在Ubuntu中使用Python，培训停止，并进行描述：分割故障（核心倾倒）

Question

我正在研究TTS和进行培训时，培训会停止信息分割故障（核心倾倒）

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.tacotron2_config import Tacotron2Config

# 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.tacotron2 import Tacotron2
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
from TTS.config.shared_configs import BaseAudioConfig


# 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.
audio_config = BaseAudioConfig(
    sample_rate=22050,
    do_trim_silence=True,
    trim_db=60.0,
    signal_norm=False,
    mel_fmin=0.0,
    mel_fmax=8000,
    spec_gain=1.0,
    log_func="np.log",
    ref_level_db=20,
    preemphasis=0.0,
)

config = Tacotron2Config(  # This is the config that is saved for the future use
    audio=audio_config,
    batch_size=4,
    eval_batch_size=4,
    num_loader_workers=2,
    num_eval_loader_workers=2,
    run_eval=True,
    test_delay_epochs=-1,
    r=6,
    gradual_training=[[0, 6, 4], [10000, 4, 4], [50000, 3, 4], [100000, 2, 4]],
    double_decoder_consistency=True,
    epochs=1000,
    text_cleaner="phoneme_cleaners",
    use_phonemes=True,
    phoneme_language="en-us",
    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
    print_step=10,
    print_eval=True,
    mixed_precision=False,
    output_path=output_path,
    datasets=[dataset_config],
)

# 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.


from TTS.tts.datasets import load_tts_samples


# custom formatter implementation
def formatter(root_path, manifest_file, **kwargs):  # pylint: disable=unused-argument
    """Assumes each line as ```<filename>|<transcription>```
    """
    txt_file = os.path.join(root_path, manifest_file)
    items = []
    speaker_name = "my_speaker"
    with open(txt_file, "r", encoding="utf-8") as ttf:
        for line in ttf:
            cols = line.split("|")

            #wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
            wav_file = "/media/DATA-2/TTS/coqui/LJSpeech-1.1/wavs/" + cols[0] + ".wav"
            text = cols[1]
            items.append({"text":text, "audio_file":wav_file, "speaker_name":speaker_name})
    return items

# load training samples
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, formatter=formatter)
# 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.
print("================== train_samples ========================")
print("len data : ", len(train_samples))
print(train_samples)

print("======================== eval_samples ================")
print("len data : ", len(eval_samples))
print(eval_samples)




model = Tacotron2(config, ap, tokenizer, speaker_manager=None)

# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the