File size: 7,198 Bytes
3b7b011 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 |
"""
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
now_dir = os.getcwd()
sys.path.append(now_dir)
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"assets/configs/{config_file}", "r") as f:
strr = f.read().replace("true", "false")
with open(f"assets/configs/{config_file}", "w") as f:
f.write(strr)
with open("infer/modules/train/preprocess.py", "r") as f:
strr = f.read().replace("3.7", "3.0")
with open("infer/modules/train/preprocess.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("infer/modules/train/preprocess.py", "r") as f:
strr = f.read().replace("3.7", "3.0")
with open("infer/modules/train/preprocess.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 = sys.argv[9].lower() != "false"
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 lib.infer.modules.vc.modules import VC
from lib.infer.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from lib.infer.infer_libs.audio 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)
|