# Copyright (c) 2023 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 Dict
from typing import List
import librosa
import numpy as np
import paddle
from pypinyin import lazy_pinyin
class SingFrontend():
def __init__(self, pinyin_phone_path: str, phone_vocab_path: str):
"""SVS Frontend
Args:
pinyin_phone_path (str): pinyin to phone file path, a 'pinyin|phones' (like: ba|b a ) pair per line.
phone_vocab_path (str): phone to phone id file path, a 'phone phone id' (like: a 4 ) pair per line.
"""
self.punc = '[、：，；。？！“”‘’\':,;.?!]'
self.pinyin_phones = {'AP': 'AP', 'SP': 'SP'}
if pinyin_phone_path:
with open(pinyin_phone_path, 'rt', encoding='utf-8') as f:
for line in f.readlines():
pinyin_phn = [
x.strip() for x in line.split('|') if x.strip() != ''
]
self.pinyin_phones[pinyin_phn[0]] = pinyin_phn[1]
self.vocab_phones = {}
if phone_vocab_path:
with open(phone_vocab_path, 'rt', encoding='utf-8') as f:
phn_id = [line.strip().split() for line in f.readlines()]
for phn, id in phn_id:
self.vocab_phones[phn] = int(id)
def get_phones(self, sentence: str) -> List[int]:
"""get phone list
Args:
sentence (str): sentence
Returns:
List[int]: phones list
Example:
sentence = "你好"
phones = ['n i', 'h ao']
"""
# remove all punc
sentence = re.sub(self.punc, "", sentence)
# Pypinyin can't solve polyphonic words
sentence = sentence.replace('最长', '最常').replace('长睫毛', '常睫毛') \
.replace('那么长', '那么常').replace('多长', '多常') \
.replace('很长', '很常')
# lyric
pinyins = lazy_pinyin(sentence, strict=False)
# replace unk word with SP
pinyins = [
pinyin if pinyin in self.pinyin_phones.keys() else "SP"
for pinyin in pinyins
]
phones = [
self.pinyin_phones[pinyin.strip()] for pinyin in pinyins
if pinyin.strip() in self.pinyin_phones
]
return phones
def get_note_info(self, note_info: str) -> List[str]:
note_info = [x.strip() for x in note_info.split('|') if x.strip() != '']
return note_info
def process(
self,
phones: List[int],
notes: List[str],
note_durs: List[float], ) -> Dict[str, List[paddle.Tensor]]:
new_phones = []
new_notes = []
new_note_durs = []
is_slurs = []
assert len(phones) == len(notes) == len(
note_durs
), "Please check the input, text, notes, note_durs should be the same length."
for i in range(len(phones)):
phone = phones[i].split()
note = notes[i].split()
note_dur = note_durs[i].split()
for phn in phone:
new_phones.append(phn)
new_notes.append(note[0])
new_note_durs.append(note_dur[0])
is_slurs.append(0)
if len(note) > 1:
for i in range(1, len(note)):
new_phones.append(phone[-1])
new_notes.append(note[i])
new_note_durs.append(note_dur[i])
is_slurs.append(1)
return new_phones, new_notes, new_note_durs, is_slurs
def get_input_ids(self, svs_input: Dict[str, str],
to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
"""convert input to int/float.
Args:
svs_input (Dict[str, str]): include keys: if input_type is phones, phones, notes, note_durs and is_slurs are needed.
if input_type is word, text, notes, and note_durs sre needed.
to_tensor (bool, optional): whether to convert to Tensor. Defaults to True.
Returns:
Dict[str, List[paddle.Tensor]]: result include phone_ids, note_ids, note_durs, is_slurs.
"""
result = {}
input_type = svs_input['input_type']
if input_type == 'phoneme':
assert "phones" in svs_input.keys() and "notes" in svs_input.keys() and "note_durs" in svs_input.keys() and "is_slurs" in svs_input.keys(), \
"When input_type is phoneme, phones, notes, note_durs, is_slurs should be in the svs_input."
phones = svs_input["phones"].split()
notes = svs_input["notes"].split()
note_durs = svs_input["note_durs"].split()
is_slurs = svs_input["is_slurs"].split()
assert len(phones) == len(notes) == len(note_durs) == len(
is_slurs
), "Please check the input, phones, notes, note_durs is_slurs should be the same length."
elif input_type == "word":
assert "text" in svs_input.keys() and "notes" in svs_input.keys() and "note_durs" in svs_input.keys(), \
"When input_type is word, text, notes, note_durs, should be in the svs_input."
phones = self.get_phones(svs_input['text'])
notes = self.get_note_info(svs_input['notes'])
note_durs = self.get_note_info(svs_input['note_durs'])
phones, notes, note_durs, is_slurs = self.process(
phones=phones, notes=notes, note_durs=note_durs)
phone_ids = [self.vocab_phones[phn] for phn in phones]
phone_ids = np.array(phone_ids, np.int64)
note_ids = [
librosa.note_to_midi(note.split("/")[0]) if note != 'rest' else 0
for note in notes
]
note_ids = np.array(note_ids, np.int64)
note_durs = np.array(note_durs, np.float32)
is_slurs = np.array(is_slurs, np.int64)
if to_tensor:
phone_ids = paddle.to_tensor(phone_ids)
note_ids = paddle.to_tensor(note_ids)
note_durs = paddle.to_tensor(note_durs)
is_slurs = paddle.to_tensor(is_slurs)
result['phone_ids'] = [phone_ids]
result['note_ids'] = [note_ids]
result['note_durs'] = [note_durs]
result['is_slurs'] = [is_slurs]
return result