From 1596f6f414f6f41da66506debb1dff19fffeb3ec Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 24 六月 2024 11:55:17 +0800
Subject: [PATCH] fixbug hotwords

---
 funasr/datasets/large_datasets/build_dataloader.py |   87 +++++++++++++++++++++++++++++++++++--------
 1 files changed, 71 insertions(+), 16 deletions(-)

diff --git a/funasr/datasets/large_datasets/build_dataloader.py b/funasr/datasets/large_datasets/build_dataloader.py
index 146723d..da04717 100644
--- a/funasr/datasets/large_datasets/build_dataloader.py
+++ b/funasr/datasets/large_datasets/build_dataloader.py
@@ -1,10 +1,17 @@
 import logging
+from pathlib import Path
+from typing import Iterable
+from typing import List
+from typing import Union
 
-import yaml
-
+import sentencepiece as spm
 from torch.utils.data import DataLoader
+
 from funasr.datasets.large_datasets.dataset import Dataset
-from funasr.iterators.abs_iter_factory import AbsIterFactory
+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):
@@ -21,6 +28,7 @@
             symbol_table[char] = i
     return symbol_table
 
+
 def load_seg_dict(seg_dict_file):
     seg_dict = {}
     assert isinstance(seg_dict_file, str)
@@ -33,22 +41,69 @@
             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 seg_dict_file is not None:
-            seg_dict = load_seg_dict(seg_dict_file)
+
+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:
-            seg_dict = None
-        self.dataset_conf = dataset_conf
+            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))
-        self.dataset = Dataset(data_list, symbol_table, seg_dict,
-                               self.dataset_conf, mode=mode)
+        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))
+        data_loader = DataLoader(
+            self.dataset,
+            batch_size=None,
+            pin_memory=True,
+            num_workers=self.dataset_conf.get("num_workers", 8),
+        )
         return data_loader

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