Merge pull request #126 from PaddlePaddle/revert-124-use_mfa
Revert "add use_mfa example"
This commit is contained in:
Feiyu Chan
GitHub
commit
9ffdd7abd8
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# 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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from collections import OrderedDict
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from pathlib import Path
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import logging
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def detect_oov(corpus_dir, lexicon_path, transcription_pattern="*.lab"):
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corpus_dir = Path(corpus_dir)
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lexicon = OrderedDict()
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with open(lexicon_path, 'rt') as f:
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for line in f:
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syllable, phonemes = line.split(maxsplit=1)
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lexicon[syllable] = phonemes
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for fp in corpus_dir.glob(transcription_pattern):
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syllables = fp.read_text().strip().split()
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for s in syllables:
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if s not in lexicon:
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logging.warning(f"{fp.relative_to(corpus_dir)} has OOV {s} .")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="detect oov in a corpus given a lexicon")
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parser.add_argument(
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"corpus_dir", type=str, help="corpus dir for MFA alignment.")
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parser.add_argument("lexicon_path", type=str, help="dictionary to use.")
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parser.add_argument(
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"--pattern", type=str, default="*.lab", help="dictionary to use.")
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args = parser.parse_args()
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print(args)
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detect_oov(args.corpus_dir, args.lexicon_path, args.pattern)
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# 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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"""Generate lexicon and symbols for Mandarin Chinese phonology.
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The lexicon is used for Montreal Force Aligner.
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Note that syllables are used as word in this lexicon. Since syllables rather
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than words are used in transcriptions produced by `reorganize_baker.py`.
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We make this choice to better leverage other software for chinese text to
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pinyin tools like pypinyin. This is the convention for G2P in Chinese.
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"""
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import re
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import argparse
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from collections import OrderedDict
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INITIALS = [
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'b', 'p', 'm', 'f', 'd', 't', 'n', 'l', 'g', 'k', 'h', 'zh', 'ch', 'sh',
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'r', 'z', 'c', 's', 'j', 'q', 'x'
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]
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FINALS = [
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'a', 'ai', 'ao', 'an', 'ang', 'e', 'er', 'ei', 'en', 'eng', 'o', 'ou',
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'ong', 'ii', 'iii', 'i', 'ia', 'iao', 'ian', 'iang', 'ie', 'io', 'iou',
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'iong', 'in', 'ing', 'u', 'ua', 'uai', 'uan', 'uang', 'uei', 'uo', 'uen',
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'ueng', 'v', 've', 'van', 'vn'
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]
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SPECIALS = ['sil', 'sp']
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def rule(C, V, R, T):
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"""Generate a syllable given the initial, the final, erhua indicator, and tone.
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Orthographical rules for pinyin are applied. (special case for y, w, ui, un, iu)
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Note that in this system, 'ü' is alway written as 'v' when appeared in phoneme, but converted to
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'u' in syllables when certain conditions are satisfied.
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'i' is distinguished when appeared in phonemes, and separated into 3 categories, 'i', 'ii' and 'iii'.
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Erhua is is possibly applied to every finals, except for finals that already ends with 'r'.
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When a syllable is impossible or does not have any characters with this pronunciation, return None
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to filter it out.
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"""
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# 不可拼的音节, ii 只能和 z, c, s 拼
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if V in ["ii"] and (C not in ['z', 'c', 's']):
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return None
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# iii 只能和 zh, ch, sh, r 拼
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if V in ['iii'] and (C not in ['zh', 'ch', 'sh', 'r']):
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return None
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# 齐齿呼或者撮口呼不能和 f, g, k, h, zh, ch, sh, r, z, c, s
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if (V not in ['ii', 'iii']) and V[0] in ['i', 'v'] and (
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C in ['f', 'g', 'k', 'h', 'zh', 'ch', 'sh', 'r', 'z', 'c', 's']):
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return None
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# 撮口呼只能和 j, q, x l, n 拼
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if V.startswith("v"):
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# v, ve 只能和 j ,q , x, n, l 拼
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if V in ['v', 've']:
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if C not in ['j', 'q', 'x', 'n', 'l', '']:
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return None
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# 其他只能和 j, q, x 拼
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else:
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if C not in ['j', 'q', 'x', '']:
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return None
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# j, q, x 只能和齐齿呼或者撮口呼拼
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if (C in ['j', 'q', 'x']) and not (
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(V not in ['ii', 'iii']) and V[0] in ['i', 'v']):
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return None
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# b, p ,m, f 不能和合口呼拼，除了 u 之外
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# bm p, m, f 不能和撮口呼拼
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if (C in ['b', 'p', 'm', 'f']) and ((V[0] in ['u', 'v'] and V != "u") or
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V == 'ong'):
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return None
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# ua, uai, uang 不能和 d, t, n, l, r, z, c, s 拼
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if V in ['ua', 'uai', 'uang'
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] and C in ['d', 't', 'n', 'l', 'r', 'z', 'c', 's']:
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return None
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# sh 和 ong 不能拼
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if V == 'ong' and C in ['sh']:
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return None
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# o 和 gkh, zh ch sh r z c s 不能拼
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if V == "o" and C in [
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'd', 't', 'n', 'g', 'k', 'h', 'zh', 'ch', 'sh', 'r', 'z', 'c', 's'
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]:
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return None
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# ueng 只是 weng 这个 ad-hoc 其他情况下都是 ong
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if V == 'ueng' and C != '':
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return
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# 非儿化的 er 只能单独存在
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if V == 'er' and C != '':
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return None
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if C == '':
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if V in ["i", "in", "ing"]:
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C = 'y'
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elif V == 'u':
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C = 'w'
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elif V.startswith('i') and V not in ["ii", "iii"]:
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C = 'y'
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V = V[1:]
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elif V.startswith('u'):
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C = 'w'
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V = V[1:]
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elif V.startswith('v'):
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C = 'yu'
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V = V[1:]
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else:
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if C in ['j', 'q', 'x']:
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if V.startswith('v'):
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V = re.sub('v', 'u', V)
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if V == 'iou':
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V = 'iu'
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elif V == 'uei':
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V = 'ui'
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elif V == 'uen':
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V = 'un'
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result = C + V
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# Filter er 不能再儿化
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if result.endswith('r') and R == 'r':
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return None
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# ii and iii, change back to i
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result = re.sub(r'i+', 'i', result)
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result = result + R + T
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return result
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def generate_lexicon(with_tone=False, with_erhua=False):
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"""Generate lexicon for Mandarin Chinese."""
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syllables = OrderedDict()
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for C in [''] + INITIALS:
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for V in FINALS:
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for R in [''] if not with_erhua else ['', 'r']:
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for T in [''] if not with_tone else ['1', '2', '3', '4', '5']:
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result = rule(C, V, R, T)
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if result:
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syllables[result] = f'{C} {V}{R}{T}'
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return syllables
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def generate_symbols(lexicon):
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"""Generate phoneme list for a lexicon."""
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symbols = set()
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for p in SPECIALS:
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symbols.add(p)
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for syllable, phonemes in lexicon.items():
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phonemes = phonemes.split()
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for p in phonemes:
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symbols.add(p)
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return sorted(list(symbols))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Generate lexicon for Chinese pinyin to phoneme for MFA")
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parser.add_argument("output", type=str, help="Path to save lexicon.")
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parser.add_argument(
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"--with-tone", action="store_true", help="whether to consider tone.")
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parser.add_argument(
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"--with-r", action="store_true", help="whether to consider erhua.")
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args = parser.parse_args()
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lexicon = generate_lexicon(args.with_tone, args.with_r)
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symbols = generate_symbols(lexicon)
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with open(args.output + ".lexicon", 'wt') as f:
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for k, v in lexicon.items():
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f.write(f"{k} {v}\n")
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with open(args.output + ".symbols", 'wt') as f:
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for s in symbols:
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f.write(s + "\n")
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print("Done!")
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# 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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"""Script to reorganize Baker dataset so as to use Montreal Force
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Aligner to align transcription and audio.
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Please refer to https://montreal-forced-aligner.readthedocs.io/en/latest/data_prep.html
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for more details about Montreal Force Aligner's requirements on cotpus.
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For scripts to reorganize other corpus, please refer to
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https://github.com/MontrealCorpusTools/MFA-reorganization-scripts
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for more details.
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"""
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import os
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import shutil
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import argparse
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from typing import Union
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from pathlib import Path
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from concurrent.futures import ThreadPoolExecutor
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import librosa
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import soundfile as sf
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from tqdm import tqdm
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def get_transcripts(path: Union[str, Path]):
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transcripts = {}
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with open(path) as f:
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lines = f.readlines()
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for i in range(0, len(lines), 2):
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sentence_id = lines[i].split()[0]
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transcription = lines[i + 1].strip()
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# tones are dropped here
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# since the lexicon does not consider tones, too
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transcription = " ".join([item[:-1] for item in transcription.split()])
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transcripts[sentence_id] = transcription
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return transcripts
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def resample_and_save(source, target, sr=16000):
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wav, _ = librosa.load(str(source), sr=sr)
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sf.write(str(target), wav, samplerate=sr, subtype='PCM_16')
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return target
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def reorganize_baker(root_dir: Union[str, Path],
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output_dir: Union[str, Path]=None,
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resample_audio=False):
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root_dir = Path(root_dir).expanduser()
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transcript_path = root_dir / "ProsodyLabeling" / "000001-010000.txt"
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transcriptions = get_transcripts(transcript_path)
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wave_dir = root_dir / "Wave"
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wav_paths = sorted(list(wave_dir.glob("*.wav")))
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output_dir = Path(output_dir).expanduser()
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assert wave_dir != output_dir, "Don't use an the original wav's directory as output_dir"
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output_dir.mkdir(parents=True, exist_ok=True)
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if resample_audio:
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with ThreadPoolExecutor(os.cpu_count()) as pool:
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with tqdm(total=len(wav_paths), desc="resampling") as pbar:
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futures = []
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for wav_path in wav_paths:
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future = pool.submit(resample_and_save, wav_path,
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output_dir / wav_path.name)
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future.add_done_callback(lambda p: pbar.update())
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futures.append(future)
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results = []
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for ft in futures:
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results.append(ft.result())
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else:
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for wav_path in tqdm(wav_paths, desc="copying"):
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shutil.copyfile(wav_path, output_dir / wav_path.name)
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for sentence_id, transcript in tqdm(
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transcriptions.items(), desc="transcription process"):
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with open(output_dir / (sentence_id + ".lab"), 'wt') as f:
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f.write(transcript)
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f.write('\n')
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print("Done!")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Reorganize Baker dataset for MFA")
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parser.add_argument("--root-dir", type=str, help="path to baker dataset.")
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parser.add_argument(
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"--output-dir",
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type=str,
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help="path to save outputs(audio and transcriptions)")
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parser.add_argument(
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"--resample-audio",
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action="store_true",
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help="To resample audio files or just copy them")
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args = parser.parse_args()
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reorganize_baker(args.root_dir, args.output_dir, args.resample_audio)
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EXP_DIR=exp
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||||||
|
|
||||||
mkdir -p $EXP_DIR
|
|
||||||
LEXICON_NAME='simple'
|
|
||||||
if [ ! -f "$EXP_DIR/$LEXICON_NAME.lexicon" ]; then
|
|
||||||
echo "generating lexicon..."
|
|
||||||
python local/generate_lexicon.py "$EXP_DIR/$LEXICON_NAME" --with-r
|
|
||||||
echo "lexicon done"
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [ ! -d $EXP_DIR/baker_corpus ]; then
|
|
||||||
echo "reorganizing baker corpus..."
|
|
||||||
python local/recorganize_baker.py --root-dir=~/datasets/BZNSYP --output-dir=$EXP_DIR/baker_corpus --resample-audio
|
|
||||||
echo "reorganization done. Check output in $EXP_DIR/baker_corpus."
|
|
||||||
echo "audio files are resampled to 16kHz"
|
|
||||||
echo "transcription for each audio file is saved with the same namd in $EXP_DIR/baker_corpus "
|
|
||||||
fi
|
|
||||||
|
|
||||||
echo "detecting oov..."
|
|
||||||
python local/detect_oov.py $EXP_DIR/baker_corpus $EXP_DIR/"$LEXICON_NAME.lexicon"
|
|
||||||
echo "detecting oov done. you may consider regenerate lexicon if there is unexpected OOVs."
|
|
||||||
|
|
||||||
|
|
||||||
MFA_DOWNLOAD_DIR=local/
|
|
||||||
|
|
||||||
if [ ! -f "$MFA_DOWNLOAD_DIR/montreal-forced-aligner_linux.tar.gz" ]; then
|
|
||||||
echo "downloading mfa..."
|
|
||||||
(cd $MFA_DOWNLOAD_DIR && wget https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/releases/download/v1.0.1/montreal-forced-aligner_linux.tar.gz)
|
|
||||||
echo "download mfa done!"
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [ ! -d "$MFA_DOWNLOAD_DIR/montreal-forced-aligner" ]; then
|
|
||||||
echo "extracting mfa..."
|
|
||||||
(cd $MFA_DOWNLOAD_DIR && tar xvf "montreal-forced-aligner_linux.tar.gz")
|
|
||||||
echo "extraction done!"
|
|
||||||
fi
|
|
||||||
|
|
||||||
export PATH="$MFA_DOWNLOAD_DIR/montreal-forced-aligner/bin"
|
|
||||||
if [ ! -d "$EXP_DIR/baker_alignment" ]; then
|
|
||||||
echo "Start MFA training..."
|
|
||||||
mfa_train_and_align $EXP_DIR/baker_corpus "$EXP_DIR/$LEXICON_NAME.lexicon" $EXP_DIR/baker_alignment -o $EXP_DIR/baker_model --clean --verbose --temp_directory exp/.mfa_train_and_align
|
|
||||||
echo "training done!"
|
|
||||||
echo "results: $EXP_DIR/baker_alignment"
|
|
||||||
echo "model: $EXP_DIR/baker_model"
|
|
||||||
fi
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Loading…
Reference in New Issue