def synthesize(model, input_data, force_cpu=False):
item = input_data.split('|')
clean_text = item[1]
if not hp.use_punctuation:
clean_text = text.remove_punctuation(clean_text)
if not hp.case_sensitive:
clean_text = text.to_lower(clean_text)
if hp.remove_multiple_wspaces:
clean_text = text.remove_odd_whitespaces(clean_text)
t = torch.LongTensor(
text.to_sequence(clean_text, use_phonemes=hp.use_phonemes))
if hp.multi_language:
# item[3]: l1-(len1),l2*0.75:l3*0.25-(len2),l1
# l_tokens: list, [l1-(len1),l2*0.75:l3*0.25-(len2),l1]
l_tokens = item[3].split(',')
t_length = len(clean_text) + 1
# 输出的l为一维向量，长度为language_num(language dim for every token)*token_num
l = []
for token in l_tokens:
# l_d: [l2*0.75:l3*0.25,(len2)]
l_d = token.split('-')
# language: [0,0,...,0]
language = [0] * hp.language_number
for l_cw in l_d[0].split(':'):
# l_cw: l2*0.75 / l3*0.25
# l_cw_s: list, [l2,0.75]
l_cw_s = l_cw.split('*')
language[hp.languages.index(
l_cw_s[0])] = 1 if len(l_cw_s) == 1 else float(l_cw_s[1])
# language: [0,0.75,0.25,...,0]
# language_length: int, (len2). 指定该语种覆盖的长度，或者默认剩下所有的长度
language_length = (int(l_d[1]) if len(l_d) == 2 else t_length)
# l: list。对每一个token对应一个language: [0,0.75,0.25,...,0]
l += [language] * language_length
t_length -= language_length
l = torch.FloatTensor([l])
else:
l = None
# s: [int]，仅有一个元素的向量
s = torch.LongTensor([hp.unique_speakers.index(item[2])
]) if hp.multi_speaker else None
if torch.cuda.is_available() and not force_cpu:
t = t.cuda(non_blocking=True)
if l is not None: l = l.cuda(non_blocking=True)
if s is not None: s = s.cuda(non_blocking=True)
# s：仅有一个speaker_id元素的向量
# l：元素个数为language_num*token_num的向量
s = model.inference(t, speaker=s, language=l).cpu().detach().numpy()
s = audio.denormalize_spectrogram(s, not hp.predict_linear)
return s
def __init__(self, meta_file, dataset_root_dir, known_unique_speakers=[]):
random.seed(1234)
self.root_dir = dataset_root_dir
# read meta-file: id|speaker|language|audio_file_path|mel_spectrogram_path|linear_spectrogram_path|text|phonemized_text
self.unique_speakers = known_unique_speakers.copy()
unique_speakers_set = set(self.unique_speakers)
self.items = []
with open(meta_file, 'r', encoding='utf-8') as f:
for line in f:
line_tokens = line[:-1].split('|')
item = {
'id': line_tokens[0],
'speaker': line_tokens[1],
'language': line_tokens[2],
'audio': line_tokens[3],
'spectrogram': line_tokens[4],
'linear_spectrogram': line_tokens[5],
'text': line_tokens[6],
'phonemes': line_tokens[7]
}
#print(hp.languages)
#print(item['language'])
if item['language'] in hp.languages:
#print("uniq speakers {0} \nline_token[1] {1} \t".format(item['language'],line_tokens[1]))
if line_tokens[1] not in unique_speakers_set:
unique_speakers_set.add(line_tokens[1])
self.unique_speakers.append(line_tokens[1])
#print("item is: ", item)
#print("speakers: ", unique_speakers_set)
self.items.append(item)
# clean text with basic stuff -- multiple spaces, case sensitivity and punctuation
for idx in range(len(self.items)):
item_text = self.items[idx]['text']
item_phon = self.items[idx]['phonemes']
if not hp.use_punctuation:
item_text = text.remove_punctuation(item_text)
item_phon = text.remove_punctuation(item_phon)
if not hp.case_sensitive:
item_text = text.to_lower(item_text)
if hp.remove_multiple_wspaces:
item_text = text.remove_odd_whitespaces(item_text)
item_phon = text.remove_odd_whitespaces(item_phon)
self.items[idx]['text'] = item_text
self.items[idx]['phonemes'] = item_phon
# convert text into a sequence of character IDs, convert language and speaker names to IDs
for idx in range(len(self.items)):
self.items[idx]['phonemes'] = text.to_sequence(self.items[idx]['phonemes'], use_phonemes=True)
self.items[idx]['text'] = text.to_sequence(self.items[idx]['text'], use_phonemes=False)
self.items[idx]['speaker'] = self.unique_speakers.index(self.items[idx]['speaker'])
self.items[idx]['language'] = hp.languages.index(self.items[idx]['language'])
def synthesize(model, input_data, force_cpu=False):
item = input_data.split('|')
print(item)
clean_text = item[1]
if not hp.use_punctuation:
clean_text = text.remove_punctuation(clean_text)
if not hp.case_sensitive:
clean_text = text.to_lower(clean_text)
if hp.remove_multiple_wspaces:
clean_text = text.remove_odd_whitespaces(clean_text)
t = torch.LongTensor(
text.to_sequence(clean_text, use_phonemes=hp.use_phonemes))
if hp.multi_language:
l_tokens = item[3].split(',')
t_length = len(clean_text) + 1
l = []
for token in l_tokens:
l_d = token.split('-')
language = [0] * hp.language_number
for l_cw in l_d[0].split(':'):
l_cw_s = l_cw.split('*')
language[hp.languages.index(
l_cw_s[0])] = 1 if len(l_cw_s) == 1 else float(l_cw_s[1])
language_length = (int(l_d[1]) if len(l_d) == 2 else t_length)
l += [language] * language_length
t_length -= language_length
l = torch.FloatTensor([l])
else:
l = None
s = torch.LongTensor([hp.unique_speakers.index(item[2])
]) if hp.multi_speaker else None
if torch.cuda.is_available() and not force_cpu:
t = t.cuda(non_blocking=True)
if l is not None: l = l.cuda(non_blocking=True)
if s is not None: s = s.cuda(non_blocking=True)
s = model.inference(t, speaker=s, language=l).cpu().detach().numpy()
s = audio.denormalize_spectrogram(s, not hp.predict_linear)
return s