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RVC-Boss 2024-01-18 00:31:02 +08:00 committed by GitHub
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GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py
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@ -1,23 +1,20 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
import sys, os import sys,os
inp_text= os.environ.get("inp_text")
inp_text = os.environ.get("inp_text") inp_wav_dir= os.environ.get("inp_wav_dir")
inp_wav_dir = os.environ.get("inp_wav_dir") exp_name= os.environ.get("exp_name")
exp_name = os.environ.get("exp_name") i_part= os.environ.get("i_part")
i_part = os.environ.get("i_part") all_parts= os.environ.get("all_parts")
all_parts = os.environ.get("all_parts") os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
from feature_extractor import cnhubert from feature_extractor import cnhubert
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"))
opt_dir = os.environ.get("opt_dir") import pdb,traceback,numpy as np,logging
cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir")
is_half = eval(os.environ.get("is_half", "True"))
import pdb, traceback, numpy as np, logging
from scipy.io import wavfile from scipy.io import wavfile
import librosa, torch import librosa,torch
now_dir = os.getcwd() now_dir = os.getcwd()
sys.path.append(now_dir) sys.path.append(now_dir)
from my_utils import load_audio from my_utils import load_audio
@ -35,75 +32,64 @@ from my_utils import load_audio
from time import time as ttime from time import time as ttime
import shutil 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)
tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
torch.save(fea,tmp_path)
shutil.move(tmp_path,"%s/%s"%(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)
def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path maxx=0.95
dir = os.path.dirname(path) alpha=0.5
name = os.path.basename(path) device="cuda:0"
tmp_path = "%s/%s%s.pth" % (dir, ttime(), i_part) model=cnhubert.get_model()
torch.save(fea, tmp_path) if(is_half==True):
shutil.move(tmp_path, "%s/%s" % (dir, name)) model=model.half().to(device)
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
device = "cuda:0"
model = cnhubert.get_model()
if is_half == True:
model = model.half().to(device)
else: else:
model = model.to(device) model = model.to(device)
def name2go(wav_name): def name2go(wav_name):
hubert_path = "%s/%s.pt" % (hubert_dir, wav_name) hubert_path="%s/%s.pt"%(hubert_dir,wav_name)
if os.path.exists(hubert_path): if(os.path.exists(hubert_path)):return
return if(inp_wav_dir!=""):
wav_path = "%s/%s" % (inp_wav_dir, wav_name) wav_path="%s/%s"%(inp_wav_dir,wav_name)
tmp_audio = load_audio(wav_path, 32000) tmp_audio = load_audio(wav_path, 32000)
tmp_max = np.abs(tmp_audio).max() tmp_max = np.abs(tmp_audio).max()
if tmp_max > 2.2: if tmp_max > 2.2:
print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max)) print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
return return
tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ( tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * tmp_audio
(1 - alpha) * 32768 tmp_audio = librosa.resample(
) * tmp_audio tmp_audio32, orig_sr=32000, target_sr=16000
tmp_audio = librosa.resample(tmp_audio32, orig_sr=32000, target_sr=16000) )
tensor_wav16 = torch.from_numpy(tmp_audio) tensor_wav16 = torch.from_numpy(tmp_audio)
if is_half == True: if (is_half == True):
tensor_wav16 = tensor_wav16.half().to(device) tensor_wav16=tensor_wav16.half().to(device)
else: else:
tensor_wav16 = tensor_wav16.to(device) tensor_wav16 = tensor_wav16.to(device)
ssl = ( ssl=model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1,2).cpu()#torch.Size([1, 768, 215])
model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"] if np.isnan(ssl.detach().numpy()).sum()!= 0:return
.transpose(1, 2)
.cpu()
) # torch.Size([1, 768, 215])
if np.isnan(ssl.detach().numpy()).sum() != 0:
return
wavfile.write( wavfile.write(
"%s/%s" % (wav32dir, wav_name), "%s/%s"%(wav32dir,wav_name),
32000, 32000,
tmp_audio32.astype("int16"), tmp_audio32.astype("int16"),
) )
# torch.save(ssl,hubert_path ) # torch.save(ssl,hubert_path )
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")
with open(inp_text, "r", encoding="utf8") as f: for line in lines[int(i_part)::int(all_parts)]:
lines = f.read().strip("\n").split("\n")
for line in lines[int(i_part) :: int(all_parts)]:
try: try:
# wav_name,text=line.split("\t") # wav_name,text=line.split("\t")
wav_name, spk_name, language, text = line.split("|") wav_name, spk_name, language, text = line.split("|")
wav_name = os.path.basename(wav_name) wav_name=os.path.basename(wav_name)
name2go(wav_name) name2go(wav_name)
except: except:
print(line, traceback.format_exc()) print(line,traceback.format_exc())