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import re
import torch
import numpy as np
import torch.utils.data
from vinorm import TTSnorm
from librosa.filters import mel as librosa_mel_fn
from scipy.io.wavfile import read
MAX_WAV_VALUE = 32768.0
def remove_urls_and_links(text):
url_pattern = r"http[s]?:\/\/(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+|www\.[a-zA-Z0-9.\/]+"
markdown_image_pattern = r"!\[.*?\]\(http[s]?:\/\/.*?\)"
text = re.sub(markdown_image_pattern, '', text, 0, re.MULTILINE)
text = re.sub(url_pattern, '', text, 0, re.MULTILINE)
return text
def remove_emojis(text):
emoji_pattern = re.compile(
"["
"\U0001F600-\U0001F64F" # emoticons
"\U0001F300-\U0001F5FF" # symbols & pictographs
"\U0001F680-\U0001F6FF" # transport & map symbols
"\U0001F1E0-\U0001F1FF" # flags (iOS)
"\U00002702-\U000027B0" # other miscellaneous symbols
"\U000024C2-\U0001F251"
"\U0001F900-\U0001F9FF" # Supplemental Symbols and Pictographs
"\U0001FA70-\U0001FAFF" # Symbols and Pictographs Extended-A
"\U0001F004-\U0001F0CF" # Mahjong and Playing Cards
"]+",
flags=re.UNICODE
)
return emoji_pattern.sub(r'', text)
def remove_punc(text):
text = (text
.replace('<input>', '')
.replace("..", ".")
.replace("!.", "!")
.replace('!', ".")
.replace("?.", "?")
.replace("?", ".")
.replace(" .", ".")
.replace(" ,", ",")
.replace('"', "")
.replace("'", "")
.replace("AI", "Ây Ai")
.replace("A.I", "Ây Ai")
.replace("$", "")
.replace("(", "")
.replace(")", "")
.replace("**", "")
.replace(" = ", " bằng ")
.replace("#", "")
.replace('\\', '')
.replace('```', '')
.replace('- ', '')
.replace('+ ', '')
.replace(":", "")
.replace(",,", ",")
.replace(", ,", ",")
.replace(",.", ".")
.replace(".,", ".")
.replace("..", ".")
.replace(". .", ".")
)
text = re.sub(r'\n+', ' ', text)
text = ' '.join([t for t in text.split() if t.strip()])
text = text.strip()
return text
def normalize_text(text: str) -> str:
text = text.strip()
text = remove_urls_and_links(text)
text = remove_emojis(text)
text = remove_punc(text)
text = TTSnorm(text, lower=False)
return text
def split_text(text: str, tokenize, token_max_n=80, token_min_n=60, merge_len=20, comma_split=False):
def calc_utt_length(_text: str):
return len(tokenize(_text))
def should_merge(_text: str):
return len(tokenize(_text)) < merge_len
pounc = ['.', '?', '!', ';', ':']
if comma_split:
pounc.extend(['，', ','])
if text[-1] not in pounc:
text += "."
st = 0
utts = []
for i, c in enumerate(text):
if c in pounc:
if len(text[st: i]) > 0:
utts.append(text[st: i] + c)
if i + 1 < len(text) and text[i + 1] in ['"', '”']:
tmp = utts.pop(-1)
utts.append(tmp + text[i + 1])
st = i + 2
else:
st = i + 1
final_utts = []
cur_utt = ""
for utt in utts:
if calc_utt_length(cur_utt + utt) > token_max_n and calc_utt_length(cur_utt) > token_min_n:
final_utts.append(cur_utt)
cur_utt = ""
cur_utt = cur_utt + utt
if len(cur_utt) > 0:
if should_merge(cur_utt) and len(final_utts) != 0:
final_utts[-1] = final_utts[-1] + cur_utt
else:
final_utts.append(cur_utt)
final_utts = [utt.strip() for utt in final_utts]
return final_utts
def dynamic_range_compression(x, C=1, clip_val=1e-5):
return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
def dynamic_range_decompression(x, C=1):
return np.exp(x) / C
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
def spectral_de_normalize_torch(magnitudes):
output = dynamic_range_decompression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global mel_basis, hann_window # pylint: disable=global-statement
if f"{str(fmax)}_{str(y.device)}" not in mel_basis:
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
)
y = y.squeeze(1)
spec = torch.view_as_real(
torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[str(y.device)],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
)
spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec)
spec = spectral_normalize_torch(spec)
return spec
# def tokenize(data, get_tokenizer, allowed_special):
# """ Decode text to chars or BPE
# Inplace operation
# Args:
# data: Iterable[{key, wav, txt, sample_rate}]
# Returns:
# Iterable[{key, wav, txt, tokens, label, sample_rate}]
# """
# tokenizer = get_tokenizer()
# for sample in data:
# assert 'text' in sample
# sample['text_token'] = tokenizer.encode(sample['text'], allowed_special=allowed_special)
# sample['tts_text_token'] = tokenizer.encode(sample['tts_text'], allowed_special=allowed_special)
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