From 4137f5cf26e7c4b40853959cd2574edfde03aa60 Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期五, 07 四月 2023 21:03:34 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR into dev_dzh
---
funasr/runtime/python/libtorch/funasr_torch/utils/utils.py | 165 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 165 insertions(+), 0 deletions(-)
diff --git a/funasr/runtime/python/libtorch/funasr_torch/utils/utils.py b/funasr/runtime/python/libtorch/funasr_torch/utils/utils.py
new file mode 100644
index 0000000..cafc43b
--- /dev/null
+++ b/funasr/runtime/python/libtorch/funasr_torch/utils/utils.py
@@ -0,0 +1,165 @@
+# -*- encoding: utf-8 -*-
+
+import functools
+import logging
+import pickle
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
+
+import numpy as np
+import yaml
+
+from typeguard import check_argument_types
+
+import warnings
+
+root_dir = Path(__file__).resolve().parent
+
+logger_initialized = {}
+
+
+class TokenIDConverter():
+ def __init__(self, token_list: Union[List, str],
+ ):
+ check_argument_types()
+
+ # self.token_list = self.load_token(token_path)
+ self.token_list = token_list
+ self.unk_symbol = token_list[-1]
+
+ def get_num_vocabulary_size(self) -> int:
+ return len(self.token_list)
+
+ def ids2tokens(self,
+ integers: Union[np.ndarray, Iterable[int]]) -> List[str]:
+ if isinstance(integers, np.ndarray) and integers.ndim != 1:
+ raise TokenIDConverterError(
+ f"Must be 1 dim ndarray, but got {integers.ndim}")
+ return [self.token_list[i] for i in integers]
+
+ def tokens2ids(self, tokens: Iterable[str]) -> List[int]:
+ token2id = {v: i for i, v in enumerate(self.token_list)}
+ if self.unk_symbol not in token2id:
+ raise TokenIDConverterError(
+ f"Unknown symbol '{self.unk_symbol}' doesn't exist in the token_list"
+ )
+ unk_id = token2id[self.unk_symbol]
+ return [token2id.get(i, unk_id) for i in tokens]
+
+
+class CharTokenizer():
+ def __init__(
+ self,
+ symbol_value: Union[Path, str, Iterable[str]] = None,
+ space_symbol: str = "<space>",
+ remove_non_linguistic_symbols: bool = False,
+ ):
+ check_argument_types()
+
+ self.space_symbol = space_symbol
+ self.non_linguistic_symbols = self.load_symbols(symbol_value)
+ self.remove_non_linguistic_symbols = remove_non_linguistic_symbols
+
+ @staticmethod
+ def load_symbols(value: Union[Path, str, Iterable[str]] = None) -> Set:
+ if value is None:
+ return set()
+
+ if isinstance(value, Iterable[str]):
+ return set(value)
+
+ file_path = Path(value)
+ if not file_path.exists():
+ logging.warning("%s doesn't exist.", file_path)
+ return set()
+
+ with file_path.open("r", encoding="utf-8") as f:
+ return set(line.rstrip() for line in f)
+
+ def text2tokens(self, line: Union[str, list]) -> List[str]:
+ tokens = []
+ while len(line) != 0:
+ for w in self.non_linguistic_symbols:
+ if line.startswith(w):
+ if not self.remove_non_linguistic_symbols:
+ tokens.append(line[: len(w)])
+ line = line[len(w):]
+ break
+ else:
+ t = line[0]
+ if t == " ":
+ t = "<space>"
+ tokens.append(t)
+ line = line[1:]
+ return tokens
+
+ def tokens2text(self, tokens: Iterable[str]) -> str:
+ tokens = [t if t != self.space_symbol else " " for t in tokens]
+ return "".join(tokens)
+
+ def __repr__(self):
+ return (
+ f"{self.__class__.__name__}("
+ f'space_symbol="{self.space_symbol}"'
+ f'non_linguistic_symbols="{self.non_linguistic_symbols}"'
+ f")"
+ )
+
+
+
+class Hypothesis(NamedTuple):
+ """Hypothesis data type."""
+
+ yseq: np.ndarray
+ score: Union[float, np.ndarray] = 0
+ scores: Dict[str, Union[float, np.ndarray]] = dict()
+ states: Dict[str, Any] = dict()
+
+ def asdict(self) -> dict:
+ """Convert data to JSON-friendly dict."""
+ return self._replace(
+ yseq=self.yseq.tolist(),
+ score=float(self.score),
+ scores={k: float(v) for k, v in self.scores.items()},
+ )._asdict()
+
+
+def read_yaml(yaml_path: Union[str, Path]) -> Dict:
+ if not Path(yaml_path).exists():
+ raise FileExistsError(f'The {yaml_path} does not exist.')
+
+ with open(str(yaml_path), 'rb') as f:
+ data = yaml.load(f, Loader=yaml.Loader)
+ return data
+
+
+@functools.lru_cache()
+def get_logger(name='funasr_torch'):
+ """Initialize and get a logger by name.
+ If the logger has not been initialized, this method will initialize the
+ logger by adding one or two handlers, otherwise the initialized logger will
+ be directly returned. During initialization, a StreamHandler will always be
+ added.
+ Args:
+ name (str): Logger name.
+ Returns:
+ logging.Logger: The expected logger.
+ """
+ logger = logging.getLogger(name)
+ if name in logger_initialized:
+ return logger
+
+ for logger_name in logger_initialized:
+ if name.startswith(logger_name):
+ return logger
+
+ formatter = logging.Formatter(
+ '[%(asctime)s] %(name)s %(levelname)s: %(message)s',
+ datefmt="%Y/%m/%d %H:%M:%S")
+
+ sh = logging.StreamHandler()
+ sh.setFormatter(formatter)
+ logger.addHandler(sh)
+ logger_initialized[name] = True
+ logger.propagate = False
+ return logger
--
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