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Tutorial for nervous beginners

First install Coqui TTS.

Synthesizing Speech

You can run tts and synthesize speech directly on the terminal.

$ tts -h # see the help
$ tts --list_models  # list the available models.

You can call tts-server to start a local demo server that you can open on your favorite web browser and 🗣️ (make sure to install the additional dependencies with pip install coqui-tts[server]).

$ tts-server -h # see the help
$ tts-server --list_models  # list the available models.

See this page for more details on synthesizing speech with the CLI, server or Python API.

Training a `tts` Model

A breakdown of a simple script that trains a GlowTTS model on the LJspeech dataset. For a more in-depth guide to training and fine-tuning also see this page.

Pure Python Way

Download your dataset.

In this example, we download and use the LJSpeech dataset. Set the download directory based on your preferences.
```
$ python -c 'from TTS.utils.downloaders import download_ljspeech; download_ljspeech("../recipes/ljspeech/");'
```
Define train.py.

Run the script.

CUDA_VISIBLE_DEVICES=0 python train.py

Continue a previous run.

CUDA_VISIBLE_DEVICES=0 python train.py --continue_path path/to/previous/run/folder/

Fine-tune a model.

CUDA_VISIBLE_DEVICES=0 python train.py --restore_path path/to/model/checkpoint.pth

Run multi-gpu training.

CUDA_VISIBLE_DEVICES=0,1,2 python -m trainer.distribute --script train.py

CLI Way

We still support running training from CLI like in the old days. The same training run can also be started as follows.

Define your config.json

{
    "run_name": "my_run",
    "model": "glow_tts",
    "batch_size": 32,
    "eval_batch_size": 16,
    "num_loader_workers": 4,
    "num_eval_loader_workers": 4,
    "run_eval": true,
    "test_delay_epochs": -1,
    "epochs": 1000,
    "text_cleaner": "english_cleaners",
    "use_phonemes": false,
    "phoneme_language": "en-us",
    "phoneme_cache_path": "phoneme_cache",
    "print_step": 25,
    "print_eval": true,
    "mixed_precision": false,
    "output_path": "recipes/ljspeech/glow_tts/",
    "datasets":[{"formatter": "ljspeech", "meta_file_train":"metadata.csv", "path": "recipes/ljspeech/LJSpeech-1.1/"}]
}

Start training.

$ CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py --config_path config.json

Training a `vocoder` Model

❗️ Note that you can also use train_vocoder.py as the tts models above.

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