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Retrieval-based-Voice-Conversion-WebUI
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120
infer_batch_rvc.py
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@ -1,10 +1,11 @@
'''
"""
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
"""
import os, sys, pdb, torch
now_dir = os.getcwd()
sys.path.append(now_dir)
import argparse
@ -13,8 +14,10 @@ import sys
import torch
import tqdm as tq
from multiprocessing import cpu_count
class Config:
def __init__(self,device,is_half):
def __init__(self, device, is_half):
self.device = device
self.is_half = is_half
self.n_cpu = 0
@ -90,22 +93,23 @@ class Config:
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])
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()
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 (
@ -118,37 +122,66 @@ from my_utils import load_audio
from fairseq import checkpoint_utils
from scipy.io import wavfile
hubert_model=None
hubert_model = None
def load_hubert():
global hubert_model
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
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()
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
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)
audio = load_audio(input_audio, 16000)
times = [0, 0, 0]
if(hubert_model==None):load_hubert()
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)
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)
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)
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:
@ -156,26 +189,29 @@ def get_vc(model_path):
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1:#
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()
if is_half:
net_g = net_g.half()
else:
net_g = net_g.float()
vc = VC(tgt_sr, config)
n_spk=cpt["config"][-3]
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
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)