| import re
|
| import torch
|
| import numpy as np
|
| import torch.utils.data
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| from vinorm import TTSnorm
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| from librosa.filters import mel as librosa_mel_fn
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| from scipy.io.wavfile import read
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|
|
| MAX_WAV_VALUE = 32768.0
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|
|
|
|
| def remove_urls_and_links(text):
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| url_pattern = r"http[s]?:\/\/(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+|www\.[a-zA-Z0-9.\/]+"
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| markdown_image_pattern = r"!\[.*?\]\(http[s]?:\/\/.*?\)"
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| text = re.sub(markdown_image_pattern, '', text, 0, re.MULTILINE)
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| text = re.sub(url_pattern, '', text, 0, re.MULTILINE)
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| return text
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|
|
|
|
| def remove_emojis(text):
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| emoji_pattern = re.compile(
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| "["
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| "\U0001F600-\U0001F64F"
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| "\U0001F300-\U0001F5FF"
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| "\U0001F680-\U0001F6FF"
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| "\U0001F1E0-\U0001F1FF"
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| "\U00002702-\U000027B0"
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| "\U000024C2-\U0001F251"
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| "\U0001F900-\U0001F9FF"
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| "\U0001FA70-\U0001FAFF"
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| "\U0001F004-\U0001F0CF"
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| "]+",
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| flags=re.UNICODE
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| )
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| return emoji_pattern.sub(r'', text)
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|
|
|
|
| def remove_punc(text):
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| text = (text
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| .replace('<input>', '')
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| .replace("..", ".")
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| .replace("!.", "!")
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| .replace('!', ".")
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| .replace("?.", "?")
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| .replace("?", ".")
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| .replace(" .", ".")
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| .replace(" ,", ",")
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| .replace('"', "")
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| .replace("'", "")
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| .replace("AI", "Ây Ai")
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| .replace("A.I", "Ây Ai")
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| .replace("$", "")
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| .replace("(", "")
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| .replace(")", "")
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| .replace("**", "")
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| .replace(" = ", " bằng ")
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| .replace("#", "")
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| .replace('\\', '')
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| .replace('```', '')
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| .replace('- ', '')
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| .replace('+ ', '')
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| .replace(":", "")
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| .replace(",,", ",")
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| .replace(", ,", ",")
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| .replace(",.", ".")
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| .replace(".,", ".")
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| .replace("..", ".")
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| .replace(". .", ".")
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| )
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| text = re.sub(r'\n+', ' ', text)
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| text = ' '.join([t for t in text.split() if t.strip()])
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| text = text.strip()
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| return text
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|
|
|
|
| def normalize_text(text: str) -> str:
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| text = text.strip()
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| text = remove_urls_and_links(text)
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| text = remove_emojis(text)
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| text = remove_punc(text)
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| text = TTSnorm(text, lower=False)
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| return text
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|
|
|
|
| def split_text(text: str, tokenize, token_max_n=80, token_min_n=60, merge_len=20, comma_split=False):
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| def calc_utt_length(_text: str):
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| return len(tokenize(_text))
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|
|
| def should_merge(_text: str):
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| return len(tokenize(_text)) < merge_len
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|
|
| pounc = ['.', '?', '!', ';', ':']
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| if comma_split:
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| pounc.extend(['，', ','])
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|
|
| if text[-1] not in pounc:
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| text += "."
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|
|
| st = 0
|
| utts = []
|
| for i, c in enumerate(text):
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| if c in pounc:
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| if len(text[st: i]) > 0:
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| utts.append(text[st: i] + c)
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| if i + 1 < len(text) and text[i + 1] in ['"', '”']:
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| tmp = utts.pop(-1)
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| utts.append(tmp + text[i + 1])
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| st = i + 2
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| else:
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| st = i + 1
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|
|
| final_utts = []
|
| cur_utt = ""
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| for utt in utts:
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| if calc_utt_length(cur_utt + utt) > token_max_n and calc_utt_length(cur_utt) > token_min_n:
|
| final_utts.append(cur_utt)
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| cur_utt = ""
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| cur_utt = cur_utt + utt
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| if len(cur_utt) > 0:
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| if should_merge(cur_utt) and len(final_utts) != 0:
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| final_utts[-1] = final_utts[-1] + cur_utt
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| else:
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| final_utts.append(cur_utt)
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|
|
| final_utts = [utt.strip() for utt in final_utts]
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| return final_utts
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|
|
|
|
| def dynamic_range_compression(x, C=1, clip_val=1e-5):
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| return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
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|
|
|
|
| def dynamic_range_decompression(x, C=1):
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| return np.exp(x) / C
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|
|
|
|
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
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| return torch.log(torch.clamp(x, min=clip_val) * C)
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|
|
|
|
| def dynamic_range_decompression_torch(x, C=1):
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| return torch.exp(x) / C
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|
|
|
|
| def spectral_normalize_torch(magnitudes):
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| output = dynamic_range_compression_torch(magnitudes)
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| return output
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|
|
|
|
| def spectral_de_normalize_torch(magnitudes):
|
| output = dynamic_range_decompression_torch(magnitudes)
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| return output
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|
|
|
|
| mel_basis = {}
|
| hann_window = {}
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|
|
|
|
| def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
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| if torch.min(y) < -1.0:
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| print("min value is ", torch.min(y))
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| if torch.max(y) > 1.0:
|
| print("max value is ", torch.max(y))
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|
|
| global mel_basis, hann_window
|
| 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)
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|
|
| y = torch.nn.functional.pad(
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| 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(
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| y,
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| 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))
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|
|
| spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec)
|
| spec = spectral_normalize_torch(spec)
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|
|
| return spec
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