optimize: 精简未用到的配置项并在特征提取初步引入mps (#32)
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config.py
77
config.py
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@ -1,3 +1,20 @@
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########################硬件参数########################
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#填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
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device = "cuda:0"
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#9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
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is_half = True
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#默认0用上所有线程，写数字限制CPU资源使用
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n_cpu = 0
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########################硬件参数########################
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##################下为参数处理逻辑，勿动##################
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########################命令行参数########################
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--port", type=int, default=7865, help="Listen port")
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@ -5,34 +22,48 @@ parser.add_argument("--pycmd", type=str, default="python", help="Python command"
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parser.add_argument("--colab", action='store_true', help="Launch in colab")
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parser.add_argument("--noparallel", action='store_true', help="Disable parallel processing")
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cmd_opts = parser.parse_args()
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############离线VC参数
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inp_root=r"白鹭霜华长条"#对输入目录下所有音频进行转换，别放非音频文件
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opt_root=r"opt"#输出目录
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f0_up_key=0#升降调，整数，男转女12，女转男-12
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person=r"weights\洛天依v3.pt"#目前只有洛天依v3
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############硬件参数
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device = "cuda:0"#填写cuda:x或cpu，x指代第几张卡，只支持N卡加速
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is_half=True#9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
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n_cpu=0#默认0用上所有线程，写数字限制CPU资源使用
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############python命令路径
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python_cmd=cmd_opts.pycmd
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listen_port=cmd_opts.port
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iscolab=cmd_opts.colab
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noparallel=cmd_opts.noparallel
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############下头别动
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########################命令行参数########################
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import sys
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import torch
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if(torch.cuda.is_available()==False):
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print("没有发现支持的N卡, 使用CPU进行推理")
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device="cpu"
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is_half=False
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if(device!="cpu"):
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gpu_name=torch.cuda.get_device_name(int(device.split(":")[-1]))
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if("16"in gpu_name or "MX"in gpu_name):
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# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
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# check `getattr` and try it for compatibility
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def has_mps() -> bool:
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if sys.platform != "darwin":
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return False
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else:
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if not getattr(torch, 'has_mps', False): return False
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try:
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torch.zeros(1).to(torch.device("mps"))
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return True
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except Exception:
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return False
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if(not torch.cuda.is_available()):
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if has_mps():
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print("没有发现支持的N卡, 使用MPS进行推理")
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device = "mps"
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else:
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print("没有发现支持的N卡, 使用CPU进行推理")
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device = "cpu"
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is_half = False
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if(device not in ["cpu", "mps"]):
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gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1]))
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if("16" in gpu_name or "MX" in gpu_name):
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print("16系显卡/MX系显卡强制单精度")
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is_half=False
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is_half = False
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from multiprocessing import cpu_count
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if(n_cpu==0):n_cpu=cpu_count()
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if(is_half==True):
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if(n_cpu==0): n_cpu=cpu_count()
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if(is_half):
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#6G显存配置
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x_pad = 3
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x_query = 10
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@ -41,10 +72,6 @@ if(is_half==True):
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else:
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#5G显存配置
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x_pad = 1
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# x_query = 6
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# x_center = 30
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# x_max = 32
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#6G显存配置
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x_query = 6
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x_center = 38
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x_max = 41
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@ -1,13 +1,12 @@
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import os,sys,traceback
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if len(sys.argv) == 4:
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n_part=int(sys.argv[1])
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i_part=int(sys.argv[2])
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exp_dir=sys.argv[3]
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else:
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n_part=int(sys.argv[1])
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i_part=int(sys.argv[2])
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i_gpu=sys.argv[3]
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device=sys.argv[1]
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n_part=int(sys.argv[2])
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i_part=int(sys.argv[3])
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if len(sys.argv) == 5:
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exp_dir=sys.argv[4]
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else:
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i_gpu=sys.argv[4]
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exp_dir=sys.argv[5]
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os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
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import torch
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@ -15,7 +14,6 @@ import torch.nn.functional as F
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import soundfile as sf
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import numpy as np
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from fairseq import checkpoint_utils
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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f = open("%s/extract_f0_feature.log"%exp_dir, "a+")
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def printt(strr):
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@ -50,8 +48,8 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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)
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model = models[0]
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model = model.to(device)
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if torch.cuda.is_available():
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model = model.half()
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printt("move model to "+device)
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if device != "cpu": model = model.half()
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model.eval()
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todo=sorted(list(os.listdir(wavPath)))[i_part::n_part]
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@ -70,7 +68,7 @@ else:
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feats = readwave(wav_path, normalize=saved_cfg.task.normalize)
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padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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inputs = {
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"source": feats.half().to(device) if torch.cuda.is_available() else feats.to(device),
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"source": feats.half().to(device) if device != "cpu" else feats.to(device),
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"padding_mask": padding_mask.to(device),
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"output_layer": 9, # layer 9
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}
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10
infer-web.py
10
infer-web.py
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@ -36,7 +36,7 @@ from fairseq import checkpoint_utils
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import gradio as gr
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import logging
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from vc_infer_pipeline import VC
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from config import is_half,device,is_half,python_cmd,listen_port,iscolab,noparallel
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from config import is_half,device,python_cmd,listen_port,iscolab,noparallel
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from infer_uvr5 import _audio_pre_
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from my_utils import load_audio
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from train.process_ckpt import show_info,change_info,merge,extract_small_model
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@ -53,7 +53,7 @@ class ToolButton(gr.Button, gr.components.FormComponent):
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hubert_model=None
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def load_hubert():
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global hubert_model
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
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hubert_model = models[0]
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hubert_model = hubert_model.to(device)
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if(is_half):hubert_model = hubert_model.half()
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@ -79,7 +79,7 @@ def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_np
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if(hubert_model==None):load_hubert()
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if_f0 = cpt.get("f0", 1)
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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)
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print("npy: ", times[0], "s, f0:", times[1], "s, infer: ", times[2], "s", sep='')
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print("npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep='')
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return "Success", (tgt_sr, audio_opt)
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except:
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info=traceback.format_exc()
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@ -267,7 +267,7 @@ def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir):
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leng=len(gpus)
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ps=[]
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for idx,n_g in enumerate(gpus):
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cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir)
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cmd=python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s"%(device,leng,idx,n_g,now_dir,exp_dir)
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print(cmd)
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p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
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ps.append(p)
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@ -382,7 +382,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
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leng=len(gpus)
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ps=[]
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for idx,n_g in enumerate(gpus):
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cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir1)
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cmd=python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s"%(device,leng,idx,n_g,now_dir,exp_dir1)
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yield get_info_str(cmd)
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p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
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ps.append(p)
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@ -345,7 +345,7 @@ class SourceModuleHnNSF(torch.nn.Module):
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def forward(self, x,upp=None):
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sine_wavs, uv, _ = self.l_sin_gen(x,upp)
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if(self.is_half==True):sine_wavs=sine_wavs.half()
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if(self.is_half):sine_wavs=sine_wavs.half()
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sine_merge = self.l_tanh(self.l_linear(sine_wavs))
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return sine_merge,None,None# noise, uv
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class GeneratorNSF(torch.nn.Module):
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@ -345,7 +345,7 @@ class SourceModuleHnNSF(torch.nn.Module):
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def forward(self, x,upp=None):
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sine_wavs, uv, _ = self.l_sin_gen(x,upp)
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if(self.is_half==True):sine_wavs=sine_wavs.half()
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if(self.is_half):sine_wavs=sine_wavs.half()
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sine_merge = self.l_tanh(self.l_linear(sine_wavs))
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return sine_merge,None,None# noise, uv
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class GeneratorNSF(torch.nn.Module):
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@ -39,7 +39,7 @@ class _audio_pre_():
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cpk = torch.load( model_path , map_location='cpu')
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model.load_state_dict(cpk)
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model.eval()
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if(is_half==True):model = model.half().to(device)
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if(is_half):model = model.half().to(device)
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else:model = model.to(device)
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self.mp = mp
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@ -182,4 +182,4 @@ def main():
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if __name__ == '__main__':
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main()
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main()
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@ -40,7 +40,7 @@ class PreProcess():
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os.makedirs(self.gt_wavs_dir,exist_ok=True)
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os.makedirs(self.wavs16k_dir,exist_ok=True)
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def print(self, strr):
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def println(self, strr):
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mutex.acquire()
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print(strr)
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self.f.write("%s\n" % strr)
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@ -70,9 +70,9 @@ class PreProcess():
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tmp_audio = audio[start:]
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break
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self.norm_write(tmp_audio, idx0, idx1)
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self.print("%s->Suc."%path)
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self.println("%s->Suc."%path)
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except:
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self.print("%s->%s"%(path,traceback.format_exc()))
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self.println("%s->%s"%(path,traceback.format_exc()))
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def pipeline_mp(self,infos):
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for path, idx0 in infos:
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@ -91,14 +91,14 @@ class PreProcess():
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ps.append(p)
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for p in ps:p.join()
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except:
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self.print("Fail. %s"%traceback.format_exc())
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self.println("Fail. %s"%traceback.format_exc())
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def preprocess_trainset(inp_root, sr, n_p, exp_dir):
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pp=PreProcess(sr,exp_dir)
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pp.print("start preprocess")
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pp.print(sys.argv)
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pp.println("start preprocess")
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pp.println(sys.argv)
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pp.pipeline_mp_inp_dir(inp_root,n_p)
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pp.print("end preprocess")
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pp.println("end preprocess")
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if __name__=='__main__':
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preprocess_trainset(inp_root, sr, n_p, exp_dir)
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@ -27,7 +27,7 @@ def inference(X_spec, device, model, aggressiveness,data):
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start = i * roi_size
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X_mag_window = X_mag_pad[None, :, :, start:start + data['window_size']]
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X_mag_window = torch.from_numpy(X_mag_window)
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if(is_half==True):X_mag_window=X_mag_window.half()
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if(is_half):X_mag_window=X_mag_window.half()
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X_mag_window=X_mag_window.to(device)
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pred = model.predict(X_mag_window, aggressiveness)
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@ -58,7 +58,7 @@ class VC(object):
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def vc(self,model,net_g,sid,audio0,pitch,pitchf,times,index,big_npy,index_rate):#,file_index,file_big_npy
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feats = torch.from_numpy(audio0)
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if(self.is_half==True):feats=feats.half()
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if(self.is_half):feats=feats.half()
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else:feats=feats.float()
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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@ -78,10 +78,10 @@ class VC(object):
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if(isinstance(index,type(None))==False and isinstance(big_npy,type(None))==False and index_rate!=0):
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npy = feats[0].cpu().numpy()
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if(self.is_half==True):npy=npy.astype("float32")
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if(self.is_half):npy=npy.astype("float32")
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_, I = index.search(npy, 1)
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npy=big_npy[I.squeeze()]
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if(self.is_half==True):npy=npy.astype("float16")
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if(self.is_half):npy=npy.astype("float16")
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feats = torch.from_numpy(npy).unsqueeze(0).to(self.device)*index_rate + (1-index_rate)*feats
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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