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Inference_With_Vocal.py
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Inference_With_Vocal.py
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import os, pdb
import argparse
import glob
import sys
import torch
from multiprocessing import cpu_count
import ffmpeg
import numpy as np
now_dir = os.getcwd()
sys.path.append(now_dir)
os.chdir('/content/Mangio-RVC-Fork')
now_dir=os.getcwd()
sys.path.append(now_dir)
def load_audio(file, sr):
try:
# https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
file = (
file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
) # 防止小白拷路径头尾带了空格和"和回车
out, _ = (
ffmpeg.input(file, threads=0)
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except Exception as e:
raise RuntimeError(f"Failed to load audio: {e}")
return np.frombuffer(out, np.float32).flatten()
from vc_infer_pipeline import VC
from lib.infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
from fairseq import checkpoint_utils
from scipy.io import wavfile
import Config_Infer
hubert_model=None
def load_hubert():
global hubert_model
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["/content/Mangio-RVC-Fork/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,filter_radius=3,resample_sr=48000,rms_mix_rate=0.25, protect=0.33):
global tgt_sr,net_g,vc,hubert_model
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)
version = cpt.get("version")
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=filter_radius,tgt_sr=tgt_sr,resample_sr=resample_sr,rms_mix_rate=rms_mix_rate,version=version,protect=protect,f0_file=f0_file, crepe_hop_length=128)
# print(times)
return audio_opt
def get_vc(model_path, device_, is_half_):
global n_spk,tgt_sr,net_g,vc,cpt,device,is_half
device = device_
is_half = is_half_
config = Config_Infer.Inference_config()
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", "v2")
if(if_f0==1):
if version == "v1":
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
else:
if version == "v1":
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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]
import os
import sys
now_dir=os.getcwd()
sys.path.append(now_dir)
from IPython.display import Audio
from scipy.io.wavfile import write as write_wav
config = Config_Infer.Inference_config()
semantic_path = "semantic_output/pytorch_model.bin" # set to None if you don't want to use finetuned semantic
coarse_path = "coarse_output/pytorch_model.bin" # set to None if you don't want to use finetuned coarse
fine_path = "fine_output/pytorch_model.bin" # set to None if you don't want to use finetuned fine
use_rvc = True # Set to False to use bark without RVC
rvc_name = config.experiment_name
def argparser (rvc_name):
parser = argparse.ArgumentParser(description='Parser example')
parser.add_argument('-n', '--name', type=str, default = rvc_name, help='Name of model')
return parser.parse_args()
args = argparser(rvc_name)
rvc_name = args.name
rvc_path = f"/content/Mangio-RVC-Fork/weights/{rvc_name}.pth"
index_path = f"/content/Mangio-RVC-Fork/logs/{rvc_name}/added_IVF256_Flat_nprobe_1_{rvc_name}_v2.index"
device="cuda:0"
is_half=True
SAMPLE_RATE = 24_000
#from rvc_infer import get_vc, vc_single
get_vc(rvc_path, device, is_half)
infer_data_path = '/content/dataset_Infer'
for file in os.listdir(infer_data_path):
if 'Vocals.wav' in file:
file_name = file
filepath = os.path.join(infer_data_path, file)
index_rate = 0.75
f0up_key = 0
filter_radius = 3
rms_mix_rate = 0.25
protect = 0.33
resample_sr = SAMPLE_RATE
f0method = "harvest" #harvest or pm
try:
audio_array = vc_single(0,filepath,f0up_key,None,f0method,index_path,index_rate, filter_radius=filter_radius, resample_sr=resample_sr, rms_mix_rate=rms_mix_rate, protect=protect)
except:
audio_array = vc_single(0,filepath,f0up_key,None,'pm',index_path,index_rate, filter_radius=filter_radius, resample_sr=resample_sr, rms_mix_rate=rms_mix_rate, protect=protect)
save_file_path = os.path.join(infer_data_path, f'{rvc_name}_{os.path.split(filepath)[0]}')
save_data_path = os.path.join(infer_data_path, f'{rvc_name}_{file_name}.wav')
write_wav(save_data_path, SAMPLE_RATE, audio_array)
#Audio(audio_array, rate=SAMPLE_RATE)