""" 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 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 lib.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)