''' v1 runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs\1111.wav" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "test.wav" "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\1111.wav" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "test_v2.wav" "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 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]#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() sys.path.append(now_dir) from vc_infer_pipeline import VC from lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, 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): 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) wav_opt=vc_single(0,input_path,f0up_key,None,f0method,index_path,index_rate) wavfile.write(opt_path, tgt_sr, wav_opt)