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 synthesized voice from [here](https://soundcloud.com/user-565970875/commonvoice-loc-sens-attn).
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.
Install TTS using ```setup.py```. It will install all of the requirements automatically and make TTS available to all the python environment as an ordinary python module.
A barebone `Dockerfile` exists at the root of the project, which should let you quickly setup the environment. By default, it will start the server and let you query it. Make sure to use `nvidia-docker` to use your GPUs. Make sure you follow the instructions in the [`server README`](server/README.md) before you build your image so that the server can find the model within the image.
```
docker build -t mozilla-tts .
nvidia-docker run -it --rm -p 5002:5002 mozilla-tts
> "Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase the grey matter in the parts of the brain responsible for emotional regulation and learning."
The most time-consuming part is the vocoder algorithm (Griffin-Lim) which runs on CPU. By setting its number of iterations lower, you might have faster execution with a small loss of quality. Some of the experimental values are below.
TTS provides a generic dataloader easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.
Here you can find a [CoLab](https://gist.github.com/erogol/97516ad65b44dbddb8cd694953187c5b) notebook for a hands-on example, training LJSpeech. Or you can manually follow the guideline below.
To start with, split ```metadata.csv``` into train and validation subsets respectively ```metadata_train.csv``` and ```metadata_val.csv```. Note that for text-to-speech, validation performance might be misleading since the loss value does not directly measure the voice quality to the human ear and it also does not measure the attention module performance. Therefore, running the model with new sentences and listening to the results is the best way to go.
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```.
This repository is governed by Mozilla's code of conduct and etiquette guidelines. For more details, please read the [Mozilla Community Participation Guidelines.](https://www.mozilla.org/about/governance/policies/participation/)
Please send your Pull Request to ```dev``` branch. Before making a Pull Request, check your changes for basic mistakes and style problems by using a linter. We have cardboardlinter setup in this repository, so for example, if you've made some changes and would like to run the linter on just the changed code, you can use the follow command:
If you like to use TTS to try a new idea and like to share your experiments with the community, we urge you to use the following guideline for a better collaboration.
- Share your results as you proceed. (Tensorboard log files, audio results, visuals etc.)
- Use LJSpeech dataset (for English) if you like to compare results with the released models. (It is the most open scalable dataset for quick experimentation)
- [x] Adapting Neural Vocoder. TTS works with WaveRNN and ParallelWaveGAN (https://github.com/erogol/WaveRNN and https://github.com/erogol/ParallelWaveGAN)
