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