From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

---
 funasr/bin/punc_inference_launch.py |  128 ++++++++++++++++++------------------------
 1 files changed, 54 insertions(+), 74 deletions(-)

diff --git a/funasr/bin/punc_inference_launch.py b/funasr/bin/punc_inference_launch.py
index 594a7be..5d917f5 100755
--- a/funasr/bin/punc_inference_launch.py
+++ b/funasr/bin/punc_inference_launch.py
@@ -1,61 +1,42 @@
 #!/usr/bin/env python3
-
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
 
 import argparse
 import logging
 import os
 import sys
-from typing import Union, Dict, Any
-
-from funasr.utils import config_argparse
-from funasr.utils.cli_utils import get_commandline_args
-from funasr.utils.types import str2bool
-from funasr.utils.types import str2triple_str
-from funasr.utils.types import str_or_none
-from funasr.utils.types import float_or_none
-
-import argparse
-import logging
 from pathlib import Path
-import sys
-from typing import Optional
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 from typing import Any
 from typing import List
+from typing import Optional
+from typing import Union
 
-import numpy as np
 import torch
-from typeguard import check_argument_types
 
-from funasr.datasets.preprocessor import CodeMixTokenizerCommonPreprocessor
-from funasr.utils.cli_utils import get_commandline_args
-from funasr.tasks.punctuation import PunctuationTask
-from funasr.torch_utils.device_funcs import to_device
-from funasr.torch_utils.forward_adaptor import ForwardAdaptor
+from funasr.bin.punc_infer import Text2Punc, Text2PuncVADRealtime
 from funasr.torch_utils.set_all_random_seed import set_all_random_seed
 from funasr.utils import config_argparse
+from funasr.utils.cli_utils import get_commandline_args
 from funasr.utils.types import str2triple_str
 from funasr.utils.types import str_or_none
-from funasr.datasets.preprocessor import split_to_mini_sentence
-from funasr.bin.punc_infer import Text2Punc, Text2PuncVADRealtime
+
 
 def inference_punc(
-    batch_size: int,
-    dtype: str,
-    ngpu: int,
-    seed: int,
-    num_workers: int,
-    log_level: Union[int, str],
-    key_file: Optional[str],
-    train_config: Optional[str],
-    model_file: Optional[str],
-    output_dir: Optional[str] = None,
-    param_dict: dict = None,
-    **kwargs,
+        batch_size: int,
+        dtype: str,
+        ngpu: int,
+        seed: int,
+        num_workers: int,
+        log_level: Union[int, str],
+        key_file: Optional[str],
+        train_config: Optional[str],
+        model_file: Optional[str],
+        output_dir: Optional[str] = None,
+        param_dict: dict = None,
+        **kwargs,
 ):
-    assert check_argument_types()
     logging.basicConfig(
         level=log_level,
         format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
@@ -71,11 +52,11 @@
     text2punc = Text2Punc(train_config, model_file, device)
 
     def _forward(
-        data_path_and_name_and_type,
-        raw_inputs: Union[List[Any], bytes, str] = None,
-        output_dir_v2: Optional[str] = None,
-        cache: List[Any] = None,
-        param_dict: dict = None,
+            data_path_and_name_and_type,
+            raw_inputs: Union[List[Any], bytes, str] = None,
+            output_dir_v2: Optional[str] = None,
+            cache: List[Any] = None,
+            param_dict: dict = None,
     ):
         results = []
         split_size = 20
@@ -119,22 +100,22 @@
 
     return _forward
 
+
 def inference_punc_vad_realtime(
-    batch_size: int,
-    dtype: str,
-    ngpu: int,
-    seed: int,
-    num_workers: int,
-    log_level: Union[int, str],
-    #cache: list,
-    key_file: Optional[str],
-    train_config: Optional[str],
-    model_file: Optional[str],
-    output_dir: Optional[str] = None,
-    param_dict: dict = None,
-    **kwargs,
+        batch_size: int,
+        dtype: str,
+        ngpu: int,
+        seed: int,
+        num_workers: int,
+        log_level: Union[int, str],
+        # cache: list,
+        key_file: Optional[str],
+        train_config: Optional[str],
+        model_file: Optional[str],
+        output_dir: Optional[str] = None,
+        param_dict: dict = None,
+        **kwargs,
 ):
-    assert check_argument_types()
     ncpu = kwargs.get("ncpu", 1)
     torch.set_num_threads(ncpu)
 
@@ -148,11 +129,11 @@
     text2punc = Text2PuncVADRealtime(train_config, model_file, device)
 
     def _forward(
-        data_path_and_name_and_type,
-        raw_inputs: Union[List[Any], bytes, str] = None,
-        output_dir_v2: Optional[str] = None,
-        cache: List[Any] = None,
-        param_dict: dict = None,
+            data_path_and_name_and_type,
+            raw_inputs: Union[List[Any], bytes, str] = None,
+            output_dir_v2: Optional[str] = None,
+            cache: List[Any] = None,
+            param_dict: dict = None,
     ):
         results = []
         split_size = 10
@@ -173,6 +154,16 @@
         return results
 
     return _forward
+
+
+def inference_launch(mode, **kwargs):
+    if mode == "punc":
+        return inference_punc(**kwargs)
+    if mode == "punc_VadRealtime":
+        return inference_punc_vad_realtime(**kwargs)
+    else:
+        logging.info("Unknown decoding mode: {}".format(mode))
+        return None
 
 
 def get_parser():
@@ -230,16 +221,6 @@
     return parser
 
 
-def inference_launch(mode, **kwargs):
-    if mode == "punc":
-        return inference_punc(**kwargs)
-    if mode == "punc_VadRealtime":
-        return inference_punc_vad_realtime(**kwargs)
-    else:
-        logging.info("Unknown decoding mode: {}".format(mode))
-        return None
-
-
 def main(cmd=None):
     print(get_commandline_args(), file=sys.stderr)
     parser = get_parser()
@@ -265,7 +246,6 @@
     kwargs.pop("njob", None)
     inference_pipeline = inference_launch(**kwargs)
     return inference_pipeline(kwargs["data_path_and_name_and_type"])
-
 
 
 if __name__ == "__main__":

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