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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 BaseAudioConfig, BaseDatasetConfig, CharactersConfig | |
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 = "/storage/output-glowtts/" | |
# 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="bel_tts_formatter", | |
meta_file_train="ipa_final_dataset.csv", | |
path=os.path.join(output_path, "/storage/filtered_dataset/"), | |
) | |
characters = CharactersConfig( | |
characters_class="TTS.tts.utils.text.characters.Graphemes", | |
pad="_", | |
eos="~", | |
bos="^", | |
blank="@", | |
characters="Iabdfgijklmnprstuvxzɔɛɣɨɫɱʂʐʲˈː̯͡β", | |
punctuations="!,.?: -‒–—…", | |
) | |
audio_config = BaseAudioConfig( | |
mel_fmin=50, | |
mel_fmax=8000, | |
hop_length=256, | |
stats_path="/storage/TTS/scale_stats.npy", | |
) | |
# INITIALIZE THE TRAINING CONFIGURATION | |
# Configure the model. Every config class inherits the BaseTTSConfig. | |
config = GlowTTSConfig( | |
batch_size=96, | |
eval_batch_size=32, | |
num_loader_workers=8, | |
num_eval_loader_workers=8, | |
use_noise_augment=True, | |
run_eval=True, | |
test_delay_epochs=-1, | |
epochs=1000, | |
print_step=50, | |
print_eval=True, | |
output_path=output_path, | |
add_blank=True, | |
datasets=[dataset_config], | |
# characters=characters, | |
enable_eos_bos_chars=True, | |
mixed_precision=False, | |
save_step=10000, | |
save_n_checkpoints=2, | |
save_best_after=5000, | |
text_cleaner="no_cleaners", | |
audio=audio_config, | |
test_sentences=[], | |
use_phonemes=True, | |
phoneme_language="be", | |
) | |
if __name__ == "__main__": | |
# 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() | |