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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']) |