mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 04:22:46 +08:00
Reformat scripts
This commit is contained in:
laubonghaudoi
parent
165882d64f
commit
c18c9a9eed
3
.gitignore
vendored
3
.gitignore
vendored
@ -17,4 +17,5 @@ SoVITS_weights_v3
|
||||
TEMP
|
||||
weight.json
|
||||
ffmpeg*
|
||||
ffprobe*
|
||||
ffprobe*
|
||||
zoengjyutgaai*
|
||||
@ -1,6 +1,17 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import os
|
||||
import os.path
|
||||
import shutil
|
||||
import traceback
|
||||
from time import time as ttime
|
||||
|
||||
import torch
|
||||
from text.cleaner import clean_text
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
from tqdm import tqdm
|
||||
|
||||
from tools.my_utils import clean_path
|
||||
|
||||
inp_text = os.environ.get("inp_text")
|
||||
inp_wav_dir = os.environ.get("inp_wav_dir")
|
||||
@ -8,20 +19,11 @@ exp_name = os.environ.get("exp_name")
|
||||
i_part = os.environ.get("i_part")
|
||||
all_parts = os.environ.get("all_parts")
|
||||
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||||
opt_dir = os.environ.get("opt_dir")
|
||||
bert_pretrained_dir = os.environ.get("bert_pretrained_dir")
|
||||
import torch
|
||||
is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
|
||||
version = os.environ.get('version', None)
|
||||
import sys, numpy as np, traceback, pdb
|
||||
import os.path
|
||||
from glob import glob
|
||||
from tqdm import tqdm
|
||||
from text.cleaner import clean_text
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
import numpy as np
|
||||
from tools.my_utils import clean_path
|
||||
|
||||
# inp_text=sys.argv[1]
|
||||
# inp_wav_dir=sys.argv[2]
|
||||
@ -32,17 +34,14 @@ from tools.my_utils import clean_path
|
||||
# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
|
||||
# bert_pretrained_dir="/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large"
|
||||
|
||||
from time import time as ttime
|
||||
import shutil
|
||||
|
||||
|
||||
def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
|
||||
dir=os.path.dirname(path)
|
||||
name=os.path.basename(path)
|
||||
def my_save(fea, path): # fix issue: torch.save doesn't support chinese path
|
||||
dir = os.path.dirname(path)
|
||||
name = os.path.basename(path)
|
||||
# tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
|
||||
tmp_path="%s%s.pth"%(ttime(),i_part)
|
||||
torch.save(fea,tmp_path)
|
||||
shutil.move(tmp_path,"%s/%s"%(dir,name))
|
||||
tmp_path = "%s%s.pth" % (ttime(), i_part)
|
||||
torch.save(fea, tmp_path)
|
||||
shutil.move(tmp_path, "%s/%s" % (dir, name))
|
||||
|
||||
|
||||
txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
|
||||
@ -56,11 +55,13 @@ if os.path.exists(txt_path) == False:
|
||||
# device = "mps"
|
||||
else:
|
||||
device = "cpu"
|
||||
if os.path.exists(bert_pretrained_dir):...
|
||||
else:raise FileNotFoundError(bert_pretrained_dir)
|
||||
if os.path.exists(bert_pretrained_dir):
|
||||
...
|
||||
else:
|
||||
raise FileNotFoundError(bert_pretrained_dir)
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
|
||||
if is_half == True:
|
||||
if is_half:
|
||||
bert_model = bert_model.half().to(device)
|
||||
else:
|
||||
bert_model = bert_model.to(device)
|
||||
@ -86,7 +87,7 @@ if os.path.exists(txt_path) == False:
|
||||
def process(data, res):
|
||||
for name, text, lan in data:
|
||||
try:
|
||||
name=clean_path(name)
|
||||
name = clean_path(name)
|
||||
name = os.path.basename(name)
|
||||
print(name)
|
||||
phones, word2ph, norm_text = clean_text(
|
||||
@ -126,7 +127,7 @@ if os.path.exists(txt_path) == False:
|
||||
"YUE": "yue",
|
||||
"Yue": "yue",
|
||||
}
|
||||
for line in lines[int(i_part) :: int(all_parts)]:
|
||||
for line in tqdm(lines[int(i_part):: int(all_parts)]):
|
||||
try:
|
||||
wav_name, spk_name, language, text = line.split("|")
|
||||
# todo.append([name,text,"zh"])
|
||||
|
||||
@ -1,25 +1,40 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Step 2 of data preparation: Extract HuBERT features from the audio files,
|
||||
and resample the audio to 32kHz and saving it.
|
||||
"""
|
||||
|
||||
import sys,os
|
||||
inp_text= os.environ.get("inp_text")
|
||||
inp_wav_dir= os.environ.get("inp_wav_dir")
|
||||
exp_name= os.environ.get("exp_name")
|
||||
i_part= os.environ.get("i_part")
|
||||
all_parts= os.environ.get("all_parts")
|
||||
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||||
from feature_extractor import cnhubert
|
||||
opt_dir= os.environ.get("opt_dir")
|
||||
cnhubert.cnhubert_base_path= os.environ.get("cnhubert_base_dir")
|
||||
import os
|
||||
import shutil
|
||||
import sys
|
||||
import traceback
|
||||
from time import time as ttime
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import torch
|
||||
from feature_extractor import cnhubert
|
||||
from scipy.io import wavfile
|
||||
from tqdm import tqdm
|
||||
|
||||
from tools.my_utils import clean_path, load_audio
|
||||
|
||||
inp_text = os.environ.get("inp_text")
|
||||
inp_wav_dir = os.environ.get("inp_wav_dir")
|
||||
exp_name = os.environ.get("exp_name")
|
||||
i_part = os.environ.get("i_part")
|
||||
all_parts = os.environ.get("all_parts")
|
||||
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||||
|
||||
opt_dir = os.environ.get("opt_dir")
|
||||
cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir")
|
||||
|
||||
is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
|
||||
|
||||
import pdb,traceback,numpy as np,logging
|
||||
from scipy.io import wavfile
|
||||
import librosa
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
from tools.my_utils import load_audio,clean_path
|
||||
|
||||
|
||||
# from config import cnhubert_base_path
|
||||
# cnhubert.cnhubert_base_path=cnhubert_base_path
|
||||
@ -32,92 +47,112 @@ from tools.my_utils import load_audio,clean_path
|
||||
# cnhubert.cnhubert_base_path=sys.argv[7]
|
||||
# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
|
||||
|
||||
from time import time as ttime
|
||||
import shutil
|
||||
def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
|
||||
dir=os.path.dirname(path)
|
||||
name=os.path.basename(path)
|
||||
def my_save(fea, path): # fix issue: torch.save doesn't support chinese path
|
||||
dir = os.path.dirname(path)
|
||||
name = os.path.basename(path)
|
||||
# tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
|
||||
tmp_path="%s%s.pth"%(ttime(),i_part)
|
||||
torch.save(fea,tmp_path)
|
||||
shutil.move(tmp_path,"%s/%s"%(dir,name))
|
||||
tmp_path = f"{ttime()}{i_part}.pth"
|
||||
torch.save(fea, tmp_path)
|
||||
shutil.move(tmp_path, f"{dir}/{name}")
|
||||
|
||||
hubert_dir="%s/4-cnhubert"%(opt_dir)
|
||||
wav32dir="%s/5-wav32k"%(opt_dir)
|
||||
os.makedirs(opt_dir,exist_ok=True)
|
||||
os.makedirs(hubert_dir,exist_ok=True)
|
||||
os.makedirs(wav32dir,exist_ok=True)
|
||||
|
||||
maxx=0.95
|
||||
alpha=0.5
|
||||
hubert_dir = "%s/4-cnhubert" % (opt_dir)
|
||||
wav32dir = "%s/5-wav32k" % (opt_dir)
|
||||
os.makedirs(opt_dir, exist_ok=True)
|
||||
os.makedirs(hubert_dir, exist_ok=True)
|
||||
os.makedirs(wav32dir, exist_ok=True)
|
||||
|
||||
maxx = 0.95
|
||||
alpha = 0.5
|
||||
if torch.cuda.is_available():
|
||||
device = "cuda:0"
|
||||
# elif torch.backends.mps.is_available():
|
||||
# device = "mps"
|
||||
else:
|
||||
device = "cpu"
|
||||
model=cnhubert.get_model()
|
||||
|
||||
|
||||
model = cnhubert.get_model()
|
||||
# is_half=False
|
||||
if(is_half==True):
|
||||
model=model.half().to(device)
|
||||
if (is_half == True):
|
||||
model = model.half().to(device)
|
||||
else:
|
||||
model = model.to(device)
|
||||
|
||||
nan_fails=[]
|
||||
def name2go(wav_name,wav_path):
|
||||
hubert_path="%s/%s.pt"%(hubert_dir,wav_name)
|
||||
if(os.path.exists(hubert_path)):return
|
||||
tmp_audio = load_audio(wav_path, 32000)
|
||||
tmp_max = np.abs(tmp_audio).max()
|
||||
if tmp_max > 2.2:
|
||||
print("%s-filtered,%s" % (wav_name, tmp_max))
|
||||
nan_fails = []
|
||||
|
||||
|
||||
def name2go(wav_name, wav_path):
|
||||
"""
|
||||
Extract HuBERT features from the audio files, and resample the audio to 32kHz and saving it.
|
||||
"""
|
||||
# Skip if the file already exists
|
||||
hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
|
||||
if (os.path.exists(hubert_path)):
|
||||
return
|
||||
tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * tmp_audio
|
||||
tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha*1145.14)) + ((1 - alpha)*1145.14) * tmp_audio
|
||||
|
||||
# Load the audio file in 32kHz sampling rate
|
||||
tmp_audio = load_audio(wav_path, 32000)
|
||||
|
||||
# Check the maximum amplitude of the audio file
|
||||
tmp_max = np.abs(tmp_audio).max()
|
||||
# Skip if the maximum amplitude is too high (volume is too loud)
|
||||
if tmp_max > 2.2:
|
||||
print(f"{wav_name}-filtered,{tmp_max}")
|
||||
return
|
||||
# Normalize the audio
|
||||
tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio
|
||||
tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
|
||||
tmp_audio = librosa.resample(
|
||||
tmp_audio32b, orig_sr=32000, target_sr=16000
|
||||
)#不是重采样问题
|
||||
) # 不是重采样问题
|
||||
tensor_wav16 = torch.from_numpy(tmp_audio)
|
||||
if (is_half == True):
|
||||
tensor_wav16=tensor_wav16.half().to(device)
|
||||
|
||||
# if half-precision is enabled, convert the tensor to half-precision
|
||||
if is_half:
|
||||
tensor_wav16 = tensor_wav16.half().to(device)
|
||||
else:
|
||||
tensor_wav16 = tensor_wav16.to(device)
|
||||
ssl=model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1,2).cpu()#torch.Size([1, 768, 215])
|
||||
if np.isnan(ssl.detach().numpy()).sum()!= 0:
|
||||
nan_fails.append((wav_name,wav_path))
|
||||
print("nan filtered:%s"%wav_name)
|
||||
|
||||
# Extract HuBERT features from the audio file
|
||||
ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu() # torch.Size([1, 768, 215])
|
||||
|
||||
if np.isnan(ssl.detach().numpy()).sum() != 0:
|
||||
nan_fails.append((wav_name, wav_path))
|
||||
print(f"nan filtered:{wav_name}")
|
||||
return
|
||||
wavfile.write(
|
||||
"%s/%s"%(wav32dir,wav_name),
|
||||
f"{wav32dir}/{wav_name}",
|
||||
32000,
|
||||
tmp_audio32.astype("int16"),
|
||||
)
|
||||
my_save(ssl,hubert_path)
|
||||
my_save(ssl, hubert_path)
|
||||
|
||||
with open(inp_text,"r",encoding="utf8")as f:
|
||||
lines=f.read().strip("\n").split("\n")
|
||||
|
||||
for line in lines[int(i_part)::int(all_parts)]:
|
||||
with open(inp_text, "r", encoding="utf8")as f:
|
||||
lines = f.read().strip("\n").split("\n")
|
||||
|
||||
for line in tqdm(lines[int(i_part)::int(all_parts)]):
|
||||
try:
|
||||
# wav_name,text=line.split("\t")
|
||||
wav_name, spk_name, language, text = line.split("|")
|
||||
wav_name=clean_path(wav_name)
|
||||
if (inp_wav_dir != "" and inp_wav_dir != None):
|
||||
wav_name = clean_path(wav_name)
|
||||
if (inp_wav_dir != "" and inp_wav_dir is not None):
|
||||
wav_name = os.path.basename(wav_name)
|
||||
wav_path = "%s/%s"%(inp_wav_dir, wav_name)
|
||||
wav_path = f"{inp_wav_dir}/{wav_name}"
|
||||
|
||||
else:
|
||||
wav_path=wav_name
|
||||
wav_path = wav_name
|
||||
wav_name = os.path.basename(wav_name)
|
||||
name2go(wav_name,wav_path)
|
||||
name2go(wav_name, wav_path)
|
||||
except:
|
||||
print(line,traceback.format_exc())
|
||||
print(line, traceback.format_exc())
|
||||
|
||||
if(len(nan_fails)>0 and is_half==True):
|
||||
is_half=False
|
||||
model=model.float()
|
||||
if (len(nan_fails) > 0 and is_half):
|
||||
is_half = False
|
||||
model = model.float()
|
||||
for wav in nan_fails:
|
||||
try:
|
||||
name2go(wav[0],wav[1])
|
||||
name2go(wav[0], wav[1])
|
||||
except:
|
||||
print(wav_name,traceback.format_exc())
|
||||
print(wav_name, traceback.format_exc())
|
||||
|
||||
@ -1,17 +1,27 @@
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import torch
|
||||
import utils
|
||||
|
||||
from tools.my_utils import clean_path
|
||||
|
||||
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")
|
||||
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||||
opt_dir = os.environ.get("opt_dir")
|
||||
pretrained_s2G = os.environ.get("pretrained_s2G")
|
||||
s2config_path = os.environ.get("s2config_path")
|
||||
|
||||
if os.path.exists(pretrained_s2G):...
|
||||
else:raise FileNotFoundError(pretrained_s2G)
|
||||
if os.path.exists(pretrained_s2G):
|
||||
...
|
||||
else:
|
||||
raise FileNotFoundError(pretrained_s2G)
|
||||
# version=os.environ.get("version","v2")
|
||||
size = os.path.getsize(pretrained_s2G)
|
||||
if size < 82978 * 1024:
|
||||
@ -24,24 +34,14 @@ elif size < 700 * 1024 * 1024:
|
||||
version = "v2"
|
||||
else:
|
||||
version = "v3"
|
||||
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
|
||||
if version!="v3":
|
||||
if version != "v3":
|
||||
from module.models import SynthesizerTrn
|
||||
else:
|
||||
from module.models import SynthesizerTrnV3 as SynthesizerTrn
|
||||
from tools.my_utils import clean_path
|
||||
logging.getLogger("numba").setLevel(logging.WARNING)
|
||||
# from config import pretrained_s2G
|
||||
|
||||
@ -102,12 +102,12 @@ if os.path.exists(semantic_path) == False:
|
||||
lines = f.read().strip("\n").split("\n")
|
||||
|
||||
lines1 = []
|
||||
for line in lines[int(i_part) :: int(all_parts)]:
|
||||
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 = clean_path(wav_name)
|
||||
wav_name = os.path.basename(wav_name)
|
||||
# name2go(name,lines1)
|
||||
name2go(wav_name, lines1)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user