mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2026-06-03 20:40:30 +08:00
145 lines
5.1 KiB
Python
145 lines
5.1 KiB
Python
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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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_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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import torch
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
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version = os.environ.get("version", None)
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import os.path
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import shutil
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import traceback
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# inp_text=sys.argv[1]
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# inp_wav_dir=sys.argv[2]
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# exp_name=sys.argv[3]
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# i_part=sys.argv[4]
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# all_parts=sys.argv[5]
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# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]#i_gpu
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# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
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# bert_pretrained_dir="/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large"
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from time import time as ttime
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from text.cleaner import clean_text
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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from gsv_tools.my_utils import clean_path
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def my_save(fea, path): #####fix issue: torch.save doesn't support chinese 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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tmp_path = f"{ttime()}{i_part}.pth"
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torch.save(fea, tmp_path)
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shutil.move(tmp_path, f"{dir}/{name}")
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txt_path = f"{opt_dir}/2-name2text-{i_part}.txt"
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if not os.path.exists(txt_path):
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bert_dir = f"{opt_dir}/3-bert"
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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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if os.path.exists(bert_pretrained_dir):
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...
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else:
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raise FileNotFoundError(bert_pretrained_dir)
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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:
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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 = clean_path(name)
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name = os.path.basename(name)
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print(name)
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phones, word2ph, norm_text = clean_text(text.replace("%", "-").replace("￥", ","), lan, version)
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path_bert = f"{bert_dir}/{name}.pt"
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if not os.path.exists(path_bert) 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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# torch.save(bert_feature, path_bert)
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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])
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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, 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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"KO": "ko",
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"Ko": "ko",
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"ko": "ko",
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"yue": "yue",
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"YUE": "yue",
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"Yue": "yue",
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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([name,text,"zh"])
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if language in language_v1_to_language_v2.keys():
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todo.append([wav_name, text, language_v1_to_language_v2.get(language, language)])
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else:
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print(f"\033[33m[Waring] The {language = } of {wav_name} is not supported for training.\033[0m")
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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(f"{name}\t{phones}\t{word2ph}\t{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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