first commit for takway.ai

funasr/datasets/large_datasets/build_dataloader.py

@@ -0,0 +1,109 @@
+import logging
+from pathlib import Path
+from typing import Iterable
+from typing import List
+from typing import Union
+
+import sentencepiece as spm
+from torch.utils.data import DataLoader
+
+from funasr.datasets.large_datasets.dataset import Dataset
+from funasr.datasets.large_datasets.abs_iter_factory import AbsIterFactory
+from funasr.tokenizer.abs_tokenizer import AbsTokenizer
+
+from funasr.register import tables
+
+
+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 SentencepiecesTokenizer(AbsTokenizer):
+    def __init__(self, model: Union[Path, str]):
+        self.model = str(model)
+        self.sp = None
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}(model="{self.model}")'
+
+    def _build_sentence_piece_processor(self):
+        if self.sp is None:
+            self.sp = spm.SentencePieceProcessor()
+            self.sp.load(self.model)
+
+    def text2tokens(self, line: str) -> List[str]:
+        self._build_sentence_piece_processor()
+        return self.sp.EncodeAsPieces(line)
+
+    def tokens2text(self, tokens: Iterable[str]) -> str:
+        self._build_sentence_piece_processor()
+        return self.sp.DecodePieces(list(tokens))
+
+
+@tables.register("dataset_classes", "LargeDataset")
+class LargeDataLoader(AbsIterFactory):
+    def __init__(self, args, mode="train"):
+        symbol_table, seg_dict, punc_dict, bpe_tokenizer = None, None, None, None
+        if hasattr(args, "token_list") and args.token_list is not None:
+            symbol_table = read_symbol_table(args.token_list)
+        if hasattr(args, "seg_dict_file") and args.seg_dict_file is not None:
+            seg_dict = load_seg_dict(args.seg_dict_file)
+        if hasattr(args, "punc_list") and args.punc_list is not None:
+            punc_dict = read_symbol_table(args.punc_list)
+        if hasattr(args, "bpemodel") and args.bpemodel is not None:
+            bpe_tokenizer = SentencepiecesTokenizer(args.bpemodel)
+        self.dataset_conf = args.dataset_conf
+        if "frontend_conf" not in args:
+            self.frontend_conf = None
+        else:
+            self.frontend_conf = args.frontend_conf
+        self.speed_perturb = args.speed_perturb if hasattr(args, "speed_perturb") else None
+        logging.info("dataloader config: {}".format(self.dataset_conf))
+        batch_mode = self.dataset_conf.get("batch_mode", "padding")
+        data_list = args.train_data_file if mode == "train" else args.valid_data_file
+        self.dataset = Dataset(
+            data_list,
+            symbol_table,
+            seg_dict,
+            punc_dict,
+            bpe_tokenizer,
+            self.dataset_conf,
+            self.frontend_conf,
+            speed_perturb=self.speed_perturb if mode == "train" else None,
+            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