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more code refactor

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This commit is contained in:
Blaise
2024-01-16 17:14:18 +01:00
parent 0d92575115
commit 0d3d47f3c3
44 changed files with 4516 additions and 2623 deletions
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79
GPT_SoVITS/prepare_datasets/3-get-semantic.py
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@@ -1,24 +1,27 @@
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
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
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
import logging, librosa, utils, torch
from module.models import SynthesizerTrn
logging.getLogger("numba").setLevel(logging.WARNING)
# from config import pretrained_s2G
@@ -30,52 +33,58 @@ logging.getLogger("numba").setLevel(logging.WARNING)
# 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)
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"
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)
**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))
print(
vq_model.load_state_dict(
torch.load(pretrained_s2G, map_location="cpu")["weight"], strict=False
)
)
def name2go(wav_name,lines):
def name2go(wav_name, lines):
hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
if(os.path.exists(hubert_path)==False):return
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)
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))
lines.append("%s\t%s" % (wav_name, semantic))
with open(inp_text,"r",encoding="utf8")as f:
lines=f.read().strip("\n").split("\n")
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)]:
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)
wav_name = os.path.basename(wav_name)
# name2go(name,lines1)
name2go(wav_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))
print(line, traceback.format_exc())
with open(semantic_path, "w", encoding="utf8") as f:
f.write("\n".join(lines1))