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.

    import os

# Trainer: Where the ✨️ happens.
# TrainingArgs: Defines the set of arguments of the Trainer.
from trainer import Trainer, TrainerArgs

# GlowTTSConfig: all model related values for training, validating and testing.
from TTS.tts.configs.glow_tts_config import GlowTTSConfig

# BaseDatasetConfig: defines name, formatter and path of the dataset.
from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.glow_tts import GlowTTS
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor

# we use the same path as this script as our training folder.
output_path = os.path.dirname(os.path.abspath(__file__))

# DEFINE DATASET CONFIG
# Set LJSpeech as our target dataset and define its path.
# You can also use a simple Dict to define the dataset and pass it to your custom formatter.
dataset_config = BaseDatasetConfig(
    formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
)

# INITIALIZE THE TRAINING CONFIGURATION
# Configure the model. Every config class inherits the BaseTTSConfig.
config = GlowTTSConfig(
    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="phoneme_cleaners",
    use_phonemes=True,
    phoneme_language="en-us",
    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
    print_step=25,
    print_eval=False,
    mixed_precision=True,
    output_path=output_path,
    datasets=[dataset_config],
)

# INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)

# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)

# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(
    dataset_config,
    eval_split=True,
    eval_split_max_size=config.eval_split_max_size,
    eval_split_size=config.eval_split_size,
)

# INITIALIZE THE MODEL
# Models take a config object and a speaker manager as input
# Config defines the details of the model like the number of layers, the size of the embedding, etc.
# Speaker manager is used by multi-speaker models.
model = GlowTTS(config, ap, tokenizer, speaker_manager=None)

# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
    TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)

# AND... 3,2,1... 🚀
trainer.fit()

  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#

import os

from trainer import Trainer, TrainerArgs

from TTS.utils.audio import AudioProcessor
from TTS.vocoder.configs import HifiganConfig
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.gan import GAN

output_path = os.path.dirname(os.path.abspath(__file__))

config = HifiganConfig(
    batch_size=32,
    eval_batch_size=16,
    num_loader_workers=4,
    num_eval_loader_workers=4,
    run_eval=True,
    test_delay_epochs=5,
    epochs=1000,
    seq_len=8192,
    pad_short=2000,
    use_noise_augment=True,
    eval_split_size=10,
    print_step=25,
    print_eval=False,
    mixed_precision=False,
    lr_gen=1e-4,
    lr_disc=1e-4,
    data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
    output_path=output_path,
)

# init audio processor
ap = AudioProcessor(**config.audio.to_dict())

# load training samples
eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)

# init model
model = GAN(config, ap)

# init the trainer and 🚀
trainer = Trainer(
    TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
trainer.fit()

❗️ 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