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video-dubbing / TTS /docs /source /tutorial_for_nervous_beginners.md
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Tutorial For Nervous Beginners

Installation

User friendly installation. Recommended only for synthesizing voice.

$ pip install TTS

Developer friendly installation.

$ git clone https://github.com/coqui-ai/TTS
$ cd TTS
$ pip install -e .

Training a tts Model

A breakdown of a simple script that trains a GlowTTS model on the LJspeech dataset. See the comments for more details.

Pure Python Way

  1. 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/");'

  2. Define train.py.

  3. 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.

  1. 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/"}]
}

  2. 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.

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.

cli.gif

You can call tts-server to start a local demo server that you can open it on your favorite web browser and 🗣️.

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

server.gif