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PaddleSpeech/paddlespeech/text/exps/ernie_linear/punc_restore.py

116 lines
3.7 KiB

3 years ago
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import re
import paddle
import yaml
from paddlenlp.transformers import ErnieTokenizer
from yacs.config import CfgNode
from paddlespeech.text.models.ernie_linear import ErnieLinear
DefinedClassifier = {
'ErnieLinear': ErnieLinear,
}
def _clean_text(text, punc_list):
text = text.lower()
text = re.sub('[^A-Za-z0-9\u4e00-\u9fa5]', '', text)
text = re.sub(f'[{"".join([p for p in punc_list][1:])}]', '', text)
return text
def preprocess(text, punc_list, tokenizer):
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clean_text = _clean_text(text, punc_list)
assert len(clean_text) > 0, f'Invalid input string: {text}'
tokenized_input = tokenizer(
list(clean_text), return_length=True, is_split_into_words=True)
_inputs = dict()
_inputs['input_ids'] = tokenized_input['input_ids']
_inputs['seg_ids'] = tokenized_input['token_type_ids']
_inputs['seq_len'] = tokenized_input['seq_len']
return _inputs
def test(args):
with open(args.config) as f:
config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args), allow_unicode=True))
# print(args)
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print("========Config========")
print(config)
punc_list = []
with open(config["data_params"]["punc_path"], 'r') as f:
for line in f:
punc_list.append(line.strip())
model = DefinedClassifier[config["model_type"]](**config["model"])
# print(model)
pretrained_token = config['data_params']['pretrained_token']
tokenizer = ErnieTokenizer.from_pretrained(pretrained_token)
# tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
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state_dict = paddle.load(args.checkpoint)
model.set_state_dict(state_dict["main_params"])
model.eval()
_inputs = preprocess(args.text, punc_list, tokenizer)
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seq_len = _inputs['seq_len']
input_ids = paddle.to_tensor(_inputs['input_ids']).unsqueeze(0)
seg_ids = paddle.to_tensor(_inputs['seg_ids']).unsqueeze(0)
logits, _ = model(input_ids, seg_ids)
preds = paddle.argmax(logits, axis=-1).squeeze(0)
tokens = tokenizer.convert_ids_to_tokens(
_inputs['input_ids'][1:seq_len - 1])
labels = preds[1:seq_len - 1].tolist()
assert len(tokens) == len(labels)
# add 0 for non punc
punc_list = [0] + punc_list
text = ''
for t, l in zip(tokens, labels):
text += t
if l != 0: # Non punc.
text += punc_list[l]
print("Punctuation Restoration Result:", text)
return text
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(description="Run Punctuation Restoration.")
parser.add_argument("--config", type=str, help="ErnieLinear config file.")
parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
parser.add_argument("--text", type=str, help="raw text to be restored.")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
args = parser.parse_args()
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
test(args)
if __name__ == "__main__":
main()