File size: 14,739 Bytes
45ee559
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
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) Ξ± 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 πŸš€
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()