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liu 2023-05-06 16:48:19 +00:00 committed by huggingface-web
parent 782ca29ef5
commit 82273dda23
1 changed files with 101 additions and 24 deletions
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myinfer.py
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@ -1,12 +1,102 @@
'''
runtime\python.exe myinfer.py 0 "E:\codes\py39\RVC-beta\todo-songs\1111.wav" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "test.wav" "weights/mi-test.pth" 0.6 cuda:0 True
'''
import os,sys,pdb,torch
now_dir = os.getcwd()
sys.path.append(now_dir)
import argparse
import glob
import sys
import torch
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]
npy_path=sys.argv[4]
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])
print(sys.argv)
sys.argv=['myinfer.py']
config=Config(device,is_half)
now_dir=os.getcwd()
sys.path.append(now_dir)
from vc_infer_pipeline import VC
@ -15,19 +105,7 @@ from my_utils import load_audio
from fairseq import checkpoint_utils
from scipy.io import wavfile
# f0up_key=0
# input_path=r"E:\codes\py39\RVC-beta\todo-songs\1111.wav"
# index_path=r"E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index"
# npy_path =r"E:\codes\py39\logs\mi-test\total_fea.npy"
# opt_path ="test.wav"
# model_path="mi-test.pth"
hubert_model=None
is_half=False
device="cuda"
def load_hubert():
global hubert_model
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
@ -37,8 +115,7 @@ def load_hubert():
else:hubert_model = hubert_model.float()
hubert_model.eval()
def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_npy,index_rate):#spk_item, input_audio0, vc_transform0,f0_file,f0method0
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
if input_audio is None:return "You need to upload an audio", None
f0_up_key = int(f0_up_key)
@ -46,16 +123,16 @@ def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_np
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,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,times,f0_up_key,f0_method,file_index,index_rate,if_f0,f0_file=f0_file)
print(times)
return audio_opt
def get_vc(sid):
global n_spk,tgt_sr,net_g,vc,cpt
person = "weights/%s" % (sid)
print("loading %s"%person)
cpt = torch.load(person, map_location="cpu")
def get_vc(model_path):
global n_spk,tgt_sr,net_g,vc,cpt,device,is_half
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)
@ -68,12 +145,12 @@ def get_vc(sid):
net_g.eval().to(device)
if (is_half):net_g = net_g.half()
else:net_g = net_g.float()
vc = VC(tgt_sr, device, is_half)
vc = VC(tgt_sr, config)
n_spk=cpt["config"][-3]
# return {"visible": True,"maximum": n_spk, "__type__": "update"}
get_vc(model_path)
wav_opt=vc_single(0,input_path,f0up_key,None,"harvest",index_path,npy_path,0.6)
wav_opt=vc_single(0,input_path,f0up_key,None,f0method,index_path,index_rate)
wavfile.write(opt_path, tgt_sr, wav_opt)