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PaddleSpeech/examples/other/text_frontend/test_textnorm.py

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3.2 KiB

# 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
from parakeet.frontend.zh_normalization.text_normlization import TextNormalizer
from parakeet.utils.error_rate import char_errors
# 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()