Spaces:
Running
Running
File size: 10,086 Bytes
26925fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 |
import os
import torch
import numpy as np
from modules.hifigan.hifigan import HifiGanGenerator
from vocoders.hifigan import HifiGAN
from inference.m4singer.m4singer.map import m4singer_pinyin2ph_func
from utils import load_ckpt
from utils.hparams import set_hparams, hparams
from utils.text_encoder import TokenTextEncoder
from pypinyin import pinyin, lazy_pinyin, Style
import librosa
import glob
import re
class BaseSVSInfer:
def __init__(self, hparams, device=None):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.hparams = hparams
self.device = device
phone_list = ["<AP>", "<SP>", "a", "ai", "an", "ang", "ao", "b", "c", "ch", "d", "e", "ei", "en", "eng", "er", "f", "g", "h",
"i", "ia", "ian", "iang", "iao", "ie", "in", "ing", "iong", "iou", "j", "k", "l", "m", "n", "o", "ong", "ou",
"p", "q", "r", "s", "sh", "t", "u", "ua", "uai", "uan", "uang", "uei", "uen", "uo", "v", "van", "ve", "vn",
"x", "z", "zh"]
self.ph_encoder = TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',')
self.pinyin2phs = m4singer_pinyin2ph_func()
self.spk_map = {"Alto-1": 0, "Alto-2": 1, "Alto-3": 2, "Alto-4": 3, "Alto-5": 4, "Alto-6": 5, "Alto-7": 6, "Bass-1": 7,
"Bass-2": 8, "Bass-3": 9, "Soprano-1": 10, "Soprano-2": 11, "Soprano-3": 12, "Tenor-1": 13, "Tenor-2": 14,
"Tenor-3": 15, "Tenor-4": 16, "Tenor-5": 17, "Tenor-6": 18, "Tenor-7": 19}
self.model = self.build_model()
self.model.eval()
self.model.to(self.device)
self.vocoder = self.build_vocoder()
self.vocoder.eval()
self.vocoder.to(self.device)
def build_model(self):
raise NotImplementedError
def forward_model(self, inp):
raise NotImplementedError
def build_vocoder(self):
base_dir = hparams['vocoder_ckpt']
config_path = f'{base_dir}/config.yaml'
ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1]
print('| load HifiGAN: ', ckpt)
ckpt_dict = torch.load(ckpt, map_location="cpu")
config = set_hparams(config_path, global_hparams=False)
state = ckpt_dict["state_dict"]["model_gen"]
vocoder = HifiGanGenerator(config)
vocoder.load_state_dict(state, strict=True)
vocoder.remove_weight_norm()
vocoder = vocoder.eval().to(self.device)
return vocoder
def run_vocoder(self, c, **kwargs):
c = c.transpose(2, 1) # [B, 80, T]
f0 = kwargs.get('f0') # [B, T]
if f0 is not None and hparams.get('use_nsf'):
# f0 = torch.FloatTensor(f0).to(self.device)
y = self.vocoder(c, f0).view(-1)
else:
y = self.vocoder(c).view(-1)
# [T]
return y[None]
def preprocess_word_level_input(self, inp):
# Pypinyin can't solve polyphonic words
text_raw = inp['text']
# lyric
pinyins = lazy_pinyin(text_raw, strict=False)
ph_per_word_lst = [self.pinyin2phs[pinyin.strip()] for pinyin in pinyins if pinyin.strip() in self.pinyin2phs]
# Note
note_per_word_lst = [x.strip() for x in inp['notes'].split('|') if x.strip() != '']
mididur_per_word_lst = [x.strip() for x in inp['notes_duration'].split('|') if x.strip() != '']
if len(note_per_word_lst) == len(ph_per_word_lst) == len(mididur_per_word_lst):
print('Pass word-notes check.')
else:
print('The number of words does\'t match the number of notes\' windows. ',
'You should split the note(s) for each word by | mark.')
print(ph_per_word_lst, note_per_word_lst, mididur_per_word_lst)
print(len(ph_per_word_lst), len(note_per_word_lst), len(mididur_per_word_lst))
return None
note_lst = []
ph_lst = []
midi_dur_lst = []
is_slur = []
for idx, ph_per_word in enumerate(ph_per_word_lst):
# for phs in one word:
# single ph like ['ai'] or multiple phs like ['n', 'i']
ph_in_this_word = ph_per_word.split()
# for notes in one word:
# single note like ['D4'] or multiple notes like ['D4', 'E4'] which means a 'slur' here.
note_in_this_word = note_per_word_lst[idx].split()
midi_dur_in_this_word = mididur_per_word_lst[idx].split()
# process for the model input
# Step 1.
# Deal with note of 'not slur' case or the first note of 'slur' case
# j ie
# F#4/Gb4 F#4/Gb4
# 0 0
for ph in ph_in_this_word:
ph_lst.append(ph)
note_lst.append(note_in_this_word[0])
midi_dur_lst.append(midi_dur_in_this_word[0])
is_slur.append(0)
# step 2.
# Deal with the 2nd, 3rd... notes of 'slur' case
# j ie ie
# F#4/Gb4 F#4/Gb4 C#4/Db4
# 0 0 1
if len(note_in_this_word) > 1: # is_slur = True, we should repeat the YUNMU to match the 2nd, 3rd... notes.
for idx in range(1, len(note_in_this_word)):
ph_lst.append(ph_in_this_word[-1])
note_lst.append(note_in_this_word[idx])
midi_dur_lst.append(midi_dur_in_this_word[idx])
is_slur.append(1)
ph_seq = ' '.join(ph_lst)
if len(ph_lst) == len(note_lst) == len(midi_dur_lst):
print(len(ph_lst), len(note_lst), len(midi_dur_lst))
print('Pass word-notes check.')
else:
print('The number of words does\'t match the number of notes\' windows. ',
'You should split the note(s) for each word by | mark.')
return None
return ph_seq, note_lst, midi_dur_lst, is_slur
def preprocess_phoneme_level_input(self, inp):
ph_seq = inp['ph_seq']
note_lst = inp['note_seq'].split()
midi_dur_lst = inp['note_dur_seq'].split()
is_slur = [float(x) for x in inp['is_slur_seq'].split()]
print(len(note_lst), len(ph_seq.split()), len(midi_dur_lst))
if len(note_lst) == len(ph_seq.split()) == len(midi_dur_lst):
print('Pass word-notes check.')
else:
print('The number of words does\'t match the number of notes\' windows. ',
'You should split the note(s) for each word by | mark.')
return None
return ph_seq, note_lst, midi_dur_lst, is_slur
def preprocess_input(self, inp, input_type='word'):
"""
:param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)}
:return:
"""
item_name = inp.get('item_name', '<ITEM_NAME>')
spk_name = inp.get('spk_name', 'Alto-1')
# single spk
spk_id = self.spk_map[spk_name]
# get ph seq, note lst, midi dur lst, is slur lst.
if input_type == 'word':
ret = self.preprocess_word_level_input(inp)
elif input_type == 'phoneme':
ret = self.preprocess_phoneme_level_input(inp)
else:
print('Invalid input type.')
return None
if ret:
ph_seq, note_lst, midi_dur_lst, is_slur = ret
else:
print('==========> Preprocess_word_level or phone_level input wrong.')
return None
# convert note lst to midi id; convert note dur lst to midi duration
try:
midis = [librosa.note_to_midi(x.split("/")[0]) if x != 'rest' else 0
for x in note_lst]
midi_dur_lst = [float(x) for x in midi_dur_lst]
except Exception as e:
print(e)
print('Invalid Input Type.')
return None
ph_token = self.ph_encoder.encode(ph_seq)
item = {'item_name': item_name, 'text': inp['text'], 'ph': ph_seq, 'spk_id': spk_id,
'ph_token': ph_token, 'pitch_midi': np.asarray(midis), 'midi_dur': np.asarray(midi_dur_lst),
'is_slur': np.asarray(is_slur), }
item['ph_len'] = len(item['ph_token'])
return item
def input_to_batch(self, item):
item_names = [item['item_name']]
text = [item['text']]
ph = [item['ph']]
txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device)
txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device)
spk_ids = torch.LongTensor([item['spk_id']])[:].to(self.device)
pitch_midi = torch.LongTensor(item['pitch_midi'])[None, :hparams['max_frames']].to(self.device)
midi_dur = torch.FloatTensor(item['midi_dur'])[None, :hparams['max_frames']].to(self.device)
is_slur = torch.LongTensor(item['is_slur'])[None, :hparams['max_frames']].to(self.device)
batch = {
'item_name': item_names,
'text': text,
'ph': ph,
'txt_tokens': txt_tokens,
'txt_lengths': txt_lengths,
'spk_ids': spk_ids,
'pitch_midi': pitch_midi,
'midi_dur': midi_dur,
'is_slur': is_slur
}
return batch
def postprocess_output(self, output):
return output
def infer_once(self, inp):
inp = self.preprocess_input(inp, input_type=inp['input_type'] if inp.get('input_type') else 'word')
output = self.forward_model(inp)
output = self.postprocess_output(output)
return output
@classmethod
def example_run(cls, inp):
from utils.audio import save_wav
set_hparams(print_hparams=False)
infer_ins = cls(hparams)
out = infer_ins.infer_once(inp)
os.makedirs('infer_out', exist_ok=True)
f_name = inp['spk_name'] + ' | ' + inp['text']
save_wav(out, f'infer_out/{f_name}.wav', hparams['audio_sample_rate']) |