from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
# set experiment paths
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_path = os.path.join(output_path, "../VCTK/")
# download the dataset if not downloaded
if not os.path.exists(dataset_path):
from TTS.utils.downloaders import download_vctk
download_vctk(dataset_path)
# define dataset config
dataset_config = BaseDatasetConfig(name="vctk",
meta_file_train="",
path=dataset_path)
# define audio config
# ❗ resample the dataset externally using `TTS/bin/resample.py` and set `resample=False` for faster training
audio_config = BaseAudioConfig(sample_rate=22050,
resample=True,
do_trim_silence=True,
trim_db=23.0)
# define model config
config = GlowTTSConfig(
batch_size=64,
eval_batch_size=16,
num_loader_workers=4,
num_eval_loader_workers=4,
import os
from TTS.config.shared_configs import BaseAudioConfig
from TTS.trainer import Trainer, TrainingArgs
from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.configs.tacotron2_config import Tacotron2Config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.speakers import SpeakerManager
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(name="vctk",
meta_file_train="",
path=os.path.join(output_path, "../VCTK/"))
audio_config = BaseAudioConfig(
sample_rate=22050,
resample=
False, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training.
do_trim_silence=True,
trim_db=23.0,
signal_norm=False,
mel_fmin=0.0,
mel_fmax=8000,
spec_gain=1.0,
log_func="np.log",
preemphasis=0.0,
)
config = Tacotron2Config( # This is the config that is saved for the future use
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.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(
name="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",
OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
os.makedirs(OUTPATH, exist_ok=True)
# create a dummy config for testing data loaders.
c = BaseTTSConfig(text_cleaner="english_cleaners",
num_loader_workers=0,
batch_size=2,
use_noise_augment=False)
c.r = 5
c.data_path = "tests/data/ljspeech/"
ok_ljspeech = os.path.exists(c.data_path)
dataset_config = BaseDatasetConfig(
name="ljspeech_test", # ljspeech_test to multi-speaker
meta_file_train="metadata.csv",
meta_file_val=None,
path=c.data_path,
language="en",
)
DATA_EXIST = True
if not os.path.exists(c.data_path):
DATA_EXIST = False
print(" > Dynamic data loader test: {}".format(DATA_EXIST))
class TestTTSDataset(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.max_loader_iter = 4
# 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
from TTS.utils.downloaders import download_thorsten_de
# 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(
name="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
)
# download dataset if not already present
if not os.path.exists(dataset_config.path):
print("Downloading dataset")
download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
# 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,
from TTS.config.shared_configs import BaseAudioConfig
from TTS.tts.configs.shared_configs import BaseDatasetConfig, CapacitronVAEConfig
from TTS.tts.configs.tacotron_config import TacotronConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron import Tacotron
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
data_path = "/srv/data/"
# Using LJSpeech like dataset processing for the blizzard dataset
dataset_config = BaseDatasetConfig(name="ljspeech",
meta_file_train="metadata.csv",
path=data_path)
audio_config = BaseAudioConfig(
sample_rate=24000,
do_trim_silence=True,
trim_db=60.0,
signal_norm=True,
mel_fmin=80.0,
mel_fmax=12000,
spec_gain=20.0,
log_func="np.log10",
ref_level_db=20,
preemphasis=0.0,
min_level_db=-100,
)
from TTS.trainer import Trainer, TrainingArgs
from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.vits import Vits, VitsArgs
from TTS.tts.utils.languages import LanguageManager
from TTS.tts.utils.speakers import SpeakerManager
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
mailabs_path = "/home/julian/workspace/mailabs/**"
dataset_paths = glob(mailabs_path)
dataset_config = [
BaseDatasetConfig(name="mailabs",
meta_file_train=None,
path=path,
language=path.split("/")[-1]) for path in dataset_paths
]
audio_config = BaseAudioConfig(
sample_rate=16000,
win_length=1024,
hop_length=256,
num_mels=80,
preemphasis=0.0,
ref_level_db=20,
log_func="np.log",
do_trim_silence=False,
trim_db=23.0,
mel_fmin=0,
mel_fmax=None,