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Add CML-TTS dataset YourTTS training recipe (#2934)

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Edresson Casanova 2023-09-12 06:49:14 -03:00 committed by GitHub
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39
TTS/tts/datasets/formatters.py
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@ -13,6 +13,45 @@ from tqdm import tqdm
########################
def cml_tts(root_path, meta_file, ignored_speakers=None):
"""Normalizes the CML-TTS meta data file to TTS format
https://github.com/freds0/CML-TTS-Dataset/"""
filepath = os.path.join(root_path, meta_file)
# ensure there are 4 columns for every line
with open(filepath, "r", encoding="utf8") as f:
lines = f.readlines()
num_cols = len(lines[0].split("|")) # take the first row as reference
for idx, line in enumerate(lines[1:]):
if len(line.split("|")) != num_cols:
print(f" > Missing column in line {idx + 1} -> {line.strip()}")
# load metadata
metadata = pd.read_csv(os.path.join(root_path, meta_file), sep="|")
assert all(x in metadata.columns for x in ["wav_filename", "transcript"])
client_id = None if "client_id" in metadata.columns else "default"
emotion_name = None if "emotion_name" in metadata.columns else "neutral"
items = []
not_found_counter = 0
for row in metadata.itertuples():
if client_id is None and ignored_speakers is not None and row.client_id in ignored_speakers:
continue
audio_path = os.path.join(root_path, row.wav_filename)
if not os.path.exists(audio_path):
not_found_counter += 1
continue
items.append(
{
"text": row.transcript,
"audio_file": audio_path,
"speaker_name": client_id if client_id is not None else row.client_id,
"emotion_name": emotion_name if emotion_name is not None else row.emotion_name,
"root_path": root_path,
}
)
if not_found_counter > 0:
print(f" | > [!] {not_found_counter} files not found")
return items
def coqui(root_path, meta_file, ignored_speakers=None):
"""Interal dataset formatter."""
filepath = os.path.join(root_path, meta_file)

432
recipes/multilingual/cml_yourtts/train_yourtts.py Normal file
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@ -0,0 +1,432 @@
import os
import torch
from trainer import Trainer, TrainerArgs
from TTS.bin.compute_embeddings import compute_embeddings
from TTS.bin.resample import resample_files
from TTS.config.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 CharactersConfig, Vits, VitsArgs, VitsAudioConfig
from TTS.utils.downloaders import download_libri_tts
torch.set_num_threads(24)
# pylint: disable=W0105
"""
This recipe replicates the first experiment proposed in the CML-TTS paper (https://arxiv.org/abs/2306.10097). It uses the YourTTS model.
YourTTS model is based on the VITS model however it uses external speaker embeddings extracted from a pre-trained speaker encoder and has small architecture changes.
"""
CURRENT_PATH = os.path.dirname(os.path.abspath(__file__))
# Name of the run for the Trainer
RUN_NAME = "YourTTS-CML-TTS"
# Path where you want to save the models outputs (configs, checkpoints and tensorboard logs)
OUT_PATH = os.path.dirname(os.path.abspath(__file__)) # "/raid/coqui/Checkpoints/original-YourTTS/"
# If you want to do transfer learning and speedup your training you can set here the path to the CML-TTS available checkpoint that cam be downloaded here: https://drive.google.com/u/2/uc?id=1yDCSJ1pFZQTHhL09GMbOrdjcPULApa0p
RESTORE_PATH = "/raid/edresson/CML_YourTTS/checkpoints_yourtts_cml_tts_dataset/best_model.pth" # Download the checkpoint here: https://drive.google.com/u/2/uc?id=1yDCSJ1pFZQTHhL09GMbOrdjcPULApa0p
# This paramter is useful to debug, it skips the training epochs and just do the evaluation and produce the test sentences
SKIP_TRAIN_EPOCH = False
# Set here the batch size to be used in training and evaluation
BATCH_SIZE = 32
# Training Sampling rate and the target sampling rate for resampling the downloaded dataset (Note: If you change this you might need to redownload the dataset !!)
# Note: If you add new datasets, please make sure that the dataset sampling rate and this parameter are matching, otherwise resample your audios
SAMPLE_RATE = 24000
# Max audio length in seconds to be used in training (every audio bigger than it will be ignored)
MAX_AUDIO_LEN_IN_SECONDS = float("inf")
### Download CML-TTS dataset
# You need to download the dataset for all languages manually and extract it to a path and then set the CML_DATASET_PATH to this path: https://github.com/freds0/CML-TTS-Dataset#download
CML_DATASET_PATH = "./datasets/CML-TTS-Dataset/"
### Download LibriTTS dataset
# it will automatic download the dataset, if you have problems you can comment it and manually donwload and extract it ! Download link: https://www.openslr.org/resources/60/train-clean-360.tar.gz
LIBRITTS_DOWNLOAD_PATH = "./datasets/LibriTTS/"
# Check if LibriTTS dataset is not already downloaded, if not download it
if not os.path.exists(LIBRITTS_DOWNLOAD_PATH):
print(">>> Downloading LibriTTS dataset:")
download_libri_tts(LIBRITTS_DOWNLOAD_PATH, subset="libri-tts-clean-360")
# init LibriTTS configs
libritts_config = BaseDatasetConfig(
formatter="libri_tts",
dataset_name="libri_tts",
meta_file_train="",
meta_file_val="",
path=os.path.join(LIBRITTS_DOWNLOAD_PATH, "train-clean-360/"),
language="en"
)
# init CML-TTS configs
pt_config = BaseDatasetConfig(
formatter="cml_tts",
dataset_name="cml_tts",
meta_file_train="train.csv",
meta_file_val="",
path=os.path.join(CML_DATASET_PATH,"cml_tts_dataset_portuguese_v0.1/"),
language="pt-br"
)
pl_config = BaseDatasetConfig(
formatter="cml_tts",
dataset_name="cml_tts",
meta_file_train="train.csv",
meta_file_val="",
path=os.path.join(CML_DATASET_PATH,"cml_tts_dataset_polish_v0.1/"),
language="pl"
)
it_config = BaseDatasetConfig(
formatter="cml_tts",
dataset_name="cml_tts",
meta_file_train="train.csv",
meta_file_val="",
path=os.path.join(CML_DATASET_PATH,"cml_tts_dataset_italian_v0.1/"),
language="it"
)
fr_config = BaseDatasetConfig(
formatter="cml_tts",
dataset_name="cml_tts",
meta_file_train="train.csv",
meta_file_val="",
path=os.path.join(CML_DATASET_PATH,"cml_tts_dataset_french_v0.1/"),
language="fr"
)
du_config = BaseDatasetConfig(
formatter="cml_tts",
dataset_name="cml_tts",
meta_file_train="train.csv",
meta_file_val="",
path=os.path.join(CML_DATASET_PATH,"cml_tts_dataset_dutch_v0.1/"),
language="du"
)
ge_config = BaseDatasetConfig(
formatter="cml_tts",
dataset_name="cml_tts",
meta_file_train="train.csv",
meta_file_val="",
path=os.path.join(CML_DATASET_PATH,"cml_tts_dataset_german_v0.1/"),
language="ge"
)
sp_config = BaseDatasetConfig(
formatter="cml_tts",
dataset_name="cml_tts",
meta_file_train="train.csv",
meta_file_val="",
path=os.path.join(CML_DATASET_PATH,"cml_tts_dataset_spanish_v0.1/"),
language="sp"
)
# Add here all datasets configs Note: If you want to add new datasets, just add them here and it will automatically compute the speaker embeddings (d-vectors) for this new dataset :)
DATASETS_CONFIG_LIST = [libritts_config, pt_config, pl_config, it_config, fr_config, du_config, ge_config, sp_config]
### Extract speaker embeddings
SPEAKER_ENCODER_CHECKPOINT_PATH = (
"https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar"
)
SPEAKER_ENCODER_CONFIG_PATH = "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json"
D_VECTOR_FILES = [] # List of speaker embeddings/d-vectors to be used during the training
# Iterates all the dataset configs checking if the speakers embeddings are already computated, if not compute it
for dataset_conf in DATASETS_CONFIG_LIST:
# Check if the embeddings weren't already computed, if not compute it
embeddings_file = os.path.join(dataset_conf.path, "speakers.pth")
if not os.path.isfile(embeddings_file):
print(f">>> Computing the speaker embeddings for the {dataset_conf.dataset_name} dataset")
compute_embeddings(
SPEAKER_ENCODER_CHECKPOINT_PATH,
SPEAKER_ENCODER_CONFIG_PATH,
embeddings_file,
old_speakers_file=None,
config_dataset_path=None,
formatter_name=dataset_conf.formatter,
dataset_name=dataset_conf.dataset_name,
dataset_path=dataset_conf.path,
meta_file_train=dataset_conf.meta_file_train,
meta_file_val=dataset_conf.meta_file_val,
disable_cuda=False,
no_eval=False,
)
D_VECTOR_FILES.append(embeddings_file)
# Audio config used in training.
audio_config = VitsAudioConfig(
sample_rate=SAMPLE_RATE,
hop_length=256,
win_length=1024,
fft_size=1024,
mel_fmin=0.0,
mel_fmax=None,
num_mels=80,
)
# Init VITSArgs setting the arguments that are needed for the YourTTS model
model_args = VitsArgs(
spec_segment_size=62,
hidden_channels=192,
hidden_channels_ffn_text_encoder=768,
num_heads_text_encoder=2,
num_layers_text_encoder=10,
kernel_size_text_encoder=3,
dropout_p_text_encoder=0.1,
d_vector_file=D_VECTOR_FILES,
use_d_vector_file=True,
d_vector_dim=512,
speaker_encoder_model_path=SPEAKER_ENCODER_CHECKPOINT_PATH,
speaker_encoder_config_path=SPEAKER_ENCODER_CONFIG_PATH,
resblock_type_decoder="2", # In the paper, we accidentally trained the YourTTS using ResNet blocks type 2, if you like you can use the ResNet blocks type 1 like the VITS model
# Useful parameters to enable the Speaker Consistency Loss (SCL) described in the paper
use_speaker_encoder_as_loss=False,
# Useful parameters to enable multilingual training
use_language_embedding=True,
embedded_language_dim=4,
)
# General training config, here you can change the batch size and others useful parameters
config = VitsConfig(
output_path=OUT_PATH,
model_args=model_args,
run_name=RUN_NAME,
project_name="YourTTS",
run_description="""
- YourTTS trained using CML-TTS and LibriTTS datasets
""",
dashboard_logger="tensorboard",
logger_uri=None,
audio=audio_config,
batch_size=BATCH_SIZE,
batch_group_size=48,
eval_batch_size=BATCH_SIZE,
num_loader_workers=8,
eval_split_max_size=256,
print_step=50,
plot_step=100,
log_model_step=1000,
save_step=5000,
save_n_checkpoints=2,
save_checkpoints=True,
target_loss="loss_1",
print_eval=False,
use_phonemes=False,
phonemizer="espeak",
phoneme_language="en",
compute_input_seq_cache=True,
add_blank=True,
text_cleaner="multilingual_cleaners",
characters=CharactersConfig(
characters_class="TTS.tts.models.vits.VitsCharacters",
pad="_",
eos="&",
bos="*",
blank=None,
characters="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\u00a1\u00a3\u00b7\u00b8\u00c0\u00c1\u00c2\u00c3\u00c4\u00c5\u00c7\u00c8\u00c9\u00ca\u00cb\u00cc\u00cd\u00ce\u00cf\u00d1\u00d2\u00d3\u00d4\u00d5\u00d6\u00d9\u00da\u00db\u00dc\u00df\u00e0\u00e1\u00e2\u00e3\u00e4\u00e5\u00e7\u00e8\u00e9\u00ea\u00eb\u00ec\u00ed\u00ee\u00ef\u00f1\u00f2\u00f3\u00f4\u00f5\u00f6\u00f9\u00fa\u00fb\u00fc\u0101\u0104\u0105\u0106\u0107\u010b\u0119\u0141\u0142\u0143\u0144\u0152\u0153\u015a\u015b\u0161\u0178\u0179\u017a\u017b\u017c\u020e\u04e7\u05c2\u1b20",
punctuations="\u2014!'(),-.:;?\u00bf ",
phonemes="iy\u0268\u0289\u026fu\u026a\u028f\u028ae\u00f8\u0258\u0259\u0275\u0264o\u025b\u0153\u025c\u025e\u028c\u0254\u00e6\u0250a\u0276\u0251\u0252\u1d7b\u0298\u0253\u01c0\u0257\u01c3\u0284\u01c2\u0260\u01c1\u029bpbtd\u0288\u0256c\u025fk\u0261q\u0262\u0294\u0274\u014b\u0272\u0273n\u0271m\u0299r\u0280\u2c71\u027e\u027d\u0278\u03b2fv\u03b8\u00f0sz\u0283\u0292\u0282\u0290\u00e7\u029dx\u0263\u03c7\u0281\u0127\u0295h\u0266\u026c\u026e\u028b\u0279\u027bj\u0270l\u026d\u028e\u029f\u02c8\u02cc\u02d0\u02d1\u028dw\u0265\u029c\u02a2\u02a1\u0255\u0291\u027a\u0267\u025a\u02de\u026b'\u0303' ",
is_unique=True,
is_sorted=True,
),
phoneme_cache_path=None,
precompute_num_workers=12,
start_by_longest=True,
datasets=DATASETS_CONFIG_LIST,
cudnn_benchmark=False,
max_audio_len=SAMPLE_RATE * MAX_AUDIO_LEN_IN_SECONDS,
mixed_precision=False,
test_sentences=[
[
"Voc\u00ea ter\u00e1 a vista do topo da montanha que voc\u00ea escalar.",
"9351",
None,
"pt-br"
],
[
"Quando voc\u00ea n\u00e3o corre nenhum risco, voc\u00ea arrisca tudo.",
"12249",
None,
"pt-br"
],
[
"S\u00e3o necess\u00e1rios muitos anos de trabalho para ter sucesso da noite para o dia.",
"2961",
None,
"pt-br"
],
[
"You'll have the view of the top of the mountain that you climb.",
"LTTS_6574",
None,
"en"
],
[
"When you don\u2019t take any risks, you risk everything.",
"LTTS_6206",
None,
"en"
],
[
"Are necessary too many years of work to succeed overnight.",
"LTTS_5717",
None,
"en"
],
[
"Je hebt uitzicht op de top van de berg die je beklimt.",
"960",
None,
"du"
],
[
"Als je geen risico neemt, riskeer je alles.",
"2450",
None,
"du"
],
[
"Zijn te veel jaren werk nodig om van de ene op de andere dag te slagen.",
"10984",
None,
"du"
],
[
"Vous aurez la vue sur le sommet de la montagne que vous gravirez.",
"6381",
None,
"fr"
],
[
"Quand tu ne prends aucun risque, tu risques tout.",
"2825",
None,
"fr"
],
[
"Sont n\u00e9cessaires trop d'ann\u00e9es de travail pour r\u00e9ussir du jour au lendemain.",
"1844",
None,
"fr"
],
[
"Sie haben die Aussicht auf die Spitze des Berges, den Sie erklimmen.",
"2314",
None,
"ge"
],
[
"Wer nichts riskiert, riskiert alles.",
"7483",
None,
"ge"
],
[
"Es sind zu viele Jahre Arbeit notwendig, um \u00fcber Nacht erfolgreich zu sein.",
"12461",
None,
"ge"
],
[
"Avrai la vista della cima della montagna che sali.",
"4998",
None,
"it"
],
[
"Quando non corri alcun rischio, rischi tutto.",
"6744",
None,
"it"
],
[
"Are necessary too many years of work to succeed overnight.",
"1157",
None,
"it"
],
[
"B\u0119dziesz mie\u0107 widok na szczyt g\u00f3ry, na kt\u00f3r\u0105 si\u0119 wspinasz.",
"7014",
None,
"pl"
],
[
"Kiedy nie podejmujesz \u017cadnego ryzyka, ryzykujesz wszystko.",
"3492",
None,
"pl"
],
[
"Potrzebne s\u0105 zbyt wiele lat pracy, aby odnie\u015b\u0107 sukces z dnia na dzie\u0144.",
"1890",
None,
"pl"
],
[
"Tendr\u00e1s la vista de la cima de la monta\u00f1a que subes",
"101",
None,
"sp"
],
[
"Cuando no te arriesgas, lo arriesgas todo.",
"5922",
None,
"sp"
],
[
"Son necesarios demasiados a\u00f1os de trabajo para triunfar de la noche a la ma\u00f1ana.",
"10246",
None,
"sp"
]
],
# Enable the weighted sampler
use_weighted_sampler=True,
# Ensures that all speakers are seen in the training batch equally no matter how many samples each speaker has
# weighted_sampler_attrs={"language": 1.0, "speaker_name": 1.0},
weighted_sampler_attrs={"language": 1.0},
weighted_sampler_multipliers={
# "speaker_name": {
# you can force the batching scheme to give a higher weight to a certain speaker and then this speaker will appears more frequently on the batch.
# It will speedup the speaker adaptation process. Considering the CML train dataset and "new_speaker" as the speaker name of the speaker that you want to adapt.
# The line above will make the balancer consider the "new_speaker" as 106 speakers so 1/4 of the number of speakers present on CML dataset.
# 'new_speaker': 106, # (CML tot. train speaker)/4 = (424/4) = 106
# }
},
# It defines the Speaker Consistency Loss (SCL) α to 9 like the YourTTS paper
speaker_encoder_loss_alpha=9.0,
)
# Load all the datasets samples and split traning and evaluation sets
train_samples, eval_samples = load_tts_samples(
config.datasets,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size
)
# Init the model
model = Vits.init_from_config(config)
# Init the trainer and 🚀
trainer = Trainer(
TrainerArgs(restore_path=RESTORE_PATH, skip_train_epoch=SKIP_TRAIN_EPOCH),
config,
output_path=OUT_PATH,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
)
trainer.fit()