| ''' |
| runtime\python.exe myinfer.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" "weights/mi-test.pth" 0.6 cuda: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: |
| |
| x_pad = 3 |
| x_query = 10 |
| x_center = 60 |
| x_max = 65 |
| else: |
| |
| 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]) |
| 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 |
| 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 |
| 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,index_rate,if_f0,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] |
| if_f0=cpt.get("f0",1) |
| if(if_f0==1): |
| net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) |
| else: |
| net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) |
| del net_g.enc_q |
| print(net_g.load_state_dict(cpt["weight"], strict=False)) |
| 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] |
| |
|
|
|
|
| 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) |
|
|
|
|
