mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
275 lines
8.5 KiB
Python
Executable File
275 lines
8.5 KiB
Python
Executable File
# -*- encoding: utf-8 -*-
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#!/usr/bin/env python3
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import argparse
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import logging
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import os
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import sys
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from typing import Union, Dict, Any
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.utils.types import float_or_none
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import argparse
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import logging
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from pathlib import Path
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import sys
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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from typing import Any
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from typing import List
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import numpy as np
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import torch
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from typeguard import check_argument_types
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from funasr.datasets.preprocessor import CodeMixTokenizerCommonPreprocessor
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.tasks.punctuation import PunctuationTask
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.forward_adaptor import ForwardAdaptor
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.datasets.preprocessor import split_to_mini_sentence
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from funasr.bin.punc_infer import Text2Punc, Text2PuncVADRealtime
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def inference_punc(
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batch_size: int,
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dtype: str,
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ngpu: int,
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seed: int,
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num_workers: int,
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log_level: Union[int, str],
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key_file: Optional[str],
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train_config: Optional[str],
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model_file: Optional[str],
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output_dir: Optional[str] = None,
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param_dict: dict = None,
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**kwargs,
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):
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assert check_argument_types()
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logging.basicConfig(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# 1. Set random-seed
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set_all_random_seed(seed)
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text2punc = Text2Punc(train_config, model_file, device)
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def _forward(
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data_path_and_name_and_type,
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raw_inputs: Union[List[Any], bytes, str] = None,
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output_dir_v2: Optional[str] = None,
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cache: List[Any] = None,
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param_dict: dict = None,
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):
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results = []
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split_size = 20
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if raw_inputs != None:
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line = raw_inputs.strip()
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key = "demo"
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if line == "":
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item = {'key': key, 'value': ""}
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results.append(item)
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return results
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result, _ = text2punc(line)
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item = {'key': key, 'value': result}
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results.append(item)
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return results
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for inference_text, _, _ in data_path_and_name_and_type:
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with open(inference_text, "r", encoding="utf-8") as fin:
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for line in fin:
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line = line.strip()
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segs = line.split("\t")
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if len(segs) != 2:
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continue
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key = segs[0]
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if len(segs[1]) == 0:
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continue
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result, _ = text2punc(segs[1])
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item = {'key': key, 'value': result}
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results.append(item)
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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if output_path != None:
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output_file_name = "infer.out"
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Path(output_path).mkdir(parents=True, exist_ok=True)
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output_file_path = (Path(output_path) / output_file_name).absolute()
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with open(output_file_path, "w", encoding="utf-8") as fout:
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for item_i in results:
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key_out = item_i["key"]
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value_out = item_i["value"]
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fout.write(f"{key_out}\t{value_out}\n")
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return results
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return _forward
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def inference_punc_vad_realtime(
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batch_size: int,
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dtype: str,
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ngpu: int,
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seed: int,
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num_workers: int,
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log_level: Union[int, str],
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#cache: list,
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key_file: Optional[str],
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train_config: Optional[str],
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model_file: Optional[str],
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output_dir: Optional[str] = None,
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param_dict: dict = None,
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**kwargs,
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):
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assert check_argument_types()
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ncpu = kwargs.get("ncpu", 1)
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torch.set_num_threads(ncpu)
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# 1. Set random-seed
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set_all_random_seed(seed)
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text2punc = Text2PuncVADRealtime(train_config, model_file, device)
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def _forward(
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data_path_and_name_and_type,
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raw_inputs: Union[List[Any], bytes, str] = None,
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output_dir_v2: Optional[str] = None,
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cache: List[Any] = None,
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param_dict: dict = None,
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):
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results = []
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split_size = 10
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cache_in = param_dict["cache"]
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if raw_inputs != None:
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line = raw_inputs.strip()
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key = "demo"
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if line == "":
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item = {'key': key, 'value': ""}
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results.append(item)
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return results
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result, _, cache = text2punc(line, cache_in)
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param_dict["cache"] = cache
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item = {'key': key, 'value': result}
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results.append(item)
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return results
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return results
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return _forward
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def inference_launch(mode, **kwargs):
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if mode == "punc":
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return inference_punc(**kwargs)
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if mode == "punc_VadRealtime":
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return inference_punc_vad_realtime(**kwargs)
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else:
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logging.info("Unknown decoding mode: {}".format(mode))
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return None
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="Punctuation inference",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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"--log_level",
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type=lambda x: x.upper(),
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default="INFO",
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choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
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help="The verbose level of logging",
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)
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parser.add_argument("--output_dir", type=str, required=True)
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parser.add_argument("--gpuid_list", type=str, required=True)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=0,
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help="The number of gpus. 0 indicates CPU mode",
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)
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument("--njob", type=int, default=1, help="Random seed")
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parser.add_argument(
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"--dtype",
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default="float32",
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choices=["float16", "float32", "float64"],
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help="Data type",
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)
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parser.add_argument(
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"--num_workers",
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type=int,
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default=1,
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help="The number of workers used for DataLoader",
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)
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parser.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="The batch size for inference",
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)
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group = parser.add_argument_group("Input data related")
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group.add_argument("--data_path_and_name_and_type", type=str2triple_str, action="append", required=False)
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group.add_argument("--raw_inputs", type=str, required=False)
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group.add_argument("--key_file", type=str_or_none)
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group.add_argument("--cache", type=list, required=False)
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group.add_argument("--param_dict", type=dict, required=False)
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group = parser.add_argument_group("The model configuration related")
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group.add_argument("--train_config", type=str)
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group.add_argument("--model_file", type=str)
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group.add_argument("--mode", type=str, default="punc")
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return parser
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def main(cmd=None):
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print(get_commandline_args(), file=sys.stderr)
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parser = get_parser()
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args = parser.parse_args(cmd)
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kwargs = vars(args)
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kwargs.pop("config", None)
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# set logging messages
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logging.basicConfig(
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level=args.log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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logging.info("Decoding args: {}".format(kwargs))
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# gpu setting
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if args.ngpu > 0:
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jobid = int(args.output_dir.split(".")[-1])
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gpuid = args.gpuid_list.split(",")[(jobid - 1) // args.njob]
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
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kwargs.pop("gpuid_list", None)
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kwargs.pop("njob", None)
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inference_pipeline = inference_launch(**kwargs)
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return inference_pipeline(kwargs["data_path_and_name_and_type"])
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if __name__ == "__main__":
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main()
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