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import os
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inp_text = os.environ.get("inp_text")
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inp_wav_dir = os.environ.get("inp_wav_dir")
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exp_name = os.environ.get("exp_name")
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i_part = os.environ.get("i_part")
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all_parts = os.environ.get("all_parts")
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
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opt_dir = os.environ.get("opt_dir")
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bert_pretrained_dir = os.environ.get("bert_pretrained_dir")
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is_half = eval(os.environ.get("is_half", "True"))
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import sys, numpy as np, traceback, pdb
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import os.path
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from glob import glob
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from tqdm import tqdm
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from text.cleaner import clean_text
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import torch
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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from time import time as ttime
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import shutil
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def my_save(fea, path):
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dir = os.path.dirname(path)
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name = os.path.basename(path)
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tmp_path = "%s/%s%s.pth" % (dir, ttime(), i_part)
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torch.save(fea, tmp_path)
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shutil.move(tmp_path, "%s/%s" % (dir, name))
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txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
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if os.path.exists(txt_path) == False:
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bert_dir = "%s/3-bert" % (opt_dir)
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os.makedirs(opt_dir, exist_ok=True)
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os.makedirs(bert_dir, exist_ok=True)
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if torch.cuda.is_available():
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device = "cuda:0"
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elif torch.backends.mps.is_available():
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device = "mps"
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else:
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device = "cpu"
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tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
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if is_half == True:
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bert_model = bert_model.half().to(device)
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else:
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bert_model = bert_model.to(device)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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phone_level_feature = []
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for i in range(len(word2ph)):
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repeat_feature = res[i].repeat(word2ph[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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def process(data, res):
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for name, text, lan in data:
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try:
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name = os.path.basename(name)
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phones, word2ph, norm_text = clean_text(
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text.replace("%", "-").replace("οΏ₯", ","), lan
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)
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path_bert = "%s/%s.pt" % (bert_dir, name)
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if os.path.exists(path_bert) == False and lan == "zh":
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bert_feature = get_bert_feature(norm_text, word2ph)
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assert bert_feature.shape[-1] == len(phones)
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my_save(bert_feature, path_bert)
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phones = " ".join(phones)
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res.append([name, phones, word2ph, norm_text])
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except:
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print(name, text, traceback.format_exc())
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todo = []
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res = []
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with open(inp_text, "r", encoding="utf8") as f:
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lines = f.read().strip("\n").split("\n")
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language_v1_to_language_v2 = {
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"ZH": "zh",
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"zh": "zh",
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"JP": "ja",
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"jp": "ja",
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"JA": "ja",
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"ja": "ja",
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"EN": "en",
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"en": "en",
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"En": "en",
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}
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for line in lines[int(i_part) :: int(all_parts)]:
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try:
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wav_name, spk_name, language, text = line.split("|")
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todo.append(
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[wav_name, text, language_v1_to_language_v2.get(language, language)]
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)
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except:
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print(line, traceback.format_exc())
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process(todo, res)
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opt = []
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for name, phones, word2ph, norm_text in res:
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opt.append("%s\t%s\t%s\t%s" % (name, phones, word2ph, norm_text))
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with open(txt_path, "w", encoding="utf8") as f:
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f.write("\n".join(opt) + "\n")
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