coqui-ai / trainer Goto Github PK

> DataLoader initialization
| > Tokenizer:
        | > add_blank: True
        | > use_eos_bos: False
        | > use_phonemes: False
| > Number of instances : 20
 | > Preprocessing samples
 | > Max text length: 119
 | > Min text length: 21
 | > Avg text length: 45.2
 |
 | > Max audio length: 119952.0
 | > Min audio length: 18103.0
 | > Avg audio length: 48773.45
 | > Num. instances discarded samples: 0
 | > Batch group size: 0.
 > Using weighted sampler for attribute 'speaker_name' with alpha '1.0'
None
 > Attribute weights for '['ljspeech']'
 | > [0.22360679774997894]

 > EVALUATION 

 ! Run is kept in /home/caraduf/Models/YourTTS_ME_22k-February-07-2023_06+31AM-0000000
Traceback (most recent call last):
  File "/home/caraduf/CoquiTTS/Trainer/trainer/trainer.py", line 1659, in fit
    self._fit()
  File "/home/caraduf/CoquiTTS/Trainer/trainer/trainer.py", line 1614, in _fit
    self.eval_epoch()
  File "/home/caraduf/CoquiTTS/Trainer/trainer/trainer.py", line 1501, in eval_epoch
    outputs,
UnboundLocalError: local variable 'outputs' referenced before assignment

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_vctk

torch.set_num_threads(24)

# pylint: disable=W0105
"""
    This recipe replicates the first experiment proposed in the YourTTS paper (https://arxiv.org/abs/2112.02418).
    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.
    In addition, YourTTS can be trained in multilingual data, however, this recipe replicates the single language training using the VCTK dataset.
    If you are interested in multilingual training, we have commented on parameters on the VitsArgs class instance that should be enabled for multilingual training.
    In addition, you will need to add the extra datasets following the VCTK as an example.
"""
# 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/"

# Name of the run for the Trainer
RUN_NAME = "YourTTS_ME_22k"

Me_Rec_1_config = BaseDatasetConfig(
    formatter="ljspeech", dataset_name="ME_Rec1", meta_file_train="metadata.csv", path="/home/caraduf/Datasets/ME_22kHz/Rec_1_LARGE_V2_22.05kHz_dataset", language="fr-fr"
)

Me_Rec_2_config = BaseDatasetConfig(
    formatter="ljspeech", dataset_name="ME_Rec2", meta_file_train="metadata.csv", path="/home/caraduf/Datasets/ME_22kHz/Rec_2_LARGE_V2_22.05kHz_dataset", language="fr-fr"
)



# Add here all datasets configs, in our case we just want to train with the VCTK dataset then we need to add just VCTK. Note: If you want to added new datasets just added they here and it will automatically compute the speaker embeddings (d-vectors) for this new dataset :)
DATASETS_CONFIG_LIST = [
    Me_Rec_1_config,
    Me_Rec_2_config
]


# If you want to do transfer learning and speedup your training you can set here the path to the original YourTTS model
RESTORE_PATH = None  # "/root/.local/share/tts/tts_models--multilingual--multi-dataset--your_tts/model_file.pth"

# This paramter is usefull 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 = 22050

# Max audio length in seconds to be used in training (every audio bigger than it will be ignored)
MAX_AUDIO_LEN_IN_SECONDS = 10

# # Define the number of threads used during the audio resampling
# NUM_RESAMPLE_THREADS = 10
# # Check if VCTK dataset is not already downloaded, if not download it
# if not os.path.exists(VCTK_DOWNLOAD_PATH):
#     print(">>> Downloading VCTK dataset:")
#     download_vctk(VCTK_DOWNLOAD_PATH)
#     resample_files(VCTK_DOWNLOAD_PATH, SAMPLE_RATE, file_ext="flac", n_jobs=NUM_RESAMPLE_THREADS)

# # init configs
# vctk_config = BaseDatasetConfig(
#     formatter="vctk",
#     dataset_name="vctk",
#     meta_file_train="",
#     meta_file_val="",
#     path=VCTK_DOWNLOAD_PATH,
#     language="en",
#     ignored_speakers=[
#         "p261",
#         "p225",
#         "p294",
#         "p347",
#         "p238",
#         "p234",
#         "p248",
#         "p335",
#         "p245",
#         "p326",
#         "p302",
#     ],  # Ignore the test speakers to full replicate the paper experiment
# )


### 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_spakers_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 is needed for the YourTTS model
model_args = VitsArgs(
    d_vector_file=D_VECTOR_FILES,
    use_d_vector_file=True,
    d_vector_dim=512,
    num_layers_text_encoder=10,
    speaker_encoder_model_path=SPEAKER_ENCODER_CHECKPOINT_PATH,
    speaker_encoder_config_path=SPEAKER_ENCODER_CONFIG_PATH,
    resblock_type_decoder="1",  # On 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
    # Usefull parameters to enable the Speaker Consistency Loss (SCL) discribed in the paper
    # use_speaker_encoder_as_loss=True,
    # Usefull parameters to the enable multilingual training
    # use_language_embedding=True,
    # embedded_language_dim=4,
)

# General training config, here you can change the batch size and others usefull parameters
config = VitsConfig(
    output_path=OUT_PATH,
    model_args=model_args,
    run_name=RUN_NAME,
    project_name="YourTTS",
    run_description="""
            - Original YourTTS trained using shorter extacts made by new method
        """,
    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=4,
    eval_split_max_size=256,
    print_step=50,
    plot_step=100,
    log_model_step=1000,
    save_step=5000,
    save_n_checkpoints=10,
    save_checkpoints=True,
    target_loss="loss_1",
    print_eval=False,
    use_phonemes=False,
    phonemizer="espeak",
    phoneme_language="fr-fr",
    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\u00af\u00b7\u00df\u00e0\u00e1\u00e2\u00e3\u00e4\u00e6\u00e7\u00e8\u00e9\u00ea\u00eb\u00ec\u00ed\u00ee\u00ef\u00f1\u00f2\u00f3\u00f4\u00f5\u00f6\u00f9\u00fa\u00fb\u00fc\u00ff\u0101\u0105\u0107\u0113\u0119\u011b\u012b\u0131\u0142\u0144\u014d\u0151\u0153\u015b\u016b\u0171\u017a\u017c\u01ce\u01d0\u01d2\u01d4\u0430\u0431\u0432\u0433\u0434\u0435\u0436\u0437\u0438\u0439\u043a\u043b\u043c\u043d\u043e\u043f\u0440\u0441\u0442\u0443\u0444\u0445\u0446\u0447\u0448\u0449\u044a\u044b\u044c\u044d\u044e\u044f\u0451\u0454\u0456\u0457\u0491\u2013!'(),-.:;? ",
        
        characters="!',-.:?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz «»ÀÇÉÊàâçèéêëîïôùûœ–’…",
        punctuations="!'(),-.:;? ",
        phonemes="",
        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=True,
    test_sentences=[
        [
            "Il m'a fallu du temps pour créer cette voix alors ma bouche ne restera pas fermée.",
            # "ME",
            None,
            "fr_FR",
        ],
        [
            "Il m'a fallu beaucoup de temps pour développer une voix, et maintenant que je l'ai, je ne vais pas me taire.",
            # "ME",
            None,
            "fr_FR",
        ],
        [
            "Mais son âge rendait cette dernière qualité plus saillante.",
            # "ME",
            None,
            "fr_FR",
        ],
        # [
        #     "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
        #     "VCTK_p277",
        #     None,
        #     "en",
        # ],
        # [
        #     "Be a voice, not an echo.",
        #     "VCTK_p239",
        #     None,
        #     "en",
        # ],
        # [
        #     "I'm sorry Dave. I'm afraid I can't do that.",
        #     "VCTK_p258",
        #     None,
        #     "en",
        # ],
        # [
        #     "This cake is great. It's so delicious and moist.",
        #     "VCTK_p244",
        #     None,
        #     "en",
        # ],
        # [
        #     "Prior to November 22, 1963.",
        #     "VCTK_p305",
        #     None,
        #     "en",
        # ],
    ],
    # 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={"speaker_name": 1.0},
    weighted_sampler_multipliers={},
    # It defines the Speaker Consistency Loss (SCL) α to 9 like the 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()

python3 YourTTS_recipe.py

  File "<stdin>", line 1, in <module>
  File "...\lib\site-packages\trainer\__init__.py", line 4, in <module>
    from trainer.trainer import *
  File "...\lib\site-packages\trainer\trainer.py", line 47, in <module>
    multiprocessing.set_start_method("fork")
  File "...\lib\multiprocessing\context.py", line 246, in set_start_method
    self._actual_context = self.get_context(method)
  File "...\lib\multiprocessing\context.py", line 238, in get_context
    return super().get_context(method)
  File "...\lib\multiprocessing\context.py", line 192, in get_context
    raise ValueError('cannot find context for %r' % method) from None
ValueError: cannot find context for 'fork'

 > Restoring from best_model_22016.pth ...
 > Restoring Model...
 > Restoring Optimizer...
Traceback (most recent call last):
  File "/home/caraduf/Models/train_yourtts_16kHz.py", line 318, in <module>
    trainer = Trainer(
  File "/home/caraduf/CoquiTTS/Trainer/trainer/trainer.py", line 507, in __init__
    (self.model, self.optimizer, self.scaler, self.restore_step, self.restore_epoch) = self.restore_model(
  File "/home/caraduf/CoquiTTS/Trainer/trainer/trainer.py", line 711, in restore_model
    optimizer = _restore_list_objs(checkpoint["optimizer"], optimizer)
  File "/home/caraduf/CoquiTTS/Trainer/trainer/trainer.py", line 701, in _restore_list_objs
    obj.load_state_dict(states)
AttributeError: 'list' object has no attribute 'load_state_dict'

def _restore_list_objs(states, obj):           
            if isinstance(obj, list):
                for idx, state in enumerate(states):
                    obj[idx].load_state_dict(state)
            if isinstance(obj, dict):
                for key, state in states.items():
                    obj[key].load_state_dict(state)
            else:
                obj.load_state_dict(states)
            return obj

python3 train_yourtts.py

| > stats_path:None
2023-06-14T07:29:43.025431079Z  | > base:10
2023-06-14T07:29:43.025437149Z  | > hop_length:256
2023-06-14T07:29:43.025444429Z  | > win_length:1024
2023-06-14T07:29:43.025450699Z  > initialization of speaker-embedding layers.
2023-06-14T07:29:43.025462919Z Traceback (most recent call last):
2023-06-14T07:29:43.025469199Z   File "/workspace/coqui-tts/train.py", line 320, in <module>
2023-06-14T07:29:43.025476859Z     trainer = Trainer(
2023-06-14T07:29:43.025484659Z   File "/usr/local/lib/python3.10/dist-packages/trainer/trainer.py", line 405, in __init__
2023-06-14T07:29:43.025494939Z     self.use_cuda, self.num_gpus = self.setup_training_environment(args=args, config=config, gpu=gpu)
2023-06-14T07:29:43.025500099Z   File "/usr/local/lib/python3.10/dist-packages/trainer/trainer.py", line 632, in setup_training_environment
2023-06-14T07:29:43.025543959Z     resource.setrlimit(resource.RLIMIT_NOFILE, (4096, rlimit[1]))
2023-06-14T07:29:43.025560229Z ValueError: not allowed to raise maximum limit

Traceback (most recent call last):
  File "runs/train_vits.py", line 85, in <module>
    eval_samples=eval_samples,
  File "/home/fijipants/miniconda3/envs/coqui-0.6.1/lib/python3.7/site-packages/trainer/trainer.py", line 359, in __init__
    self.dashboard_logger = logger_factory(config, output_path)
  File "/home/fijipants/miniconda3/envs/coqui-0.6.1/lib/python3.7/site-packages/trainer/logging/__init__.py", line 36, in logger_factory
    entity=config.wandb_entity,
TypeError: Can't instantiate abstract class WandbLogger with abstract methods add_audio, add_figure, add_scalar