FunASR/funasr/datasets/large_datasets/build_dataloader.py

import logging

import yaml

from torch.utils.data import DataLoader
from funasr.datasets.large_datasets.dataset import Dataset
from funasr.iterators.abs_iter_factory import AbsIterFactory


def read_symbol_table(symbol_table_file):
    if isinstance(symbol_table_file, str):
        symbol_table = {}
        with open(symbol_table_file, "r", encoding="utf8") as fin:
            for i, line in enumerate(fin):
                char = line.strip()
                symbol_table[char] = i
    else:
        assert isinstance(symbol_table_file, list)
        symbol_table = {}
        for i, char in enumerate(symbol_table_file):
            symbol_table[char] = i
    return symbol_table

def load_seg_dict(seg_dict_file):
    seg_dict = {}
    assert isinstance(seg_dict_file, str)
    with open(seg_dict_file, "r", encoding="utf8") as f:
        lines = f.readlines()
        for line in lines:
            s = line.strip().split()
            key = s[0]
            value = s[1:]
            seg_dict[key] = " ".join(value)
    return seg_dict

class ArkDataLoader(AbsIterFactory):
    def __init__(self, data_list, dict_file, dataset_conf, seg_dict_file=None, mode="train"):
        symbol_table = read_symbol_table(dict_file) if dict_file is not None else None
        if seg_dict_file is not None:
            seg_dict = load_seg_dict(seg_dict_file)
        else:
            seg_dict = None
        self.dataset_conf = dataset_conf
        logging.info("dataloader config: {}".format(self.dataset_conf))
        batch_mode = self.dataset_conf.get("batch_mode", "padding")
        self.dataset = Dataset(data_list, symbol_table, seg_dict,
                               self.dataset_conf, mode=mode, batch_mode=batch_mode)

    def build_iter(self, epoch, shuffle=True):
        self.dataset.set_epoch(epoch)
        data_loader = DataLoader(self.dataset,
                                 batch_size=None,
                                 pin_memory=True,
                                 num_workers=self.dataset_conf.get("num_workers", 8))
        return data_loader