myinfer.py

'''
runtime\python.exe myinfer.py 0 "C:\ YOUR PATH FOR THE ROOT (RVC0813Nvidia)\INPUTS_VOCAL\vocal.wav" "C:\ YOUR PATH FOR THE ROOT (RVC0813Nvidia)\logs\Hagrid.index" harvest "C:\ YOUR PATH FOR THE ROOT (RVC0813Nvidia)\INPUTS_VOCAL\test.wav" "C:\ YOUR PATH FOR THE ROOT (RVC0813Nvidia)\weights\HagridFR.pth" 0.6 cuda:0 True 5 44100 44100 1.0 1.0 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]                 
f0method = sys.argv[4]                   
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])        
tgt_sr = int(sys.argv[11])               
resample_sr = int(sys.argv[12])          
rms_mix_rate = float(sys.argv[13])       
version = sys.argv[14]                   
protect = sys.argv[15].lower() == 'false'

print(sys.argv)
config=Config(device,is_half)
now_dir=os.getcwd()
sys.path.append(now_dir)
from vc_infer_pipeline import VC
from lib.infer_pack.models import SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
from lib.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, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect):
    global 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)
    # 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_path, times, f0_up_key, f0_method, index_path, 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
    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)
    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)
wav_opt = vc_single(0, input_path, f0up_key, None, f0method, index_path, index_rate, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect)
wavfile.write(opt_path, tgt_sr, wav_opt)