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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) | |
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, | |
**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)) | |