# 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 re
from typing import List
import librosa
import numpy as np
# speaker|utt_id|phn dur phn dur ...
[docs]def get_phn_dur(file_name):
'''
read MFA duration.txt
Args:
file_name (str or Path): path of gen_duration_from_textgrid.py's result
Returns:
Dict: sentence: {'utt': ([char], [int])}
'''
f = open(file_name, 'r')
sentence = {}
speaker_set = set()
for line in f:
line_list = line.strip().split('|')
utt = line_list[0]
speaker = line_list[1]
p_d = line_list[-1]
speaker_set.add(speaker)
phn_dur = p_d.split()
phn = phn_dur[::2]
dur = phn_dur[1::2]
assert len(phn) == len(dur)
sentence[utt] = (phn, [int(i) for i in dur], speaker)
f.close()
return sentence, speaker_set
[docs]def note2midi(notes: List[str]) -> List[str]:
"""Covert note string to note id, for example: ["C1"] -> [24]
Args:
notes (List[str]): the list of note string
Returns:
List[str]: the list of note id
"""
midis = []
for note in notes:
if note == 'rest':
midi = 0
else:
midi = librosa.note_to_midi(note.split("/")[0])
midis.append(midi)
return midis
[docs]def time2frame(
times: List[float],
sample_rate: int=24000,
n_shift: int=128, ) -> List[int]:
"""Convert the phoneme duration of time(s) into frames
Args:
times (List[float]): phoneme duration of time(s)
sample_rate (int, optional): sample rate. Defaults to 24000.
n_shift (int, optional): frame shift. Defaults to 128.
Returns:
List[int]: phoneme duration of frame
"""
end = 0.0
ends = []
for t in times:
end += t
ends.append(end)
frame_pos = librosa.time_to_frames(ends, sr=sample_rate, hop_length=n_shift)
durations = np.diff(frame_pos, prepend=0)
return durations
[docs]def get_sentences_svs(
file_name,
dataset: str='opencpop',
sample_rate: int=24000,
n_shift: int=128, ):
'''
read label file
Args:
file_name (str or Path): path of gen_duration_from_textgrid.py's result
dataset (str): dataset name
Returns:
Dict: the information of sentence, include [phone id (int)], [the frame of phone (int)], [note id (int)], [note duration (float)], [is slur (int)], text(str), speaker name (str)
tuple: speaker name
'''
f = open(file_name, 'r')
sentence = {}
speaker_set = set()
if dataset == 'opencpop':
speaker_set.add("opencpop")
for line in f:
line_list = line.strip().split('|')
utt = line_list[0]
text = line_list[1]
ph = line_list[2].split()
midi = note2midi(line_list[3].split())
midi_dur = line_list[4].split()
ph_dur = time2frame([float(t) for t in line_list[5].split()], sample_rate=sample_rate, n_shift=n_shift)
is_slur = line_list[6].split()
assert len(ph) == len(midi) == len(midi_dur) == len(is_slur)
sentence[utt] = (ph, [int(i) for i in ph_dur],
[int(i) for i in midi],
[float(i) for i in midi_dur],
[int(i) for i in is_slur], text, "opencpop")
else:
print("dataset should in {opencpop} now!")
f.close()
return sentence, speaker_set
[docs]def merge_silence(sentence):
'''
merge silences
Args:
sentence (Dict): sentence: {'utt': (([char], [int]), str)}
'''
for utt in sentence:
cur_phn, cur_dur, speaker = sentence[utt]
new_phn = []
new_dur = []
# merge sp and sil
for i, p in enumerate(cur_phn):
if i > 0 and 'sil' == p and cur_phn[i - 1] in {"sil", "sp"}:
new_dur[-1] += cur_dur[i]
new_phn[-1] = 'sil'
else:
new_phn.append(p)
new_dur.append(cur_dur[i])
for i, (p, d) in enumerate(zip(new_phn, new_dur)):
if p in {"sp"}:
if d < 14:
new_phn[i] = 'sp'
else:
new_phn[i] = 'spl'
assert len(new_phn) == len(new_dur)
sentence[utt] = [new_phn, new_dur, speaker]
[docs]def get_phones_tones(sentence,
phones_output_path,
tones_output_path,
dataset="baker"):
'''
get phone set and tone set from training data and save it
Args:
sentence (Dict): sentence: {'utt': ([char], [int])}
phones_output_path (str or path): path to save phone_id_map
tones_output_path (str or path): path to save tone_id_map
'''
phn_token = set()
tone_token = set()
for utt in sentence:
for label in sentence[utt][0]:
# split tone from finals
match = re.match(r'^(\w+)([012345])$', label)
if match:
phn_token.add(match.group(1))
tone_token.add(match.group(2))
else:
phn_token.add(label)
tone_token.add('0')
phn_token = list(phn_token)
tone_token = list(tone_token)
phn_token.sort()
tone_token.sort()
phn_token = ["<pad>", "<unk>"] + phn_token
if dataset in {"baker", "aishell3"}:
phn_token += ["，", "。", "？", "！"]
else:
phn_token += [",", ".", "?", "!"]
phn_token += ["<eos>"]
with open(phones_output_path, 'w') as f:
for i, phn in enumerate(phn_token):
f.write(phn + ' ' + str(i) + '\n')
with open(tones_output_path, 'w') as f:
for i, tone in enumerate(tone_token):
f.write(tone + ' ' + str(i) + '\n')
[docs]def get_spk_id_map(speaker_set, output_path):
speakers = sorted(list(speaker_set))
with open(output_path, 'w') as f:
for i, spk in enumerate(speakers):
f.write(spk + ' ' + str(i) + '\n')
[docs]def compare_duration_and_mel_length(sentences, utt, mel):
'''
check duration error, correct sentences[utt] if possible, else pop sentences[utt]
Args:
sentences (Dict): sentences[utt] = [phones_list ,durations_list]
utt (str): utt_id
mel (np.ndarry): features (num_frames, n_mels)
'''
if utt in sentences:
len_diff = mel.shape[0] - sum(sentences[utt][1])
if len_diff != 0:
if len_diff > 0:
sentences[utt][1][-1] += len_diff
elif sentences[utt][1][-1] + len_diff > 0:
sentences[utt][1][-1] += len_diff
elif sentences[utt][1][0] + len_diff > 0:
sentences[utt][1][0] += len_diff
else:
print("the len_diff is unable to correct:", len_diff)
sentences.pop(utt)