You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
97 lines
3.3 KiB
97 lines
3.3 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
|
||
|
|
from pathlib import Path
|
||
|
|
|
||
|
3 years ago
|
from paddlespeech.t2s.frontend.zh_normalization.text_normlization import TextNormalizer
|
||
|
|
from paddlespeech.t2s.utils.error_rate import char_errors
|
||
|
3 years ago
|
|
||
|
|
|
||
|
|
# delete english characters
|
||
|
|
# e.g. "你好aBC" -> "你 好"
|
||
|
|
def del_en_add_space(input: str):
|
||
|
|
output = re.sub('[a-zA-Z]', '', input)
|
||
|
|
output = [char + " " for char in output]
|
||
|
|
output = "".join(output).strip()
|
||
|
|
return output
|
||
|
|
|
||
|
|
|
||
|
|
def get_avg_cer(raw_dict, ref_dict, text_normalizer, output_dir):
|
||
|
|
edit_distances = []
|
||
|
|
ref_lens = []
|
||
|
|
wf_ref = open(output_dir / "text.ref.clean", "w")
|
||
|
|
wf_tn = open(output_dir / "text.tn", "w")
|
||
|
|
for text_id in raw_dict:
|
||
|
|
if text_id not in ref_dict:
|
||
|
|
continue
|
||
|
|
raw_text = raw_dict[text_id]
|
||
|
|
gt_text = ref_dict[text_id]
|
||
|
|
textnorm_text = text_normalizer.normalize_sentence(raw_text)
|
||
|
|
|
||
|
|
gt_text = del_en_add_space(gt_text)
|
||
|
|
textnorm_text = del_en_add_space(textnorm_text)
|
||
|
|
wf_ref.write(gt_text + "(" + text_id + ")" + "\n")
|
||
|
|
wf_tn.write(textnorm_text + "(" + text_id + ")" + "\n")
|
||
|
|
edit_distance, ref_len = char_errors(gt_text, textnorm_text)
|
||
|
|
edit_distances.append(edit_distance)
|
||
|
|
ref_lens.append(ref_len)
|
||
|
|
|
||
|
|
return sum(edit_distances) / sum(ref_lens)
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
parser = argparse.ArgumentParser(description="text normalization example.")
|
||
|
|
parser.add_argument(
|
||
|
|
"--input-dir",
|
||
|
|
default="data/textnorm",
|
||
|
|
type=str,
|
||
|
|
help="directory to preprocessed test data.")
|
||
|
|
parser.add_argument(
|
||
|
|
"--output-dir",
|
||
|
|
default="exp/textnorm",
|
||
|
|
type=str,
|
||
|
|
help="directory to save textnorm results.")
|
||
|
|
|
||
|
|
args = parser.parse_args()
|
||
|
|
input_dir = Path(args.input_dir).expanduser()
|
||
|
|
output_dir = Path(args.output_dir).expanduser()
|
||
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
||
|
|
assert input_dir.is_dir()
|
||
|
|
raw_dict, ref_dict = dict(), dict()
|
||
|
|
raw_path = input_dir / "text"
|
||
|
|
ref_path = input_dir / "text.ref"
|
||
|
|
|
||
|
|
with open(raw_path, "r") as rf:
|
||
|
|
for line in rf:
|
||
|
|
line = line.strip()
|
||
|
|
line_list = line.split(" ")
|
||
|
|
text_id, raw_text = line_list[0], " ".join(line_list[1:])
|
||
|
|
raw_dict[text_id] = raw_text
|
||
|
|
with open(ref_path, "r") as rf:
|
||
|
|
for line in rf:
|
||
|
|
line = line.strip()
|
||
|
|
line_list = line.split(" ")
|
||
|
|
text_id, normed_text = line_list[0], " ".join(line_list[1:])
|
||
|
|
ref_dict[text_id] = normed_text
|
||
|
|
|
||
|
|
text_normalizer = TextNormalizer()
|
||
|
|
|
||
|
|
avg_cer = get_avg_cer(raw_dict, ref_dict, text_normalizer, output_dir)
|
||
|
|
print("The avg CER of text normalization is:", avg_cer)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|