mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-01-31 18:52:53 +08:00
红血球AE3803
GitHub
parent
c74727d487
commit
4de5d0d551
181
infer_batch_rvc.py
Normal file
181
infer_batch_rvc.py
Normal file
@ -0,0 +1,181 @@
|
||||
'''
|
||||
v1
|
||||
runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
|
||||
v2
|
||||
runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
|
||||
'''
|
||||
import os,sys,pdb,torch
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
import argparse
|
||||
import glob
|
||||
import sys
|
||||
import torch
|
||||
import tqdm as tq
|
||||
from multiprocessing import cpu_count
|
||||
class Config:
|
||||
def __init__(self,device,is_half):
|
||||
self.device = device
|
||||
self.is_half = is_half
|
||||
self.n_cpu = 0
|
||||
self.gpu_name = None
|
||||
self.gpu_mem = None
|
||||
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
|
||||
|
||||
def device_config(self) -> tuple:
|
||||
if torch.cuda.is_available():
|
||||
i_device = int(self.device.split(":")[-1])
|
||||
self.gpu_name = torch.cuda.get_device_name(i_device)
|
||||
if (
|
||||
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
|
||||
or "P40" in self.gpu_name.upper()
|
||||
or "1060" in self.gpu_name
|
||||
or "1070" in self.gpu_name
|
||||
or "1080" in self.gpu_name
|
||||
):
|
||||
print("16系/10系显卡和P40强制单精度")
|
||||
self.is_half = False
|
||||
for config_file in ["32k.json", "40k.json", "48k.json"]:
|
||||
with open(f"configs/{config_file}", "r") as f:
|
||||
strr = f.read().replace("true", "false")
|
||||
with open(f"configs/{config_file}", "w") as f:
|
||||
f.write(strr)
|
||||
with open("trainset_preprocess_pipeline_print.py", "r") as f:
|
||||
strr = f.read().replace("3.7", "3.0")
|
||||
with open("trainset_preprocess_pipeline_print.py", "w") as f:
|
||||
f.write(strr)
|
||||
else:
|
||||
self.gpu_name = None
|
||||
self.gpu_mem = int(
|
||||
torch.cuda.get_device_properties(i_device).total_memory
|
||||
/ 1024
|
||||
/ 1024
|
||||
/ 1024
|
||||
+ 0.4
|
||||
)
|
||||
if self.gpu_mem <= 4:
|
||||
with open("trainset_preprocess_pipeline_print.py", "r") as f:
|
||||
strr = f.read().replace("3.7", "3.0")
|
||||
with open("trainset_preprocess_pipeline_print.py", "w") as f:
|
||||
f.write(strr)
|
||||
elif torch.backends.mps.is_available():
|
||||
print("没有发现支持的N卡, 使用MPS进行推理")
|
||||
self.device = "mps"
|
||||
else:
|
||||
print("没有发现支持的N卡, 使用CPU进行推理")
|
||||
self.device = "cpu"
|
||||
self.is_half = True
|
||||
|
||||
if self.n_cpu == 0:
|
||||
self.n_cpu = cpu_count()
|
||||
|
||||
if self.is_half:
|
||||
# 6G显存配置
|
||||
x_pad = 3
|
||||
x_query = 10
|
||||
x_center = 60
|
||||
x_max = 65
|
||||
else:
|
||||
# 5G显存配置
|
||||
x_pad = 1
|
||||
x_query = 6
|
||||
x_center = 38
|
||||
x_max = 41
|
||||
|
||||
if self.gpu_mem != None and self.gpu_mem <= 4:
|
||||
x_pad = 1
|
||||
x_query = 5
|
||||
x_center = 30
|
||||
x_max = 32
|
||||
|
||||
return x_pad, x_query, x_center, x_max
|
||||
|
||||
f0up_key=sys.argv[1]
|
||||
input_path=sys.argv[2]
|
||||
index_path=sys.argv[3]
|
||||
f0method=sys.argv[4]#harvest or pm
|
||||
opt_path=sys.argv[5]
|
||||
model_path=sys.argv[6]
|
||||
index_rate=float(sys.argv[7])
|
||||
device=sys.argv[8]
|
||||
is_half=bool(sys.argv[9])
|
||||
filter_radius=int(sys.argv[10])
|
||||
resample_sr=int(sys.argv[11])
|
||||
rms_mix_rate=float(sys.argv[12])
|
||||
protect=float(sys.argv[13])
|
||||
print(sys.argv)
|
||||
config=Config(device,is_half)
|
||||
now_dir=os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
from vc_infer_pipeline import VC
|
||||
from infer_pack.models import (
|
||||
SynthesizerTrnMs256NSFsid,
|
||||
SynthesizerTrnMs256NSFsid_nono,
|
||||
SynthesizerTrnMs768NSFsid,
|
||||
SynthesizerTrnMs768NSFsid_nono,
|
||||
)
|
||||
from my_utils import load_audio
|
||||
from fairseq import checkpoint_utils
|
||||
from scipy.io import wavfile
|
||||
|
||||
hubert_model=None
|
||||
def load_hubert():
|
||||
global hubert_model
|
||||
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
|
||||
hubert_model = models[0]
|
||||
hubert_model = hubert_model.to(device)
|
||||
if(is_half):hubert_model = hubert_model.half()
|
||||
else:hubert_model = hubert_model.float()
|
||||
hubert_model.eval()
|
||||
|
||||
def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,index_rate):
|
||||
global tgt_sr,net_g,vc,hubert_model,version
|
||||
if input_audio is None:return "You need to upload an audio", None
|
||||
f0_up_key = int(f0_up_key)
|
||||
audio=load_audio(input_audio,16000)
|
||||
times = [0, 0, 0]
|
||||
if(hubert_model==None):load_hubert()
|
||||
if_f0 = cpt.get("f0", 1)
|
||||
# audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
|
||||
audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,input_audio,times,f0_up_key,f0_method,file_index,index_rate,if_f0,filter_radius,tgt_sr,resample_sr,rms_mix_rate,version,protect,f0_file=f0_file)
|
||||
print(times)
|
||||
return audio_opt
|
||||
|
||||
|
||||
def get_vc(model_path):
|
||||
global n_spk,tgt_sr,net_g,vc,cpt,device,is_half,version
|
||||
print("loading pth %s"%model_path)
|
||||
cpt = torch.load(model_path, map_location="cpu")
|
||||
tgt_sr = cpt["config"][-1]
|
||||
cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk
|
||||
if_f0=cpt.get("f0",1)
|
||||
version = cpt.get("version", "v1")
|
||||
if version == "v1":
|
||||
if if_f0 == 1:
|
||||
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
|
||||
else:
|
||||
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
|
||||
elif version == "v2":
|
||||
if if_f0 == 1:#
|
||||
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
|
||||
else:
|
||||
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
|
||||
del net_g.enc_q
|
||||
print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净，真奇葩
|
||||
net_g.eval().to(device)
|
||||
if (is_half):net_g = net_g.half()
|
||||
else:net_g = net_g.float()
|
||||
vc = VC(tgt_sr, config)
|
||||
n_spk=cpt["config"][-3]
|
||||
# return {"visible": True,"maximum": n_spk, "__type__": "update"}
|
||||
|
||||
|
||||
get_vc(model_path)
|
||||
audios = os.listdir(input_path)
|
||||
for file in tq.tqdm(audios):
|
||||
if file.endswith('.wav'):
|
||||
file_path = input_path + '/' + file
|
||||
wav_opt=vc_single(0,file_path,f0up_key,None,f0method,index_path,index_rate)
|
||||
out_path = opt_path + '/' + file
|
||||
wavfile.write(out_path, tgt_sr, wav_opt)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user