From ca94ea5c3690c98d179fe3d048bd9f02ddefe537 Mon Sep 17 00:00:00 2001 From: Brandon Thomas Date: Mon, 17 Jul 2023 14:47:53 -0400 Subject: [PATCH] Work on our own interface --- .../rvc-v2-brandon/fakeyou_infer.py | 238 ++++++++++++++++++ 1 file changed, 238 insertions(+) create mode 100644 voice_conversion/rvc-v2-brandon/fakeyou_infer.py diff --git a/voice_conversion/rvc-v2-brandon/fakeyou_infer.py b/voice_conversion/rvc-v2-brandon/fakeyou_infer.py new file mode 100644 index 0000000..c18814a --- /dev/null +++ b/voice_conversion/rvc-v2-brandon/fakeyou_infer.py @@ -0,0 +1,238 @@ +""" +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 + +print("Env vars:") +print(os.environ) + + +def print_gpu_info(): + print('========================================') + print('Python interpreter', sys.executable) + print('PyTorch version', torch.__version__) + print('CUDA Available?', torch.cuda.is_available()) + print('CUDA Device count', torch.cuda.device_count()) + print('CUDA architectures library was compiled for', torch.cuda.get_arch_list()) + + #try: + # from tensorflow.python.client import device_lib + # print('local devices', str(device_lib.list_local_devices()).replace("\n", "\n ")) + #except ImportError: + # print('no tensorflow - cannot list devices') + # pass + print('========================================', flush=True) + +print_gpu_info() + + +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 = sys.argv[9].lower() != "false" +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)