dataset=DataList(lists, shuffle=True, partition=True) # [str] str is a json line has key/wav/txt processor.parse_raw # [{key, wav:torch.Size([1, sample_rate * duration]), txt, sample_rate}] import torchaudio import librosa def get_wav_length(wav_path): return librosa.get_duration...
TRAIN_EPOCH, value=i // eval_steps) if packed_dataset and i > 0: problem = registry.problem(self._hparams.problem.name + "_packed") p_hparams = problem.get_hparams(self._hparams) self._hparams.problem = problem self._hparams.problem_hparams = p_hparams self._estimator.train( self._...
train_dataset = PrepareDataset(vocab_file=config.get("vocab_file"), max_seq_len=config.get("max_seq_len"), num_class=config.get("num_class"), data_file=args.train_data) logging.info("Load Training Dataset Done, Total training line: %s", train_dataset.__len__()) if args.eval_data...
dataset.train) train_batch_size = config.dataset.train.batch_size train_steps, train_epochs = get_train_steps_and_epochs( train_steps=config.get('train_steps'), train_epochs=config.get('train_epochs'), train_batch_size=train_batch_size, train_examples=train_examples) logging.info( '...
train_dataset=self.instances, validation_dataset=self.instances, num_epochs=2, serialization_dir=self.TEST_DIR) trainer.train() new_lr_scheduler = LearningRateScheduler.from_params( self.optimizer, Params({"type":"exponential","gamma":0.5})) ...
# 需要导入模块: import train [as 别名]# 或者: from train importTrainer[as 别名]defmain():data_transformer = DataTransformer(config.dataset_path, use_cuda=config.use_cuda) vanilla_encoder = VanillaEncoder(vocab_size=data_transformer.vocab_size, ...
# In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.train_file is not None or data_args.validation_file is not None: data_files = {} if data_args.train_file is not None: ...
from preprocess import get_dataset, BaseProcessor from tqdm import tqdm import datasets # import evaluate # import nltk # Here to have a nice missing dependency error message early on import numpy as np from datasets import load_dataset, DatasetDict, Dataset, concatenate_datasets ...
import numpy as np import torch # from torch.utils.tensorboard import SummaryWriter import torch.nn as nn import argparse from tqdm import tqdm from config import device, print_freq, vocab_size, sos_id, eos_id from data_gen import AiShellDataset, pad_collate from transformer.decoder import ...
valid_dataset, num_workers=1, batch_size=config.batch_size, collate_fn=train_dataset.TextMelCollate, pin_memory=True, ) with open(config.model_config_path, 'r') as fin: conf = CONFIG.load_cfg(fin) conf.n_vocab = config.n_symbols conf.n_speaker = config.speaker_n_label...