import logging
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import yaml
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from torch.utils.data import DataLoader
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from funasr.datasets.large_datasets.dataset import Dataset
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from funasr.iterators.abs_iter_factory import AbsIterFactory
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def read_symbol_table(symbol_table_file):
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if isinstance(symbol_table_file, str):
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symbol_table = {}
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with open(symbol_table_file, "r", encoding="utf8") as fin:
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for i, line in enumerate(fin):
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char = line.strip()
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symbol_table[char] = i
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else:
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assert isinstance(symbol_table_file, list)
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symbol_table = {}
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for i, char in enumerate(symbol_table_file):
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symbol_table[char] = i
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return symbol_table
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def load_seg_dict(seg_dict_file):
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seg_dict = {}
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assert isinstance(seg_dict_file, str)
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with open(seg_dict_file, "r", encoding="utf8") as f:
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lines = f.readlines()
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for line in lines:
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s = line.strip().split()
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key = s[0]
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value = s[1:]
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seg_dict[key] = " ".join(value)
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return seg_dict
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class ArkDataLoader(AbsIterFactory):
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def __init__(self, data_list, dict_file, dataset_conf, frontend_conf=None, seg_dict_file=None, punc_dict_file=None, mode="train"):
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symbol_table = read_symbol_table(dict_file) if dict_file is not None else None
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if seg_dict_file is not None:
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seg_dict = load_seg_dict(seg_dict_file)
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else:
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seg_dict = None
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if punc_dict_file is not None:
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punc_dict = read_symbol_table(punc_dict_file)
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else:
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punc_dict = None
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self.dataset_conf = dataset_conf
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self.frontend_conf = frontend_conf
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logging.info("dataloader config: {}".format(self.dataset_conf))
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batch_mode = self.dataset_conf.get("batch_mode", "padding")
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self.dataset = Dataset(data_list, symbol_table, seg_dict, punc_dict,
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self.dataset_conf, self.frontend_conf, mode=mode, batch_mode=batch_mode)
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def build_iter(self, epoch, shuffle=True):
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self.dataset.set_epoch(epoch)
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data_loader = DataLoader(self.dataset,
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batch_size=None,
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pin_memory=True,
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num_workers=self.dataset_conf.get("num_workers", 8))
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return data_loader
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