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111 lines
3.5 KiB
111 lines
3.5 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 re
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import paddle
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import yaml
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from paddlenlp.transformers import ErnieTokenizer
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from yacs.config import CfgNode
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from paddlespeech.text.models.ernie_linear import ErnieLinear
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DefinedClassifier = {
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'ErnieLinear': ErnieLinear,
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}
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tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
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def _clean_text(text, punc_list):
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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 punc_list][1:])}]', '', text)
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return text
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def preprocess(text, punc_list):
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clean_text = _clean_text(text, punc_list)
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assert len(clean_text) > 0, f'Invalid input string: {text}'
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tokenized_input = tokenizer(
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list(clean_text), return_length=True, is_split_into_words=True)
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_inputs = dict()
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_inputs['input_ids'] = tokenized_input['input_ids']
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_inputs['seg_ids'] = tokenized_input['token_type_ids']
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_inputs['seq_len'] = tokenized_input['seq_len']
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return _inputs
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def test(args):
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with open(args.config) as f:
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config = CfgNode(yaml.safe_load(f))
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(config)
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punc_list = []
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with open(config["data_params"]["punc_path"], 'r') as f:
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for line in f:
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punc_list.append(line.strip())
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model = DefinedClassifier[config["model_type"]](**config["model"])
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state_dict = paddle.load(args.checkpoint)
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model.set_state_dict(state_dict["main_params"])
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model.eval()
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_inputs = preprocess(args.text, punc_list)
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seq_len = _inputs['seq_len']
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input_ids = paddle.to_tensor(_inputs['input_ids']).unsqueeze(0)
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seg_ids = paddle.to_tensor(_inputs['seg_ids']).unsqueeze(0)
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logits, _ = model(input_ids, seg_ids)
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preds = paddle.argmax(logits, axis=-1).squeeze(0)
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tokens = tokenizer.convert_ids_to_tokens(
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_inputs['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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# add 0 for non punc
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punc_list = [0] + 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 += punc_list[l]
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print("Punctuation Restoration Result:", text)
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return text
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(description="Run Punctuation Restoration.")
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parser.add_argument("--config", type=str, help="ErnieLinear config file.")
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parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
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parser.add_argument("--text", type=str, help="raw text to be restored.")
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parser.add_argument(
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"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
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args = parser.parse_args()
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if args.ngpu == 0:
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paddle.set_device("cpu")
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elif args.ngpu > 0:
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paddle.set_device("gpu")
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else:
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print("ngpu should >= 0 !")
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test(args)
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if __name__ == "__main__":
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main()
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