From 2a71c31b6674f704866068bfd3ffb6c04eb66711 Mon Sep 17 00:00:00 2001 From: "Seth T. Allen" <85146497+sethtallen@users.noreply.github.com> Date: Wed, 26 Jul 2023 02:43:08 -0400 Subject: [PATCH] Create infer_cli.py (#875) The my_inferer.py mentioned in the RBVC docs is broken. This one works. I think we should add it :^) --- infer_cli.py | 235 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 235 insertions(+) create mode 100644 infer_cli.py diff --git a/infer_cli.py b/infer_cli.py new file mode 100644 index 0000000..75e905f --- /dev/null +++ b/infer_cli.py @@ -0,0 +1,235 @@ +import os,sys,pdb,torch +now_dir = os.getcwd() +sys.path.append(now_dir) +import argparse +import glob +import sys +import torch +import numpy as np +from multiprocessing import cpu_count + +#### +#USAGE +# +#In your Terminal or CMD or whatever +#python infer_cli.py [TRANSPOSE_VALUE] "[INPUT_PATH]" "[OUTPUT_PATH]" "[MODEL_PATH]" "[INDEX_FILE_PATH]" "[INFERENCE_DEVICE]" "[METHOD]" + +using_cli = False +device = "cuda:0" +is_half = False + +if(len(sys.argv) > 0): + f0_up_key=int(sys.argv[1]) #transpose value + input_path=sys.argv[2] + output_path=sys.argv[3] + model_path=sys.argv[4] + file_index=sys.argv[5] #.index file + device=sys.argv[6] + f0_method=sys.argv[7] #pm or harvest or crepe + + using_cli = True + + #file_index2=sys.argv[8] + #index_rate=float(sys.argv[10]) #search feature ratio + #filter_radius=float(sys.argv[11]) #median filter + #resample_sr=float(sys.argv[12]) #resample audio in post processing + #rms_mix_rate=float(sys.argv[13]) #search feature + print(sys.argv) + +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() and device != "cpu": + 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 = False + + 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 + +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 +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, _, _ = checkpoint_utils.load_model_ensemble_and_task( + ["hubert_base.pt"], + suffix="", + ) + hubert_model = models[0] + hubert_model = hubert_model.to(config.device) + if config.is_half: + hubert_model = hubert_model.half() + else: + hubert_model = hubert_model.float() + hubert_model.eval() + +def vc_single( + sid=0, + input_audio_path=None, + f0_up_key=0, + f0_file=None, + f0_method="pm", + file_index="", #.index file + file_index2="", + # file_big_npy, + index_rate=1.0, + filter_radius=3, + resample_sr=0, + rms_mix_rate=1.0, + model_path="", + output_path="", + protect=0.33 +): + global tgt_sr, net_g, vc, hubert_model, version + get_vc(model_path) + if input_audio_path is None: + return "You need to upload an audio file", None + + f0_up_key = int(f0_up_key) + audio = load_audio(input_audio_path, 16000) + audio_max = np.abs(audio).max() / 0.95 + + if audio_max > 1: + audio /= audio_max + times = [0, 0, 0] + + if hubert_model == None: + load_hubert() + + if_f0 = cpt.get("f0", 1) + + file_index = ( + ( + file_index.strip(" ") + .strip('"') + .strip("\n") + .strip('"') + .strip(" ") + .replace("trained", "added") + ) + if file_index != "" + else file_index2 + ) + + audio_opt = vc.pipeline( + hubert_model, + net_g, + sid, + audio, + input_audio_path, + times, + f0_up_key, + f0_method, + file_index, + # file_big_npy, + index_rate, + if_f0, + filter_radius, + tgt_sr, + resample_sr, + rms_mix_rate, + version, + f0_file=f0_file, + protect=protect + ) + wavfile.write(output_path, tgt_sr, audio_opt) + return('processed') + + +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(if_f0==1): + net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) + else: + net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) + del net_g.enc_q + print(net_g.load_state_dict(cpt["weight"], strict=False)) + 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"} + +if(using_cli): + vc_single(sid=0,input_audio_path=input_path,f0_up_key=f0_up_key,f0_file=None,f0_method=f0_method,file_index=file_index,file_index2="",index_rate=1,filter_radius=3,resample_sr=0,rms_mix_rate=0,model_path=model_path,output_path=output_path)