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| import os | |
| inp_text= os.environ.get("inp_text") | |
| exp_name= os.environ.get("exp_name") | |
| i_part= os.environ.get("i_part") | |
| all_parts= os.environ.get("all_parts") | |
| os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES") | |
| opt_dir= os.environ.get("opt_dir") | |
| pretrained_s2G= os.environ.get("pretrained_s2G") | |
| s2config_path= os.environ.get("s2config_path") | |
| is_half=eval(os.environ.get("is_half","True")) | |
| import math,traceback | |
| import multiprocessing | |
| import sys,pdb | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| from random import shuffle | |
| import torch.multiprocessing as mp | |
| from glob import glob | |
| from tqdm import tqdm | |
| import logging,librosa,utils,torch | |
| from module.models import SynthesizerTrn | |
| logging.getLogger("numba").setLevel(logging.WARNING) | |
| # from config import pretrained_s2G | |
| # inp_text=sys.argv[1] | |
| # exp_name=sys.argv[2] | |
| # i_part=sys.argv[3] | |
| # all_parts=sys.argv[4] | |
| # os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[5] | |
| # opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name | |
| hubert_dir="%s/4-cnhubert"%(opt_dir) | |
| semantic_path="%s/6-name2semantic-%s.tsv"%(opt_dir,i_part) | |
| if(os.path.exists(semantic_path)==False): | |
| os.makedirs(opt_dir,exist_ok=True) | |
| device="cuda:0" | |
| hps = utils.get_hparams_from_file(s2config_path) | |
| vq_model = SynthesizerTrn( | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model) | |
| if(is_half==True): | |
| vq_model=vq_model.half().to(device) | |
| else: | |
| vq_model = vq_model.to(device) | |
| vq_model.eval() | |
| # utils.load_checkpoint(utils.latest_checkpoint_path(hps.s2_ckpt_dir, "G_*.pth"), vq_model, None, True) | |
| # utils.load_checkpoint(pretrained_s2G, vq_model, None, True) | |
| print(vq_model.load_state_dict(torch.load(pretrained_s2G,map_location="cpu")["weight"], strict=False)) | |
| def name2go(wav_name,lines): | |
| hubert_path = "%s/%s.pt" % (hubert_dir, wav_name) | |
| if(os.path.exists(hubert_path)==False):return | |
| ssl_content = torch.load(hubert_path, map_location="cpu") | |
| if(is_half==True): | |
| ssl_content=ssl_content.half().to(device) | |
| else: | |
| ssl_content = ssl_content.to(device) | |
| codes = vq_model.extract_latent(ssl_content) | |
| semantic = " ".join([str(i) for i in codes[0, 0, :].tolist()]) | |
| lines.append("%s\t%s"%(wav_name,semantic)) | |
| with open(inp_text,"r",encoding="utf8")as f: | |
| lines=f.read().strip("\n").split("\n") | |
| lines1=[] | |
| for line in lines[int(i_part)::int(all_parts)]: | |
| # print(line) | |
| try: | |
| # wav_name,text=line.split("\t") | |
| wav_name, spk_name, language, text = line.split("|") | |
| wav_name=os.path.basename(wav_name) | |
| # name2go(name,lines1) | |
| name2go(wav_name,lines1) | |
| except: | |
| print(line,traceback.format_exc()) | |
| with open(semantic_path,"w",encoding="utf8")as f:f.write("\n".join(lines1)) | |