|
import phonemizer |
|
import re |
|
import torch |
|
|
|
def split_num(num): |
|
num = num.group() |
|
if '.' in num: |
|
return num |
|
elif ':' in num: |
|
h, m = [int(n) for n in num.split(':')] |
|
if m == 0: |
|
return f"{h} o'clock" |
|
elif m < 10: |
|
return f'{h} oh {m}' |
|
return f'{h} {m}' |
|
year = int(num[:4]) |
|
if year < 1100 or year % 1000 < 10: |
|
return num |
|
left, right = num[:2], int(num[2:4]) |
|
s = 's' if num.endswith('s') else '' |
|
if 100 <= year % 1000 <= 999: |
|
if right == 0: |
|
return f'{left} hundred{s}' |
|
elif right < 10: |
|
return f'{left} oh {right}{s}' |
|
return f'{left} {right}{s}' |
|
|
|
def flip_money(m): |
|
m = m.group() |
|
bill = 'dollar' if m[0] == '$' else 'pound' |
|
if m[-1].isalpha(): |
|
return f'{m[1:]} {bill}s' |
|
elif '.' not in m: |
|
s = '' if m[1:] == '1' else 's' |
|
return f'{m[1:]} {bill}{s}' |
|
b, c = m[1:].split('.') |
|
s = '' if b == '1' else 's' |
|
c = int(c.ljust(2, '0')) |
|
coins = f"cent{'' if c == 1 else 's'}" if m[0] == '$' else ('penny' if c == 1 else 'pence') |
|
return f'{b} {bill}{s} and {c} {coins}' |
|
|
|
def point_num(num): |
|
a, b = num.group().split('.') |
|
return ' point '.join([a, ' '.join(b)]) |
|
|
|
def normalize_text(text): |
|
text = text.replace(chr(8216), "'").replace(chr(8217), "'") |
|
text = text.replace('«', chr(8220)).replace('»', chr(8221)) |
|
text = text.replace(chr(8220), '"').replace(chr(8221), '"') |
|
text = text.replace('(', '«').replace(')', '»') |
|
for a, b in zip('、。!,:;?', ',.!,:;?'): |
|
text = text.replace(a, b+' ') |
|
text = re.sub(r'[^\S \n]', ' ', text) |
|
text = re.sub(r' +', ' ', text) |
|
text = re.sub(r'(?<=\n) +(?=\n)', '', text) |
|
text = re.sub(r'\bD[Rr]\.(?= [A-Z])', 'Doctor', text) |
|
text = re.sub(r'\b(?:Mr\.|MR\.(?= [A-Z]))', 'Mister', text) |
|
text = re.sub(r'\b(?:Ms\.|MS\.(?= [A-Z]))', 'Miss', text) |
|
text = re.sub(r'\b(?:Mrs\.|MRS\.(?= [A-Z]))', 'Mrs', text) |
|
text = re.sub(r'\betc\.(?! [A-Z])', 'etc', text) |
|
text = re.sub(r'(?i)\b(y)eah?\b', r"\1e'a", text) |
|
text = re.sub(r'\d*\.\d+|\b\d{4}s?\b|(?<!:)\b(?:[1-9]|1[0-2]):[0-5]\d\b(?!:)', split_num, text) |
|
text = re.sub(r'(?<=\d),(?=\d)', '', text) |
|
text = re.sub(r'(?i)[$£]\d+(?:\.\d+)?(?: hundred| thousand| (?:[bm]|tr)illion)*\b|[$£]\d+\.\d\d?\b', flip_money, text) |
|
text = re.sub(r'\d*\.\d+', point_num, text) |
|
text = re.sub(r'(?<=\d)-(?=\d)', ' to ', text) |
|
text = re.sub(r'(?<=\d)S', ' S', text) |
|
text = re.sub(r"(?<=[BCDFGHJ-NP-TV-Z])'?s\b", "'S", text) |
|
text = re.sub(r"(?<=X')S\b", 's', text) |
|
text = re.sub(r'(?:[A-Za-z]\.){2,} [a-z]', lambda m: m.group().replace('.', '-'), text) |
|
text = re.sub(r'(?i)(?<=[A-Z])\.(?=[A-Z])', '-', text) |
|
return text.strip() |
|
|
|
def get_vocab(): |
|
_pad = "$" |
|
_punctuation = ';:,.!?¡¿—…"«»“” ' |
|
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' |
|
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ" |
|
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa) |
|
dicts = {} |
|
for i in range(len((symbols))): |
|
dicts[symbols[i]] = i |
|
return dicts |
|
|
|
VOCAB = get_vocab() |
|
def tokenize(ps): |
|
return [i for i in map(VOCAB.get, ps) if i is not None] |
|
|
|
phonemizers = dict( |
|
a=phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True), |
|
b=phonemizer.backend.EspeakBackend(language='en-gb', preserve_punctuation=True, with_stress=True), |
|
) |
|
def phonemize(text, lang, norm=True): |
|
if norm: |
|
text = normalize_text(text) |
|
ps = phonemizers[lang].phonemize([text]) |
|
ps = ps[0] if ps else '' |
|
|
|
ps = ps.replace('kəkˈoːɹoʊ', 'kˈoʊkəɹoʊ').replace('kəkˈɔːɹəʊ', 'kˈəʊkəɹəʊ') |
|
ps = ps.replace('ʲ', 'j').replace('r', 'ɹ').replace('x', 'k').replace('ɬ', 'l') |
|
ps = re.sub(r'(?<=[a-zɹː])(?=hˈʌndɹɪd)', ' ', ps) |
|
ps = re.sub(r' z(?=[;:,.!?¡¿—…"«»“” ]|$)', 'z', ps) |
|
if lang == 'a': |
|
ps = re.sub(r'(?<=nˈaɪn)ti(?!ː)', 'di', ps) |
|
ps = ''.join(filter(lambda p: p in VOCAB, ps)) |
|
return ps.strip() |
|
|
|
def length_to_mask(lengths): |
|
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) |
|
mask = torch.gt(mask+1, lengths.unsqueeze(1)) |
|
return mask |
|
|
|
@torch.no_grad() |
|
def forward(model, tokens, ref_s, speed): |
|
device = ref_s.device |
|
tokens = torch.LongTensor([[0, *tokens, 0]]).to(device) |
|
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) |
|
text_mask = length_to_mask(input_lengths).to(device) |
|
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) |
|
d_en = model.bert_encoder(bert_dur).transpose(-1, -2) |
|
s = ref_s[:, 128:] |
|
d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask) |
|
x, _ = model.predictor.lstm(d) |
|
duration = model.predictor.duration_proj(x) |
|
duration = torch.sigmoid(duration).sum(axis=-1) / speed |
|
pred_dur = torch.round(duration).clamp(min=1).long() |
|
pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item()) |
|
c_frame = 0 |
|
for i in range(pred_aln_trg.size(0)): |
|
pred_aln_trg[i, c_frame:c_frame + pred_dur[0,i].item()] = 1 |
|
c_frame += pred_dur[0,i].item() |
|
en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device) |
|
F0_pred, N_pred = model.predictor.F0Ntrain(en, s) |
|
t_en = model.text_encoder(tokens, input_lengths, text_mask) |
|
asr = t_en @ pred_aln_trg.unsqueeze(0).to(device) |
|
return model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().cpu().numpy() |
|
|
|
def generate(model, text, voicepack, lang='a', speed=1, ps=None): |
|
ps = ps or phonemize(text, lang) |
|
tokens = tokenize(ps) |
|
if not tokens: |
|
return None |
|
elif len(tokens) > 510: |
|
tokens = tokens[:510] |
|
print('Truncated to 510 tokens') |
|
ref_s = voicepack[len(tokens)] |
|
out = forward(model, tokens, ref_s, speed) |
|
ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens) |
|
return out, ps |
|
|