From 4de5d0d551eaa7d5c09f7c8aa46a044f9081a512 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=BA=A2=E8=A1=80=E7=90=83AE3803?= <2544390577@qq.com> Date: Wed, 14 Jun 2023 11:22:47 +0900 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=89=B9=E9=87=8F=E6=8E=A8?= =?UTF-8?q?=E7=90=86=E8=84=9A=E6=9C=AC=EF=BC=8C=E5=8F=AF=E4=BB=A5=E4=B8=8D?= =?UTF-8?q?=E7=94=A8=20webui=20(#518)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 增加批量推理脚本 * 更新批量推理脚本 --- infer_batch_rvc.py | 181 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 181 insertions(+) create mode 100644 infer_batch_rvc.py diff --git a/infer_batch_rvc.py b/infer_batch_rvc.py new file mode 100644 index 0000000..78342be --- /dev/null +++ b/infer_batch_rvc.py @@ -0,0 +1,181 @@ +''' +v1 +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 +v2 +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 +''' +import os,sys,pdb,torch +now_dir = os.getcwd() +sys.path.append(now_dir) +import argparse +import glob +import sys +import torch +import tqdm as tq +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 infer_pack.models import ( + SynthesizerTrnMs256NSFsid, + SynthesizerTrnMs256NSFsid_nono, + SynthesizerTrnMs768NSFsid, + SynthesizerTrnMs768NSFsid_nono, +) +from my_utils 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) +audios = os.listdir(input_path) +for file in tq.tqdm(audios): + if file.endswith('.wav'): + file_path = input_path + '/' + file + wav_opt=vc_single(0,file_path,f0up_key,None,f0method,index_path,index_rate) + out_path = opt_path + '/' + file + wavfile.write(out_path, tgt_sr, wav_opt) +