blackteay/GPT-SoVITS
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RVC-Boss 2024-01-18 00:31:02 +08:00 committed by GitHub
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1 changed files with 48 additions and 62 deletions
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34
GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py
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@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
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")
@ -9,7 +8,6 @@ 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")
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"))
@ -17,7 +15,6 @@ is_half = eval(os.environ.get("is_half", "True"))
import pdb,traceback,numpy as np,logging
from scipy.io import wavfile
import librosa,torch
now_dir = os.getcwd()
sys.path.append(now_dir)
from my_utils import load_audio
@ -35,8 +32,6 @@ from my_utils import load_audio
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)
@ -44,7 +39,6 @@ def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path
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)
@ -55,38 +49,31 @@ maxx = 0.95
alpha=0.5
device="cuda:0"
model=cnhubert.get_model()
if is_half == True:
if(is_half==True):
model=model.half().to(device)
else:
model = model.to(device)
def name2go(wav_name):
hubert_path="%s/%s.pt"%(hubert_dir,wav_name)
if os.path.exists(hubert_path):
return
if(os.path.exists(hubert_path)):return
if(inp_wav_dir!=""):
wav_path="%s/%s"%(inp_wav_dir,wav_name)
tmp_audio = load_audio(wav_path, 32000)
tmp_max = np.abs(tmp_audio).max()
if tmp_max > 2.2:
print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
return
tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + (
(1 - alpha) * 32768
) * tmp_audio
tmp_audio = librosa.resample(tmp_audio32, orig_sr=32000, target_sr=16000)
tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * tmp_audio
tmp_audio = librosa.resample(
tmp_audio32, orig_sr=32000, target_sr=16000
)
tensor_wav16 = torch.from_numpy(tmp_audio)
if is_half == True:
if (is_half == True):
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:
return
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:return
wavfile.write(
"%s/%s"%(wav32dir,wav_name),
32000,
@ -95,7 +82,6 @@ def name2go(wav_name):
# torch.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")