From 0f3571db844432bba5ee9fcfb0260c6bdd1e5a6d Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 16 五月 2023 09:40:10 +0800
Subject: [PATCH] inference

---
 funasr/bin/punc_inference_launch.py |  170 +++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 files changed, 165 insertions(+), 5 deletions(-)

diff --git a/funasr/bin/punc_inference_launch.py b/funasr/bin/punc_inference_launch.py
index b1d9235..594a7be 100755
--- a/funasr/bin/punc_inference_launch.py
+++ b/funasr/bin/punc_inference_launch.py
@@ -14,6 +14,166 @@
 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
+
+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.torch_utils.set_all_random_seed import set_all_random_seed
+from funasr.utils import config_argparse
+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,
+):
+    assert check_argument_types()
+    logging.basicConfig(
+        level=log_level,
+        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+    )
+
+    if ngpu >= 1 and torch.cuda.is_available():
+        device = "cuda"
+    else:
+        device = "cpu"
+
+    # 1. Set random-seed
+    set_all_random_seed(seed)
+    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,
+    ):
+        results = []
+        split_size = 20
+
+        if raw_inputs != None:
+            line = raw_inputs.strip()
+            key = "demo"
+            if line == "":
+                item = {'key': key, 'value': ""}
+                results.append(item)
+                return results
+            result, _ = text2punc(line)
+            item = {'key': key, 'value': result}
+            results.append(item)
+            return results
+
+        for inference_text, _, _ in data_path_and_name_and_type:
+            with open(inference_text, "r", encoding="utf-8") as fin:
+                for line in fin:
+                    line = line.strip()
+                    segs = line.split("\t")
+                    if len(segs) != 2:
+                        continue
+                    key = segs[0]
+                    if len(segs[1]) == 0:
+                        continue
+                    result, _ = text2punc(segs[1])
+                    item = {'key': key, 'value': result}
+                    results.append(item)
+        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+        if output_path != None:
+            output_file_name = "infer.out"
+            Path(output_path).mkdir(parents=True, exist_ok=True)
+            output_file_path = (Path(output_path) / output_file_name).absolute()
+            with open(output_file_path, "w", encoding="utf-8") as fout:
+                for item_i in results:
+                    key_out = item_i["key"]
+                    value_out = item_i["value"]
+                    fout.write(f"{key_out}\t{value_out}\n")
+        return results
+
+    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,
+):
+    assert check_argument_types()
+    ncpu = kwargs.get("ncpu", 1)
+    torch.set_num_threads(ncpu)
+
+    if ngpu >= 1 and torch.cuda.is_available():
+        device = "cuda"
+    else:
+        device = "cpu"
+
+    # 1. Set random-seed
+    set_all_random_seed(seed)
+    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,
+    ):
+        results = []
+        split_size = 10
+        cache_in = param_dict["cache"]
+        if raw_inputs != None:
+            line = raw_inputs.strip()
+            key = "demo"
+            if line == "":
+                item = {'key': key, 'value': ""}
+                results.append(item)
+                return results
+            result, _, cache = text2punc(line, cache_in)
+            param_dict["cache"] = cache
+            item = {'key': key, 'value': result}
+            results.append(item)
+            return results
+
+        return results
+
+    return _forward
+
 
 def get_parser():
     parser = config_argparse.ArgumentParser(
@@ -72,11 +232,9 @@
 
 def inference_launch(mode, **kwargs):
     if mode == "punc":
-        from funasr.bin.punctuation_infer import inference_modelscope
-        return inference_modelscope(**kwargs)
+        return inference_punc(**kwargs)
     if mode == "punc_VadRealtime":
-        from funasr.bin.punctuation_infer_vadrealtime import inference_modelscope
-        return inference_modelscope(**kwargs)
+        return inference_punc_vad_realtime(**kwargs)
     else:
         logging.info("Unknown decoding mode: {}".format(mode))
         return None
@@ -105,7 +263,9 @@
 
     kwargs.pop("gpuid_list", None)
     kwargs.pop("njob", None)
-    results = inference_launch(**kwargs)
+    inference_pipeline = inference_launch(**kwargs)
+    return inference_pipeline(kwargs["data_path_and_name_and_type"])
+
 
 
 if __name__ == "__main__":

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