Paolify/RVC_4

1"""2v13runtime\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 0.334v25runtime\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 0.336"""7import os, sys, pdb, torch8 9now_dir = os.getcwd()10sys.path.append(now_dir)11import sys12import torch13import tqdm as tq14from multiprocessing import cpu_count15 16 17class Config:18    def __init__(self, device, is_half):19        self.device = device20        self.is_half = is_half21        self.n_cpu = 022        self.gpu_name = None23        self.gpu_mem = None24        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()25 26    def device_config(self) -> tuple:27        if torch.cuda.is_available():28            i_device = int(self.device.split(":")[-1])29            self.gpu_name = torch.cuda.get_device_name(i_device)30            if (31                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())32                or "P40" in self.gpu_name.upper()33                or "1060" in self.gpu_name34                or "1070" in self.gpu_name35                or "1080" in self.gpu_name36            ):37                print("16系/10系显卡和P40强制单精度")38                self.is_half = False39                for config_file in ["32k.json", "40k.json", "48k.json"]:40                    with open(f"configs/{config_file}", "r") as f:41                        strr = f.read().replace("true", "false")42                    with open(f"configs/{config_file}", "w") as f:43                        f.write(strr)44                with open("infer/modules/train/preprocess.py", "r") as f:45                    strr = f.read().replace("3.7", "3.0")46                with open("infer/modules/train/preprocess.py", "w") as f:47                    f.write(strr)48            else:49                self.gpu_name = None50            self.gpu_mem = int(51                torch.cuda.get_device_properties(i_device).total_memory52                / 102453                / 102454                / 102455                + 0.456            )57            if self.gpu_mem <= 4:58                with open("infer/modules/train/preprocess.py", "r") as f:59                    strr = f.read().replace("3.7", "3.0")60                with open("infer/modules/train/preprocess.py", "w") as f:61                    f.write(strr)62        elif torch.backends.mps.is_available():63            print("没有发现支持的N卡, 使用MPS进行推理")64            self.device = "mps"65        else:66            print("没有发现支持的N卡, 使用CPU进行推理")67            self.device = "cpu"68            self.is_half = True69 70        if self.n_cpu == 0:71            self.n_cpu = cpu_count()72 73        if self.is_half:74            # 6G显存配置75            x_pad = 376            x_query = 1077            x_center = 6078            x_max = 6579        else:80            # 5G显存配置81            x_pad = 182            x_query = 683            x_center = 3884            x_max = 4185 86        if self.gpu_mem != None and self.gpu_mem <= 4:87            x_pad = 188            x_query = 589            x_center = 3090            x_max = 3291 92        return x_pad, x_query, x_center, x_max93 94 95f0up_key = sys.argv[1]96input_path = sys.argv[2]97index_path = sys.argv[3]98f0method = sys.argv[4]  # harvest or pm99opt_path = sys.argv[5]100model_path = sys.argv[6]101index_rate = float(sys.argv[7])102device = sys.argv[8]103is_half = sys.argv[9].lower() != "false"104filter_radius = int(sys.argv[10])105resample_sr = int(sys.argv[11])106rms_mix_rate = float(sys.argv[12])107protect = float(sys.argv[13])108print(sys.argv)109config = Config(device, is_half)110now_dir = os.getcwd()111sys.path.append(now_dir)112from infer.modules.vc.modules import VC113from lib.infer_pack.models import (114    SynthesizerTrnMs256NSFsid,115    SynthesizerTrnMs256NSFsid_nono,116    SynthesizerTrnMs768NSFsid,117    SynthesizerTrnMs768NSFsid_nono,118)119from infer.lib.audio import load_audio120from fairseq import checkpoint_utils121from scipy.io import wavfile122 123hubert_model = None124 125 126def load_hubert():127    global hubert_model128    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(129        ["hubert_base.pt"],130        suffix="",131    )132    hubert_model = models[0]133    hubert_model = hubert_model.to(device)134    if is_half:135        hubert_model = hubert_model.half()136    else:137        hubert_model = hubert_model.float()138    hubert_model.eval()139 140 141def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):142    global tgt_sr, net_g, vc, hubert_model, version143    if input_audio is None:144        return "You need to upload an audio", None145    f0_up_key = int(f0_up_key)146    audio = load_audio(input_audio, 16000)147    times = [0, 0, 0]148    if hubert_model == None:149        load_hubert()150    if_f0 = cpt.get("f0", 1)151    # 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)152    audio_opt = vc.pipeline(153        hubert_model,154        net_g,155        sid,156        audio,157        input_audio,158        times,159        f0_up_key,160        f0_method,161        file_index,162        index_rate,163        if_f0,164        filter_radius,165        tgt_sr,166        resample_sr,167        rms_mix_rate,168        version,169        protect,170        f0_file=f0_file,171    )172    print(times)173    return audio_opt174 175 176def get_vc(model_path):177    global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version178    print("loading pth %s" % model_path)179    cpt = torch.load(model_path, map_location="cpu")180    tgt_sr = cpt["config"][-1]181    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk182    if_f0 = cpt.get("f0", 1)183    version = cpt.get("version", "v1")184    if version == "v1":185        if if_f0 == 1:186            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)187        else:188            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])189    elif version == "v2":190        if if_f0 == 1:  #191            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)192        else:193            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])194    del net_g.enc_q195    print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净，真奇葩196    net_g.eval().to(device)197    if is_half:198        net_g = net_g.half()199    else:200        net_g = net_g.float()201    vc = VC(tgt_sr, config)202    n_spk = cpt["config"][-3]203    # return {"visible": True,"maximum": n_spk, "__type__": "update"}204 205 206get_vc(model_path)207audios = os.listdir(input_path)208for file in tq.tqdm(audios):209    if file.endswith(".wav"):210        file_path = input_path + "/" + file211        wav_opt = vc_single(212            0, file_path, f0up_key, None, f0method, index_path, index_rate213        )214        out_path = opt_path + "/" + file215        wavfile.write(out_path, tgt_sr, wav_opt)216