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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
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红血球AE3803
GitHub
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181
infer_batch_rvc.py
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181
infer_batch_rvc.py
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'''
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v1
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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
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v2
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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
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'''
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import os,sys,pdb,torch
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import argparse
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import glob
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import sys
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import torch
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import tqdm as tq
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from multiprocessing import cpu_count
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class Config:
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def __init__(self,device,is_half):
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self.device = device
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self.is_half = is_half
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self.n_cpu = 0
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self.gpu_name = None
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self.gpu_mem = None
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
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def device_config(self) -> tuple:
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if torch.cuda.is_available():
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i_device = int(self.device.split(":")[-1])
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self.gpu_name = torch.cuda.get_device_name(i_device)
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if (
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("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
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or "P40" in self.gpu_name.upper()
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or "1060" in self.gpu_name
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or "1070" in self.gpu_name
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or "1080" in self.gpu_name
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):
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print("16系/10系显卡和P40强制单精度")
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self.is_half = False
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for config_file in ["32k.json", "40k.json", "48k.json"]:
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with open(f"configs/{config_file}", "r") as f:
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strr = f.read().replace("true", "false")
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with open(f"configs/{config_file}", "w") as f:
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f.write(strr)
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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else:
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self.gpu_name = None
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self.gpu_mem = int(
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torch.cuda.get_device_properties(i_device).total_memory
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/ 1024
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/ 1024
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/ 1024
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+ 0.4
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)
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if self.gpu_mem <= 4:
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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elif torch.backends.mps.is_available():
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print("没有发现支持的N卡, 使用MPS进行推理")
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self.device = "mps"
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else:
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print("没有发现支持的N卡, 使用CPU进行推理")
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self.device = "cpu"
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self.is_half = True
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if self.n_cpu == 0:
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self.n_cpu = cpu_count()
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if self.is_half:
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# 6G显存配置
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x_pad = 3
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x_query = 10
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x_center = 60
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x_max = 65
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else:
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# 5G显存配置
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x_pad = 1
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x_query = 6
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x_center = 38
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x_max = 41
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if self.gpu_mem != None and self.gpu_mem <= 4:
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x_pad = 1
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x_query = 5
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x_center = 30
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x_max = 32
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return x_pad, x_query, x_center, x_max
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f0up_key=sys.argv[1]
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input_path=sys.argv[2]
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index_path=sys.argv[3]
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f0method=sys.argv[4]#harvest or pm
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opt_path=sys.argv[5]
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model_path=sys.argv[6]
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index_rate=float(sys.argv[7])
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device=sys.argv[8]
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is_half=bool(sys.argv[9])
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filter_radius=int(sys.argv[10])
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resample_sr=int(sys.argv[11])
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rms_mix_rate=float(sys.argv[12])
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protect=float(sys.argv[13])
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print(sys.argv)
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config=Config(device,is_half)
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now_dir=os.getcwd()
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sys.path.append(now_dir)
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from vc_infer_pipeline import VC
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from infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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from my_utils import load_audio
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from fairseq import checkpoint_utils
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from scipy.io import wavfile
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hubert_model=None
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def load_hubert():
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global hubert_model
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
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hubert_model = models[0]
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hubert_model = hubert_model.to(device)
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if(is_half):hubert_model = hubert_model.half()
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else:hubert_model = hubert_model.float()
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hubert_model.eval()
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def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,index_rate):
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global tgt_sr,net_g,vc,hubert_model,version
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if input_audio is None:return "You need to upload an audio", None
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f0_up_key = int(f0_up_key)
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audio=load_audio(input_audio,16000)
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times = [0, 0, 0]
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if(hubert_model==None):load_hubert()
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if_f0 = cpt.get("f0", 1)
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# 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)
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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)
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print(times)
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return audio_opt
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def get_vc(model_path):
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global n_spk,tgt_sr,net_g,vc,cpt,device,is_half,version
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print("loading pth %s"%model_path)
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cpt = torch.load(model_path, map_location="cpu")
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk
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if_f0=cpt.get("f0",1)
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version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif version == "v2":
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if if_f0 == 1:#
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g.enc_q
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print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净，真奇葩
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net_g.eval().to(device)
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if (is_half):net_g = net_g.half()
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else:net_g = net_g.float()
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vc = VC(tgt_sr, config)
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n_spk=cpt["config"][-3]
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# return {"visible": True,"maximum": n_spk, "__type__": "update"}
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get_vc(model_path)
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audios = os.listdir(input_path)
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for file in tq.tqdm(audios):
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if file.endswith('.wav'):
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file_path = input_path + '/' + file
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wav_opt=vc_single(0,file_path,f0up_key,None,f0method,index_path,index_rate)
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out_path = opt_path + '/' + file
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wavfile.write(out_path, tgt_sr, wav_opt)
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