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") version=os.environ.get("version","v2") import torch is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() 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 from module.models import SynthesizerTrn from tools.my_utils import clean_path 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 if os.path.exists(pretrained_s2G):... else:raise FileNotFoundError(pretrained_s2G) 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) if torch.cuda.is_available(): device = "cuda" # elif torch.backends.mps.is_available(): # device = "mps" else: device = "cpu" 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, version=version, **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=clean_path(wav_name) 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))