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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
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Format code (#877)
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119
infer_cli.py
119
infer_cli.py
@@ -1,4 +1,5 @@
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import os,sys,pdb,torch
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import os, sys, pdb, torch
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import argparse
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@@ -9,35 +10,36 @@ import numpy as np
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from multiprocessing import cpu_count
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####
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#USAGE
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# USAGE
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#
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#In your Terminal or CMD or whatever
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#python infer_cli.py [TRANSPOSE_VALUE] "[INPUT_PATH]" "[OUTPUT_PATH]" "[MODEL_PATH]" "[INDEX_FILE_PATH]" "[INFERENCE_DEVICE]" "[METHOD]"
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# In your Terminal or CMD or whatever
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# python infer_cli.py [TRANSPOSE_VALUE] "[INPUT_PATH]" "[OUTPUT_PATH]" "[MODEL_PATH]" "[INDEX_FILE_PATH]" "[INFERENCE_DEVICE]" "[METHOD]"
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using_cli = False
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device = "cuda:0"
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is_half = False
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if(len(sys.argv) > 0):
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f0_up_key=int(sys.argv[1]) #transpose value
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input_path=sys.argv[2]
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output_path=sys.argv[3]
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model_path=sys.argv[4]
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file_index=sys.argv[5] #.index file
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device=sys.argv[6]
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f0_method=sys.argv[7] #pm or harvest or crepe
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if len(sys.argv) > 0:
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f0_up_key = int(sys.argv[1]) # transpose value
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input_path = sys.argv[2]
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output_path = sys.argv[3]
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model_path = sys.argv[4]
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file_index = sys.argv[5] # .index file
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device = sys.argv[6]
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f0_method = sys.argv[7] # pm or harvest or crepe
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using_cli = True
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#file_index2=sys.argv[8]
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#index_rate=float(sys.argv[10]) #search feature ratio
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#filter_radius=float(sys.argv[11]) #median filter
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#resample_sr=float(sys.argv[12]) #resample audio in post processing
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#rms_mix_rate=float(sys.argv[13]) #search feature
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# file_index2=sys.argv[8]
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# index_rate=float(sys.argv[10]) #search feature ratio
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# filter_radius=float(sys.argv[11]) #median filter
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# resample_sr=float(sys.argv[12]) #resample audio in post processing
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# rms_mix_rate=float(sys.argv[13]) #search feature
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print(sys.argv)
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class Config:
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def __init__(self,device,is_half):
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def __init__(self, device, is_half):
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self.device = device
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self.is_half = is_half
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self.n_cpu = 0
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@@ -113,8 +115,9 @@ class Config:
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return x_pad, x_query, x_center, x_max
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config=Config(device,is_half)
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now_dir=os.getcwd()
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config = Config(device, is_half)
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from vc_infer_pipeline import VC
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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@@ -122,7 +125,9 @@ from my_utils import load_audio
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from fairseq import checkpoint_utils
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from scipy.io import wavfile
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hubert_model=None
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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, _, _ = checkpoint_utils.load_model_ensemble_and_task(
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@@ -137,41 +142,42 @@ def load_hubert():
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hubert_model = hubert_model.float()
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hubert_model.eval()
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def vc_single(
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sid=0,
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input_audio_path=None,
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f0_up_key=0,
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f0_up_key=0,
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f0_file=None,
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f0_method="pm",
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file_index="", #.index file
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f0_method="pm",
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file_index="", # .index file
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file_index2="",
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# file_big_npy,
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index_rate=1.0,
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filter_radius=3,
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resample_sr=0,
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rms_mix_rate=1.0,
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index_rate=1.0,
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filter_radius=3,
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resample_sr=0,
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rms_mix_rate=1.0,
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model_path="",
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output_path="",
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protect=0.33
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protect=0.33,
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):
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global tgt_sr, net_g, vc, hubert_model, version
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get_vc(model_path)
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if input_audio_path is None:
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return "You need to upload an audio file", None
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f0_up_key = int(f0_up_key)
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audio = load_audio(input_audio_path, 16000)
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audio_max = np.abs(audio).max() / 0.95
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if audio_max > 1:
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audio /= audio_max
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times = [0, 0, 0]
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if hubert_model == None:
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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file_index = (
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(
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file_index.strip(" ")
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@@ -184,7 +190,7 @@ def vc_single(
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if file_index != ""
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else file_index2
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)
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audio_opt = vc.pipeline(
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hubert_model,
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net_g,
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@@ -204,32 +210,49 @@ def vc_single(
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rms_mix_rate,
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version,
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f0_file=f0_file,
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protect=protect
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protect=protect,
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)
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wavfile.write(output_path, tgt_sr, audio_opt)
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return('processed')
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return "processed"
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def get_vc(model_path):
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global n_spk,tgt_sr,net_g,vc,cpt,device,is_half, version
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print("loading pth %s"%model_path)
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global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
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print("loading pth %s" % model_path)
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cpt = torch.load(model_path, map_location="cpu")
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk
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if_f0=cpt.get("f0",1)
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if(if_f0==1):
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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del net_g.enc_q
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print(net_g.load_state_dict(cpt["weight"], strict=False))
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net_g.eval().to(device)
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if (is_half):net_g = net_g.half()
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else:net_g = net_g.float()
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if is_half:
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net_g = net_g.half()
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else:
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net_g = net_g.float()
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vc = VC(tgt_sr, config)
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n_spk=cpt["config"][-3]
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n_spk = cpt["config"][-3]
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# return {"visible": True,"maximum": n_spk, "__type__": "update"}
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if(using_cli):
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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)
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if using_cli:
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vc_single(
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sid=0,
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input_audio_path=input_path,
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f0_up_key=f0_up_key,
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f0_file=None,
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f0_method=f0_method,
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file_index=file_index,
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file_index2="",
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index_rate=1,
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filter_radius=3,
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resample_sr=0,
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rms_mix_rate=0,
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model_path=model_path,
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output_path=output_path,
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)
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