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