style
This commit is contained in:
patil-suraj
parent
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commit
ace07110c1
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@ -145,8 +145,9 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
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if n_spks > 1:
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self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim)
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self.spk_mlp = torch.nn.Sequential(torch.nn.Linear(spk_emb_dim, spk_emb_dim * 4), Mish(),
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torch.nn.Linear(spk_emb_dim * 4, n_feats))
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self.spk_mlp = torch.nn.Sequential(
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torch.nn.Linear(spk_emb_dim, spk_emb_dim * 4), Mish(), torch.nn.Linear(spk_emb_dim * 4, n_feats)
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)
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self.time_pos_emb = SinusoidalPosEmb(dim)
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self.mlp = torch.nn.Sequential(torch.nn.Linear(dim, dim * 4), Mish(), torch.nn.Linear(dim * 4, dim))
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@ -1,11 +1,12 @@
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# tokenizer
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import re
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import os
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import re
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from shutil import copyfile
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import torch
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try:
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from transformers import PreTrainedTokenizer
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except:
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@ -25,17 +26,95 @@ except:
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valid_symbols = [
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'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
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'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
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'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY',
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'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1',
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'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0',
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'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW',
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'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH'
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"AA",
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"AA0",
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"AA1",
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"AA2",
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"AE",
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"AE0",
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"AE1",
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"AE2",
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"AH",
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"AH0",
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"AH1",
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"AH2",
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"AO",
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"AO0",
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"AO1",
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"AO2",
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"AW",
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"AW0",
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"AW1",
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"AW2",
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"AY",
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"AY0",
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"AY1",
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"AY2",
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"B",
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"CH",
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"D",
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"DH",
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"EH",
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"EH0",
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"EH1",
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"EH2",
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"ER",
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"ER0",
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"ER1",
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"ER2",
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"EY",
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"EY0",
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"EY1",
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"EY2",
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"F",
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"G",
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"HH",
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"IH",
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"IH0",
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"IH1",
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"IH2",
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"IY",
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"IY0",
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"IY1",
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"IY2",
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"JH",
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"K",
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"L",
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"M",
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"N",
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"NG",
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"OW",
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"OW0",
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"OW1",
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"OW2",
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"OY",
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"OY0",
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"OY1",
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"OY2",
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"P",
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"R",
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"S",
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"SH",
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"T",
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"TH",
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"UH",
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"UH0",
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"UH1",
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"UH2",
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"UW",
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"UW0",
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"UW1",
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"UW2",
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"V",
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"W",
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"Y",
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"Z",
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"ZH",
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]
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_valid_symbol_set = set(valid_symbols)
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def intersperse(lst, item):
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# Adds blank symbol
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result = [item] * (len(lst) * 2 + 1)
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@ -46,7 +125,7 @@ def intersperse(lst, item):
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class CMUDict:
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def __init__(self, file_or_path, keep_ambiguous=True):
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if isinstance(file_or_path, str):
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with open(file_or_path, encoding='latin-1') as f:
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with open(file_or_path, encoding="latin-1") as f:
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entries = _parse_cmudict(f)
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else:
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entries = _parse_cmudict(file_or_path)
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@ -61,15 +140,15 @@ class CMUDict:
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return self._entries.get(word.upper())
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_alt_re = re.compile(r'\([0-9]+\)')
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_alt_re = re.compile(r"\([0-9]+\)")
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def _parse_cmudict(file):
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cmudict = {}
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for line in file:
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if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"):
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parts = line.split(' ')
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word = re.sub(_alt_re, '', parts[0])
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if len(line) and (line[0] >= "A" and line[0] <= "Z" or line[0] == "'"):
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parts = line.split(" ")
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word = re.sub(_alt_re, "", parts[0])
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pronunciation = _get_pronunciation(parts[1])
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if pronunciation:
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if word in cmudict:
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@ -80,36 +159,38 @@ def _parse_cmudict(file):
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def _get_pronunciation(s):
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parts = s.strip().split(' ')
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parts = s.strip().split(" ")
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for part in parts:
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if part not in _valid_symbol_set:
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return None
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return ' '.join(parts)
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return " ".join(parts)
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_whitespace_re = re.compile(r"\s+")
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_whitespace_re = re.compile(r'\s+')
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_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
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('mrs', 'misess'),
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('mr', 'mister'),
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('dr', 'doctor'),
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('st', 'saint'),
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('co', 'company'),
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('jr', 'junior'),
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('maj', 'major'),
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('gen', 'general'),
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('drs', 'doctors'),
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('rev', 'reverend'),
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('lt', 'lieutenant'),
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('hon', 'honorable'),
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('sgt', 'sergeant'),
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('capt', 'captain'),
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('esq', 'esquire'),
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('ltd', 'limited'),
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('col', 'colonel'),
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('ft', 'fort'),
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]]
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_abbreviations = [
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(re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
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for x in [
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("mrs", "misess"),
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("mr", "mister"),
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("dr", "doctor"),
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("st", "saint"),
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("co", "company"),
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("jr", "junior"),
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("maj", "major"),
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("gen", "general"),
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("drs", "doctors"),
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("rev", "reverend"),
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("lt", "lieutenant"),
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("hon", "honorable"),
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("sgt", "sergeant"),
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("capt", "captain"),
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("esq", "esquire"),
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("ltd", "limited"),
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("col", "colonel"),
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("ft", "fort"),
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]
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]
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def expand_abbreviations(text):
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@ -127,7 +208,7 @@ def lowercase(text):
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def collapse_whitespace(text):
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return re.sub(_whitespace_re, ' ', text)
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return re.sub(_whitespace_re, " ", text)
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def convert_to_ascii(text):
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@ -156,46 +237,42 @@ def english_cleaners(text):
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return text
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_inflect = inflect.engine()
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_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
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_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
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_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
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_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
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_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
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_number_re = re.compile(r'[0-9]+')
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_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
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_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
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_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
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_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
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_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
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_number_re = re.compile(r"[0-9]+")
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def _remove_commas(m):
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return m.group(1).replace(',', '')
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return m.group(1).replace(",", "")
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def _expand_decimal_point(m):
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return m.group(1).replace('.', ' point ')
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return m.group(1).replace(".", " point ")
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def _expand_dollars(m):
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match = m.group(1)
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parts = match.split('.')
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parts = match.split(".")
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if len(parts) > 2:
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return match + ' dollars'
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return match + " dollars"
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dollars = int(parts[0]) if parts[0] else 0
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cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
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if dollars and cents:
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dollar_unit = 'dollar' if dollars == 1 else 'dollars'
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cent_unit = 'cent' if cents == 1 else 'cents'
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return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
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dollar_unit = "dollar" if dollars == 1 else "dollars"
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cent_unit = "cent" if cents == 1 else "cents"
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return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
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elif dollars:
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dollar_unit = 'dollar' if dollars == 1 else 'dollars'
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return '%s %s' % (dollars, dollar_unit)
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dollar_unit = "dollar" if dollars == 1 else "dollars"
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return "%s %s" % (dollars, dollar_unit)
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elif cents:
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cent_unit = 'cent' if cents == 1 else 'cents'
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return '%s %s' % (cents, cent_unit)
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cent_unit = "cent" if cents == 1 else "cents"
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return "%s %s" % (cents, cent_unit)
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else:
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return 'zero dollars'
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return "zero dollars"
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def _expand_ordinal(m):
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@ -206,37 +283,37 @@ def _expand_number(m):
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num = int(m.group(0))
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if num > 1000 and num < 3000:
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if num == 2000:
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return 'two thousand'
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return "two thousand"
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elif num > 2000 and num < 2010:
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return 'two thousand ' + _inflect.number_to_words(num % 100)
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return "two thousand " + _inflect.number_to_words(num % 100)
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elif num % 100 == 0:
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return _inflect.number_to_words(num // 100) + ' hundred'
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return _inflect.number_to_words(num // 100) + " hundred"
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else:
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return _inflect.number_to_words(num, andword='', zero='oh',
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group=2).replace(', ', ' ')
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return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ")
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else:
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return _inflect.number_to_words(num, andword='')
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return _inflect.number_to_words(num, andword="")
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def normalize_numbers(text):
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text = re.sub(_comma_number_re, _remove_commas, text)
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text = re.sub(_pounds_re, r'\1 pounds', text)
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text = re.sub(_pounds_re, r"\1 pounds", text)
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text = re.sub(_dollars_re, _expand_dollars, text)
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text = re.sub(_decimal_number_re, _expand_decimal_point, text)
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text = re.sub(_ordinal_re, _expand_ordinal, text)
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text = re.sub(_number_re, _expand_number, text)
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return text
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""" from https://github.com/keithito/tacotron """
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_pad = '_'
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_punctuation = '!\'(),.:;? '
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_special = '-'
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_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
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_pad = "_"
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_punctuation = "!'(),.:;? "
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_special = "-"
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_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
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# Prepend "@" to ARPAbet symbols to ensure uniqueness:
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_arpabet = ['@' + s for s in valid_symbols]
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_arpabet = ["@" + s for s in valid_symbols]
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# Export all symbols:
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symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet
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@ -245,7 +322,7 @@ symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpab
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_symbol_to_id = {s: i for i, s in enumerate(symbols)}
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_id_to_symbol = {i: s for i, s in enumerate(symbols)}
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_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
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_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
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def get_arpabet(word, dictionary):
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@ -257,7 +334,7 @@ def get_arpabet(word, dictionary):
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def text_to_sequence(text, cleaner_names=[english_cleaners], dictionary=None):
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'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
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"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
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The text can optionally have ARPAbet sequences enclosed in curly braces embedded
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in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
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@ -269,9 +346,9 @@ def text_to_sequence(text, cleaner_names=[english_cleaners], dictionary=None):
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Returns:
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List of integers corresponding to the symbols in the text
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'''
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"""
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sequence = []
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space = _symbols_to_sequence(' ')
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space = _symbols_to_sequence(" ")
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# Check for curly braces and treat their contents as ARPAbet:
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while len(text):
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m = _curly_re.match(text)
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@ -292,7 +369,7 @@ def text_to_sequence(text, cleaner_names=[english_cleaners], dictionary=None):
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sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
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sequence += _arpabet_to_sequence(m.group(2))
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text = m.group(3)
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# remove trailing space
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if dictionary is not None:
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sequence = sequence[:-1] if sequence[-1] == space[0] else sequence
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@ -300,16 +377,16 @@ def text_to_sequence(text, cleaner_names=[english_cleaners], dictionary=None):
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def sequence_to_text(sequence):
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'''Converts a sequence of IDs back to a string'''
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result = ''
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"""Converts a sequence of IDs back to a string"""
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result = ""
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for symbol_id in sequence:
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if symbol_id in _id_to_symbol:
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s = _id_to_symbol[symbol_id]
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# Enclose ARPAbet back in curly braces:
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if len(s) > 1 and s[0] == '@':
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s = '{%s}' % s[1:]
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if len(s) > 1 and s[0] == "@":
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s = "{%s}" % s[1:]
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result += s
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return result.replace('}{', ' ')
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return result.replace("}{", " ")
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def _clean_text(text, cleaner_names):
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@ -323,17 +400,18 @@ def _symbols_to_sequence(symbols):
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def _arpabet_to_sequence(text):
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return _symbols_to_sequence(['@' + s for s in text.split()])
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return _symbols_to_sequence(["@" + s for s in text.split()])
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def _should_keep_symbol(s):
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return s in _symbol_to_id and s != '_' and s != '~'
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return s in _symbol_to_id and s != "_" and s != "~"
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VOCAB_FILES_NAMES = {
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"dict_file": "dict_file.txt",
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}
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class GradTTSTokenizer(PreTrainedTokenizer):
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vocab_files_names = VOCAB_FILES_NAMES
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@ -341,17 +419,17 @@ class GradTTSTokenizer(PreTrainedTokenizer):
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super().__init__(**kwargs)
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self.cmu = CMUDict(dict_file)
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self.dict_file = dict_file
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def __call__(self, text):
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x = torch.LongTensor(intersperse(text_to_sequence(text, dictionary=self.cmu), len(symbols)))[None]
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x_lengths = torch.LongTensor([x.shape[-1]])
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return x, x_lengths
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def save_vocabulary(self, save_directory: str, filename_prefix = None):
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def save_vocabulary(self, save_directory: str, filename_prefix=None):
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dict_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["dict_file"]
|
||||
)
|
||||
|
||||
copyfile(self.dict_file, dict_file)
|
||||
|
||||
return (dict_file, )
|
||||
|
||||
return (dict_file,)
|
||||
|
|
|
|||
|
|
@ -4,13 +4,13 @@ import math
|
|||
|
||||
import torch
|
||||
from torch import nn
|
||||
import tqdm
|
||||
|
||||
import tqdm
|
||||
from diffusers import DiffusionPipeline
|
||||
from diffusers.configuration_utils import ConfigMixin
|
||||
from diffusers.modeling_utils import ModelMixin
|
||||
from diffusers import DiffusionPipeline
|
||||
|
||||
from .grad_tts_utils import GradTTSTokenizer # flake8: noqa
|
||||
from .grad_tts_utils import GradTTSTokenizer # flake8: noqa
|
||||
|
||||
|
||||
def sequence_mask(length, max_length=None):
|
||||
|
|
@ -382,7 +382,7 @@ class TextEncoder(ModelMixin, ConfigMixin):
|
|||
self.window_size = window_size
|
||||
self.spk_emb_dim = spk_emb_dim
|
||||
self.n_spks = n_spks
|
||||
|
||||
|
||||
self.emb = torch.nn.Embedding(n_vocab, n_channels)
|
||||
torch.nn.init.normal_(self.emb.weight, 0.0, n_channels**-0.5)
|
||||
|
||||
|
|
@ -403,7 +403,7 @@ class TextEncoder(ModelMixin, ConfigMixin):
|
|||
n_channels + (spk_emb_dim if n_spks > 1 else 0), filter_channels_dp, kernel_size, p_dropout
|
||||
)
|
||||
|
||||
def forward(self, x, x_lengths, spk=None):
|
||||
def forward(self, x, x_lengths, spk=None):
|
||||
x = self.emb(x) * math.sqrt(self.n_channels)
|
||||
x = torch.transpose(x, 1, -1)
|
||||
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
|
|
@ -424,26 +424,30 @@ class GradTTS(DiffusionPipeline):
|
|||
def __init__(self, unet, text_encoder, noise_scheduler, tokenizer):
|
||||
super().__init__()
|
||||
noise_scheduler = noise_scheduler.set_format("pt")
|
||||
self.register_modules(unet=unet, text_encoder=text_encoder, noise_scheduler=noise_scheduler, tokenizer=tokenizer)
|
||||
|
||||
self.register_modules(
|
||||
unet=unet, text_encoder=text_encoder, noise_scheduler=noise_scheduler, tokenizer=tokenizer
|
||||
)
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(self, text, num_inference_steps=50, temperature=1.3, length_scale=0.91, speaker_id=15, torch_device=None):
|
||||
def __call__(
|
||||
self, text, num_inference_steps=50, temperature=1.3, length_scale=0.91, speaker_id=15, torch_device=None
|
||||
):
|
||||
if torch_device is None:
|
||||
torch_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
self.unet.to(torch_device)
|
||||
self.text_encoder.to(torch_device)
|
||||
|
||||
|
||||
x, x_lengths = self.tokenizer(text)
|
||||
x = x.to(torch_device)
|
||||
x_lengths = x_lengths.to(torch_device)
|
||||
|
||||
|
||||
if speaker_id is not None:
|
||||
speaker_id= torch.LongTensor([speaker_id]).to(torch_device)
|
||||
|
||||
speaker_id = torch.LongTensor([speaker_id]).to(torch_device)
|
||||
|
||||
# Get encoder_outputs `mu_x` and log-scaled token durations `logw`
|
||||
mu_x, logw, x_mask = self.text_encoder(x, x_lengths)
|
||||
|
||||
|
||||
w = torch.exp(logw) * x_mask
|
||||
w_ceil = torch.ceil(w) * length_scale
|
||||
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
||||
|
|
@ -461,16 +465,16 @@ class GradTTS(DiffusionPipeline):
|
|||
|
||||
# Sample latent representation from terminal distribution N(mu_y, I)
|
||||
z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature
|
||||
|
||||
|
||||
xt = z * y_mask
|
||||
h = 1.0 / num_inference_steps
|
||||
for t in tqdm.tqdm(range(num_inference_steps), total=num_inference_steps):
|
||||
t = (1.0 - (t + 0.5)*h) * torch.ones(z.shape[0], dtype=z.dtype, device=z.device)
|
||||
t = (1.0 - (t + 0.5) * h) * torch.ones(z.shape[0], dtype=z.dtype, device=z.device)
|
||||
time = t.unsqueeze(-1).unsqueeze(-1)
|
||||
|
||||
|
||||
residual = self.unet(xt, y_mask, mu_y, t, speaker_id)
|
||||
|
||||
|
||||
xt = self.noise_scheduler.step(xt, residual, mu_y, h, time)
|
||||
xt = xt * y_mask
|
||||
|
||||
return xt[:, :, :y_max_length]
|
||||
|
||||
return xt[:, :, :y_max_length]
|
||||
|
|
|
|||
|
|
@ -19,6 +19,6 @@
|
|||
from .classifier_free_guidance import ClassifierFreeGuidanceScheduler
|
||||
from .scheduling_ddim import DDIMScheduler
|
||||
from .scheduling_ddpm import DDPMScheduler
|
||||
from .scheduling_pndm import PNDMScheduler
|
||||
from .scheduling_grad_tts import GradTTSScheduler
|
||||
from .scheduling_pndm import PNDMScheduler
|
||||
from .scheduling_utils import SchedulerMixin
|
||||
|
|
|
|||
|
|
@ -36,11 +36,11 @@ class GradTTSScheduler(SchedulerMixin, ConfigMixin):
|
|||
self.timesteps = int(timesteps)
|
||||
|
||||
self.set_format(tensor_format=tensor_format)
|
||||
|
||||
|
||||
def sample_noise(self, timestep):
|
||||
noise = self.beta_start + (self.beta_end - self.beta_start) * timestep
|
||||
return noise
|
||||
|
||||
|
||||
def step(self, xt, residual, mu, h, timestep):
|
||||
noise_t = self.sample_noise(timestep)
|
||||
dxt = 0.5 * (mu - xt - residual)
|
||||
|
|
|
|||
Loading…
Reference in New Issue