liangym
3 years ago
parent
9bf5471613
commit
4a6d04f960
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=diffsinger_opencpop \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=pwgan_opencpop \
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--voc_config=pwgan_opencpop_ckpt_1.4.0/default.yaml \
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--voc_ckpt=pwgan_opencpop_ckpt_1.4.0/snapshot_iter_100000.pdz \
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--voc_stat=pwgan_opencpop_ckpt_1.4.0/feats_stats.npy \
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--lang=sing \
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--text=${BIN_DIR}/../sentences_sing.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--speech_stretchs=dump/train/speech_stretchs.npy \
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--pinyin_phone=./pinyin_to_phone.txt
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fi
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# for more GAN Vocoders
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# hifigan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "in hifigan syn_e2e"
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=diffsinger_opencpop \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=hifigan_opencpop \
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--voc_config=hifigan_opencpop_ckpt_1.4.0/default.yaml \
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--voc_ckpt=hifigan_opencpop_ckpt_1.4.0/snapshot_iter_625000.pdz \
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--voc_stat=hifigan_opencpop_ckpt_1.4.0/feats_stats.npy \
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--lang=sing \
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--text=${BIN_DIR}/../sentences_sing.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--speech_stretchs=dump/train/speech_stretchs.npy \
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--pinyin_phone=./pinyin_to_phone.txt
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fi
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@ -0,0 +1,2 @@
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{"utt_id": "2093003457", "input_type": "word", "text": "小酒窝长睫毛AP是你最美的记号", "notes": "C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4", "note_durs": "0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340"}
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{"utt_id": "2093003458", "input_type": "phoneme", "phones": "w o m ei t ian sh ui ui b u u zh ao AP x iang n ian n i d e w ei x iao iao AP" , "notes": "C#4/Db4 C#4/Db4 D#4/Eb4 D#4/Eb4 F4 F4 F#4/Gb4 F#4/Gb4 D#4/Eb4 D#4/Eb4 D#4/Eb4 A#3/Bb3 A#3/Bb3 A#3/Bb3 rest F#4/Gb4 F#4/Gb4 F4 F4 F#4/Gb4 F#4/Gb4 F4 F4 G#4/Ab4 G#4/Ab4 D#4/Eb4 D#4/Eb4 C#4/Db4 rest", "note_durs": "0.221750 0.221750 0.414460 0.414460 0.223160 0.223160 0.430900 0.430900 0.335990 0.269270 0.269270 0.289060 0.522690 0.522690 0.355060 0.397130 0.397130 0.247690 0.247690 0.406720 0.406720 0.246830 0.246830 0.307540 0.307540 0.429910 0.429910 0.519130 0.342300", "is_slurs": "0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0"}
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@ -0,0 +1,191 @@
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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 re
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from typing import Dict
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from typing import List
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import librosa
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import numpy as np
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import paddle
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import ToJyutping
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from pypinyin import lazy_pinyin
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from pypinyin import pinyin
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from pypinyin import Style
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from paddlespeech.t2s.frontend.zh_normalization.text_normlization import TextNormalizer
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INITIALS = [
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'zh', 'ch', 'sh', 'b', 'p', 'm', 'f', 'd', 't', 'n', 'l', 'g', 'k', 'h',
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'j', 'q', 'x', 'r', 'z', 'c', 's', 'y', 'w'
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]
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FINALS = [
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'a', 'ai', 'an', 'ang', 'ao', 'e', 'ei', 'en', 'eng', 'er', 'i', 'ia',
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'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'iu', 'ng', 'o', 'ong',
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'ou', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've',
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'vn'
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]
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class SingFrontend():
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def __init__(self, pinyin_phone_path: str, phone_vocab_path: str):
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"""SVS Frontend
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Args:
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pinyin_phone_path (str): pinyin to phone file path, a 'pinyin|phones' (like: ba|b a ) pair per line.
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phone_vocab_path (str): phone to phone id file path, a 'phone phone id' (like: a 4 ) pair per line.
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"""
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self.punc = '[：，；。？！“”‘’\':,;.?!]'
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self.pinyin_phones = {'AP': 'AP', 'SP': 'SP'}
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if pinyin_phone_path:
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with open(pinyin_phone_path, 'rt', encoding='utf-8') as f:
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for line in f.readlines():
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pinyin_phn = [
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x.strip() for x in line.split('|') if x.strip() != ''
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]
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self.pinyin_phones[pinyin_phn[0]] = pinyin_phn[1]
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self.vocab_phones = {}
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if phone_vocab_path:
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with open(phone_vocab_path, 'rt', encoding='utf-8') as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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for phn, id in phn_id:
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self.vocab_phones[phn] = int(id)
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def get_phones(self, sentence: str) -> List[int]:
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"""_summary_
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Args:
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sentence (str): sentence
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Returns:
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List[int]: phones list
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Example:
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sentence = "你好"
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phones = ['n i', 'h ao']
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"""
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# remove all punc
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sentence = re.sub(self.punc, "", sentence)
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# Pypinyin can't solve polyphonic words
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sentence = sentence.replace('最长', '最常').replace('长睫毛', '常睫毛') \
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.replace('那么长', '那么常').replace('多长', '多常') \
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.replace('很长', '很常')
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# lyric
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pinyins = lazy_pinyin(sentence, strict=False)
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# replace unk word with SP
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pinyins = [
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pinyin if pinyin in self.pinyin_phones.keys() else "SP"
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for pinyin in pinyins
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]
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phones = [
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self.pinyin_phones[pinyin.strip()] for pinyin in pinyins
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if pinyin.strip() in self.pinyin_phones
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]
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return phones
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def get_note_info(self, note_info: str) -> List[str]:
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note_info = [x.strip() for x in note_info.split('|') if x.strip() != '']
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return note_info
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def process(
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self,
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phones: List[int],
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notes: List[str],
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note_durs: List[float], ) -> Dict[str, List[paddle.Tensor]]:
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new_phones = []
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new_notes = []
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new_note_durs = []
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is_slurs = []
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assert len(phones) == len(notes) == len(
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note_durs
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), "Please check the input, text, notes, note_durs should be the same length."
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for i in range(len(phones)):
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phone = phones[i].split()
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note = notes[i].split()
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note_dur = note_durs[i].split()
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for phn in phone:
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new_phones.append(phn)
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new_notes.append(note[0])
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new_note_durs.append(note_dur[0])
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is_slurs.append(0)
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if len(note) > 1:
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for i in range(1, len(note)):
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new_phones.append(phone[-1])
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new_notes.append(note[i])
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new_note_durs.append(note_dur[i])
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is_slurs.append(1)
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return new_phones, new_notes, new_note_durs, is_slurs
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def get_input_ids(self, svs_input: Dict[str, str],
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to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
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"""convert input to int/float.
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Args:
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svs_input (Dict[str, str]): include keys: if input_type is phones, phones, notes, note_durs and is_slurs are needed.
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if input_type is word, text, notes, and note_durs sre needed.
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to_tensor (bool, optional): whether to convert to Tensor. Defaults to True.
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Returns:
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Dict[str, List[paddle.Tensor]]: result include phone_ids, note_ids, note_durs, is_slurs.
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"""
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result = {}
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input_type = svs_input['input_type']
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if input_type == 'phoneme':
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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(), \
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"When input_type is phoneme, phones, notes, note_durs, is_slurs should be in the svs_input."
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phones = svs_input["phones"].split()
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notes = svs_input["notes"].split()
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note_durs = svs_input["note_durs"].split()
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is_slurs = svs_input["is_slurs"].split()
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assert len(phones) == len(notes) == len(note_durs) == len(
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is_slurs
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), "Please check the input, phones, notes, note_durs is_slurs should be the same length."
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elif input_type == "word":
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assert "text" in svs_input.keys() and "notes" in svs_input.keys() and "note_durs" in svs_input.keys(), \
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"When input_type is word, text, notes, note_durs, should be in the svs_input."
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phones = self.get_phones(svs_input['text'])
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notes = self.get_note_info(svs_input['notes'])
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note_durs = self.get_note_info(svs_input['note_durs'])
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phones, notes, note_durs, is_slurs = self.process(
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phones=phones, notes=notes, note_durs=note_durs)
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phone_ids = [self.vocab_phones[phn] for phn in phones]
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phone_ids = np.array(phone_ids, np.int64)
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note_ids = [
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librosa.note_to_midi(note.split("/")[0]) if note != 'rest' else 0
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for note in notes
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]
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note_ids = np.array(note_ids, np.int64)
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note_durs = np.array(note_durs, np.float32)
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is_slurs = np.array(is_slurs, np.int64)
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if to_tensor:
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phone_ids = paddle.to_tensor(phone_ids)
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note_ids = paddle.to_tensor(note_ids)
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note_durs = paddle.to_tensor(note_durs)
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is_slurs = paddle.to_tensor(is_slurs)
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result['phone_ids'] = [phone_ids]
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result['note_ids'] = [note_ids]
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result['note_durs'] = [note_durs]
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result['is_slurs'] = [is_slurs]
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return result
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