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295 lines
10 KiB
295 lines
10 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 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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from ...s2t.utils.dynamic_import import dynamic_import
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from ..executor import BaseExecutor
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from ..log import logger
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from ..utils import cli_register
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from ..utils import download_and_decompress
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from ..utils import MODEL_HOME
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__all__ = ['TextExecutor']
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pretrained_models = {
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# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
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# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
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# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
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# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
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"ernie_linear_p7_wudao-punc-zh": {
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'url':
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'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz',
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'md5':
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'12283e2ddde1797c5d1e57036b512746',
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'cfg_path':
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'ckpt/model_config.json',
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'ckpt_path':
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'ckpt/model_state.pdparams',
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'vocab_file':
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'punc_vocab.txt',
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},
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"ernie_linear_p3_wudao-punc-zh": {
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'url':
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'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz',
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'md5':
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'448eb2fdf85b6a997e7e652e80c51dd2',
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'cfg_path':
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'ckpt/model_config.json',
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'ckpt_path':
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'ckpt/model_state.pdparams',
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'vocab_file':
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'punc_vocab.txt',
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},
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}
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model_alias = {
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"ernie_linear_p7": "paddlespeech.text.models:ErnieLinear",
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"ernie_linear_p3": "paddlespeech.text.models:ErnieLinear",
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}
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tokenizer_alias = {
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"ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer",
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"ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer",
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}
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@cli_register(name='paddlespeech.text', description='Text infer command.')
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class TextExecutor(BaseExecutor):
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def __init__(self):
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super(TextExecutor, self).__init__()
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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, required=True, 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=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
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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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def _get_pretrained_path(self, tag: str) -> os.PathLike:
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"""
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Download and returns pretrained resources path of current task.
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"""
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assert tag in pretrained_models, 'Can not find pretrained resources of {}.'.format(
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tag)
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res_path = os.path.join(MODEL_HOME, tag)
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decompressed_path = download_and_decompress(pretrained_models[tag],
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res_path)
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decompressed_path = os.path.abspath(decompressed_path)
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logger.info(
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'Use pretrained model stored in: {}'.format(decompressed_path))
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return decompressed_path
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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.info('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.res_path = self._get_pretrained_path(tag)
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self.cfg_path = os.path.join(self.res_path,
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pretrained_models[tag]['cfg_path'])
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self.ckpt_path = os.path.join(self.res_path,
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pretrained_models[tag]['ckpt_path'])
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self.vocab_file = os.path.join(self.res_path,
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pretrained_models[tag]['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') 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 = dynamic_import(model_name, model_alias)
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tokenizer_class = dynamic_import(model_name, tokenizer_alias)
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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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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) -> 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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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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text = parser_args.input
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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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try:
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res = self(text, task, model_type, lang, cfg_path, ckpt_path,
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punc_vocab, device)
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logger.info('Text Result:\n{}'.format(res))
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return True
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except Exception as e:
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logger.exception(e)
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return False
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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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paddle.set_device(device)
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self._init_from_path(task, model, lang, config, ckpt_path, 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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return res
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