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infer_cli.py
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infer_cli.py
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import os,sys,pdb,torch
now_dir = os.getcwd()
sys.path.append(now_dir)
import argparse
import glob
import sys
import torch
import numpy as np
from multiprocessing import cpu_count
####
#USAGE
#
#In your Terminal or CMD or whatever
#python infer_cli.py [TRANSPOSE_VALUE] "[INPUT_PATH]" "[OUTPUT_PATH]" "[MODEL_PATH]" "[INDEX_FILE_PATH]" "[INFERENCE_DEVICE]" "[METHOD]"
using_cli = False
device = "cuda:0"
is_half = False
if(len(sys.argv) > 0):
f0_up_key=int(sys.argv[1]) #transpose value
input_path=sys.argv[2]
output_path=sys.argv[3]
model_path=sys.argv[4]
file_index=sys.argv[5] #.index file
device=sys.argv[6]
f0_method=sys.argv[7] #pm or harvest or crepe
using_cli = True
#file_index2=sys.argv[8]
#index_rate=float(sys.argv[10]) #search feature ratio
#filter_radius=float(sys.argv[11]) #median filter
#resample_sr=float(sys.argv[12]) #resample audio in post processing
#rms_mix_rate=float(sys.argv[13]) #search feature
print(sys.argv)
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() and device != "cpu":
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 = False
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
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
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, _, _ = checkpoint_utils.load_model_ensemble_and_task(
["hubert_base.pt"],
suffix="",
)
hubert_model = models[0]
hubert_model = hubert_model.to(config.device)
if config.is_half:
hubert_model = hubert_model.half()
else:
hubert_model = hubert_model.float()
hubert_model.eval()
def vc_single(
sid=0,
input_audio_path=None,
f0_up_key=0,
f0_file=None,
f0_method="pm",
file_index="", #.index file
file_index2="",
# file_big_npy,
index_rate=1.0,
filter_radius=3,
resample_sr=0,
rms_mix_rate=1.0,
model_path="",
output_path="",
protect=0.33
):
global tgt_sr, net_g, vc, hubert_model, version
get_vc(model_path)
if input_audio_path is None:
return "You need to upload an audio file", None
f0_up_key = int(f0_up_key)
audio = load_audio(input_audio_path, 16000)
audio_max = np.abs(audio).max() / 0.95
if audio_max > 1:
audio /= audio_max
times = [0, 0, 0]
if hubert_model == None:
load_hubert()
if_f0 = cpt.get("f0", 1)
file_index = (
(
file_index.strip(" ")
.strip('"')
.strip("\n")
.strip('"')
.strip(" ")
.replace("trained", "added")
)
if file_index != ""
else file_index2
)
audio_opt = vc.pipeline(
hubert_model,
net_g,
sid,
audio,
input_audio_path,
times,
f0_up_key,
f0_method,
file_index,
# file_big_npy,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
f0_file=f0_file,
protect=protect
)
wavfile.write(output_path, tgt_sr, audio_opt)
return('processed')
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(if_f0==1):
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
del net_g.enc_q
print(net_g.load_state_dict(cpt["weight"], strict=False))
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"}
if(using_cli):
vc_single(sid=0,input_audio_path=input_path,f0_up_key=f0_up_key,f0_file=None,f0_method=f0_method,file_index=file_index,file_index2="",index_rate=1,filter_radius=3,resample_sr=0,rms_mix_rate=0,model_path=model_path,output_path=output_path)