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338 lines
12 KiB
338 lines
12 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import re
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from collections import OrderedDict
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from typing import List
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from typing import Optional
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from typing import Union
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import paddle
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import yaml
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from yacs.config import CfgNode
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from ..executor import BaseExecutor
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from ..log import logger
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from ..utils import stats_wrapper
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from paddlespeech.text.models.ernie_linear import ErnieLinear
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__all__ = ['TextExecutor']
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class TextExecutor(BaseExecutor):
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def __init__(self):
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super().__init__(task='text')
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self.parser = argparse.ArgumentParser(
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prog='paddlespeech.text', add_help=True)
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self.parser.add_argument(
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'--input', type=str, default=None, help='Input text.')
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self.parser.add_argument(
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'--task',
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type=str,
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default='punc',
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choices=['punc'],
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help='Choose text task.')
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self.parser.add_argument(
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'--model',
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type=str,
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default='ernie_linear_p7_wudao',
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choices=[
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tag[:tag.index('-')]
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for tag in self.task_resource.pretrained_models.keys()
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],
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help='Choose model type of text task.')
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self.parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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choices=['zh', 'en'],
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help='Choose model language.')
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self.parser.add_argument(
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'--config',
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type=str,
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default=None,
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help='Config of cls task. Use deault config when it is None.')
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self.parser.add_argument(
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'--ckpt_path',
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type=str,
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default=None,
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help='Checkpoint file of model.')
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self.parser.add_argument(
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'--punc_vocab',
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type=str,
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default=None,
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help='Vocabulary file of punctuation restoration task.')
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self.parser.add_argument(
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'--device',
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type=str,
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default=paddle.get_device(),
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help='Choose device to execute model inference.')
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self.parser.add_argument(
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'-d',
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'--job_dump_result',
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action='store_true',
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help='Save job result into file.')
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self.parser.add_argument(
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'-v',
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'--verbose',
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action='store_true',
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help='Increase logger verbosity of current task.')
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def _init_from_path(self,
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task: str='punc',
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model_type: str='ernie_linear_p7_wudao',
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lang: str='zh',
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cfg_path: Optional[os.PathLike]=None,
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ckpt_path: Optional[os.PathLike]=None,
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vocab_file: Optional[os.PathLike]=None):
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"""
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Init model and other resources from a specific path.
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"""
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if hasattr(self, 'model'):
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logger.debug('Model had been initialized.')
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return
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self.task = task
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if cfg_path is None or ckpt_path is None or vocab_file is None:
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tag = '-'.join([model_type, task, lang])
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self.task_resource.set_task_model(tag, version=None)
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self.cfg_path = os.path.join(
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self.task_resource.res_dir,
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self.task_resource.res_dict['cfg_path'])
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self.ckpt_path = os.path.join(
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self.task_resource.res_dir,
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self.task_resource.res_dict['ckpt_path'])
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self.vocab_file = os.path.join(
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self.task_resource.res_dir,
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self.task_resource.res_dict['vocab_file'])
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.ckpt_path = os.path.abspath(ckpt_path)
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self.vocab_file = os.path.abspath(vocab_file)
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model_name = model_type[:model_type.rindex('_')]
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if self.task == 'punc':
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# punc list
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self._punc_list = []
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with open(self.vocab_file, 'r', encoding='utf-8') as f:
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for line in f:
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self._punc_list.append(line.strip())
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# model
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model_class, tokenizer_class = self.task_resource.get_model_class(
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model_name)
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self.model = model_class(
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cfg_path=self.cfg_path, ckpt_path=self.ckpt_path)
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self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
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else:
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raise NotImplementedError
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self.model.eval()
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#init new models
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def _init_from_path_new(self,
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task: str='punc',
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model_type: str='ernie_linear_p7_wudao',
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lang: str='zh',
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cfg_path: Optional[os.PathLike]=None,
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ckpt_path: Optional[os.PathLike]=None,
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vocab_file: Optional[os.PathLike]=None):
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if hasattr(self, 'model'):
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logger.debug('Model had been initialized.')
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return
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self.task = task
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if cfg_path is None or ckpt_path is None or vocab_file is None:
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tag = '-'.join([model_type, task, lang])
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self.task_resource.set_task_model(tag, version=None)
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self.cfg_path = os.path.join(
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self.task_resource.res_dir,
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self.task_resource.res_dict['cfg_path'])
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self.ckpt_path = os.path.join(
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self.task_resource.res_dir,
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self.task_resource.res_dict['ckpt_path'])
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self.vocab_file = os.path.join(
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self.task_resource.res_dir,
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self.task_resource.res_dict['vocab_file'])
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.ckpt_path = os.path.abspath(ckpt_path)
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self.vocab_file = os.path.abspath(vocab_file)
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model_name = model_type[:model_type.rindex('_')]
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if self.task == 'punc':
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# punc list
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self._punc_list = []
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with open(self.vocab_file, 'r', encoding='utf-8') as f:
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for line in f:
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self._punc_list.append(line.strip())
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# model
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with open(self.cfg_path, 'r', encoding='utf-8') as f:
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config = CfgNode(yaml.safe_load(f))
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self.model = ErnieLinear(**config["model"])
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_, tokenizer_class = self.task_resource.get_model_class(model_name)
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state_dict = paddle.load(self.ckpt_path)
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self.model.set_state_dict(state_dict["main_params"])
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self.model.eval()
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#tokenizer: fast version: ernie-3.0-mini-zh slow version:ernie-1.0
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if 'fast' not in model_type:
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self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
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else:
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self.tokenizer = tokenizer_class.from_pretrained(
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'ernie-3.0-mini-zh')
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else:
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raise NotImplementedError
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def _clean_text(self, text):
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text = text.lower()
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text = re.sub('[^A-Za-z0-9\u4e00-\u9fa5]', '', text)
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text = re.sub(f'[{"".join([p for p in self._punc_list][1:])}]', '',
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text)
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return text
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def preprocess(self, text: Union[str, os.PathLike]):
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"""
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Input preprocess and return paddle.Tensor stored in self.input.
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Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
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"""
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if self.task == 'punc':
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clean_text = self._clean_text(text)
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assert len(clean_text) > 0, f'Invalid input string: {text}'
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tokenized_input = self.tokenizer(
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list(clean_text), return_length=True, is_split_into_words=True)
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self._inputs['input_ids'] = tokenized_input['input_ids']
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self._inputs['seg_ids'] = tokenized_input['token_type_ids']
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self._inputs['seq_len'] = tokenized_input['seq_len']
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else:
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raise NotImplementedError
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@paddle.no_grad()
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def infer(self):
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"""
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Model inference and result stored in self.output.
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"""
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if self.task == 'punc':
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input_ids = paddle.to_tensor(self._inputs['input_ids']).unsqueeze(0)
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seg_ids = paddle.to_tensor(self._inputs['seg_ids']).unsqueeze(0)
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logits, _ = self.model(input_ids, seg_ids)
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preds = paddle.argmax(logits, axis=-1).squeeze(0)
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self._outputs['preds'] = preds
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else:
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raise NotImplementedError
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def postprocess(self, isNewTrainer: bool=False) -> Union[str, os.PathLike]:
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"""
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Output postprocess and return human-readable results such as texts and audio files.
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"""
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if self.task == 'punc':
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input_ids = self._inputs['input_ids']
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seq_len = self._inputs['seq_len']
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preds = self._outputs['preds']
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tokens = self.tokenizer.convert_ids_to_tokens(
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input_ids[1:seq_len - 1])
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labels = preds[1:seq_len - 1].tolist()
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assert len(tokens) == len(labels)
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if isNewTrainer:
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self._punc_list = [0] + self._punc_list
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text = ''
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for t, l in zip(tokens, labels):
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text += t
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if l != 0: # Non punc.
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text += self._punc_list[l]
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return text
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else:
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raise NotImplementedError
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def execute(self, argv: List[str]) -> bool:
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"""
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Command line entry.
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"""
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parser_args = self.parser.parse_args(argv)
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task = parser_args.task
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model_type = parser_args.model
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lang = parser_args.lang
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cfg_path = parser_args.config
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ckpt_path = parser_args.ckpt_path
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punc_vocab = parser_args.punc_vocab
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device = parser_args.device
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if not parser_args.verbose:
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self.disable_task_loggers()
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task_source = self.get_input_source(parser_args.input)
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task_results = OrderedDict()
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has_exceptions = False
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for id_, input_ in task_source.items():
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try:
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res = self(input_, task, model_type, lang, cfg_path, ckpt_path,
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punc_vocab, device)
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task_results[id_] = res
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except Exception as e:
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has_exceptions = True
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task_results[id_] = f'{e.__class__.__name__}: {e}'
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self.process_task_results(parser_args.input, task_results,
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parser_args.job_dump_result)
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if has_exceptions:
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return False
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else:
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return True
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@stats_wrapper
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def __call__(
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self,
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text: str,
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task: str='punc',
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model: str='ernie_linear_p7_wudao',
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lang: str='zh',
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config: Optional[os.PathLike]=None,
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ckpt_path: Optional[os.PathLike]=None,
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punc_vocab: Optional[os.PathLike]=None,
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device: str=paddle.get_device(), ):
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"""
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Python API to call an executor.
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"""
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#Here is old version models
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if model in ['ernie_linear_p7_wudao', 'ernie_linear_p3_wudao']:
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paddle.set_device(device)
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self._init_from_path(task, model, lang, config, ckpt_path,
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punc_vocab)
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self.preprocess(text)
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self.infer()
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res = self.postprocess() # Retrieve result of text task.
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#Add new way to infer
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else:
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paddle.set_device(device)
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self._init_from_path_new(task, model, lang, config, ckpt_path,
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punc_vocab)
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self.preprocess(text)
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self.infer()
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res = self.postprocess(isNewTrainer=True)
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return res
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