provides better results, especially when it is combined with a Neural vocoder. Therefore, choose depending on your project requirements.
You can find [here](http://www.erogol.com/text-speech-deep-learning-architectures/) a brief note about TTS architectures and their comparisons.
If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-learning-architectures/) a brief post about TTS architectures and their comparisons.
## Requirements and Installation
Highly recommended to use [miniconda](https://conda.io/miniconda.html) for easier installation.
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tail -n 1100 metadata_shuf.csv > metadata_val.csv
```
To train a new model, you need to define your own ```config.json``` file (check the example) and call with the command below.
To train a new model, you need to define your own ```config.json``` file (check the example) and call with the command below. You also set the model architecture in ```config.json```.
```train.py --config_path config.json```

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config.json
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"url": "tcp:\/\/localhost:54321"
},
"model": "Tacotron", // one of the model in models/
"model": "Tacotron", // one of the model in models/. For now "Tacotron" or "Tacotron2" are available models.
"grad_clip": 0.02, // upper limit for gradients for clipping.
"epochs": 1000, // total number of epochs to train.
"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.