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| """ | |
| 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) | |