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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
from multiprocessing import cpu_count
import ffmpeg
import numpy as np
def load_audio(file, sr):
try:
# https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
file = (
file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
) # 防止小白拷路径头尾带了空格和"和回车
out, _ = (
ffmpeg.input(file, threads=0)
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except Exception as e:
raise RuntimeError(f"Failed to load audio: {e}")
return np.frombuffer(out, np.float32).flatten()
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
now_dir=os.getcwd()
sys.path.append(now_dir)
sys.path.append(os.path.join(now_dir,"Retrieval-based-Voice-Conversion-WebUI"))
from vc_infer_pipeline import VC
from lib.infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
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,filter_radius=3,resample_sr=48000,rms_mix_rate=0.25, protect=0.33):
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)
audio=load_audio(input_audio,16000)
times = [0, 0, 0]
if(hubert_model==None):load_hubert()
if_f0 = cpt.get("f0", 1)
version = cpt.get("version")
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=filter_radius,tgt_sr=tgt_sr,resample_sr=resample_sr,rms_mix_rate=rms_mix_rate,version=version,protect=protect,f0_file=f0_file)
# print(times)
return audio_opt
def get_vc(model_path, device_, is_half_):
global n_spk,tgt_sr,net_g,vc,cpt,device,is_half
device = device_
is_half = is_half_
config = Config(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)
version=cpt.get("version", "v2")
if(if_f0==1):
if version == "v1":
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
else:
if version == "v1":
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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]
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