100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
# 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 parakeet.frontend.cn_frontend import Frontend as cnFrontend
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from parakeet.utils.error_rate import word_errors
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SILENCE_TOKENS = {"sp", "sil", "sp1", "spl"}
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def text_cleaner(raw_text):
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text = re.sub('#[1-4]|“|”|（|）', '', raw_text)
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text = text.replace("…。", "。")
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text = re.sub('：|；|——|……|、|…|—', '，', text)
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return text
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def get_avg_wer(raw_dict, ref_dict, frontend, output_dir):
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edit_distances = []
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ref_lens = []
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wf_g2p = open(output_dir / "text.g2p", "w")
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wf_ref = open(output_dir / "text.ref.clean", "w")
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for utt_id in raw_dict:
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if utt_id not in ref_dict:
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continue
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raw_text = raw_dict[utt_id]
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text = text_cleaner(raw_text)
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g2p_phones = frontend.get_phonemes(text)
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g2p_phones = sum(g2p_phones, [])
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gt_phones = ref_dict[utt_id].split(" ")
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# delete silence tokens in predicted phones and ground truth phones
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g2p_phones = [phn for phn in g2p_phones if phn not in SILENCE_TOKENS]
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gt_phones = [phn for phn in gt_phones if phn not in SILENCE_TOKENS]
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gt_phones = " ".join(gt_phones)
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g2p_phones = " ".join(g2p_phones)
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wf_ref.write(gt_phones + "(baker_" + utt_id + ")" + "\n")
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wf_g2p.write(g2p_phones + "(baker_" + utt_id + ")" + "\n")
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edit_distance, ref_len = word_errors(gt_phones, g2p_phones)
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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="g2p example.")
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parser.add_argument(
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"--input-dir",
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default="data/g2p",
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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/g2p",
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type=str,
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help="directory to save g2p 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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utt_id, raw_text = line_list[0], " ".join(line_list[1:])
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raw_dict[utt_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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utt_id, phones = line_list[0], " ".join(line_list[1:])
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ref_dict[utt_id] = phones
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frontend = cnFrontend()
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avg_wer = get_avg_wer(raw_dict, ref_dict, frontend, output_dir)
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print("The avg WER of g2p is:", avg_wer)
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if __name__ == "__main__":
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main()
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