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svc_inferencermvpev2.py
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svc_inferencermvpev2.py
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import sys,os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import torch
import argparse
import numpy as np
from omegaconf import OmegaConf
from scipy.io.wavfile import write
from vits.models import SynthesizerInfer
from pitch import load_csv_pitch
def load_svc_model(checkpoint_path, model):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
saved_state_dict = checkpoint_dict["model_g"]
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items():
try:
new_state_dict[k] = saved_state_dict[k]
except:
print("%s is not in the checkpoint" % k)
new_state_dict[k] = v
model.load_state_dict(new_state_dict)
return model
def svc_infer(model, spk, pit, ppg, vec, hp, device):
len_pit = pit.size()[0]
len_vec = vec.size()[0]
len_ppg = ppg.size()[0]
len_min = min(len_pit, len_vec)
len_min = min(len_min, len_ppg)
pit = pit[:len_min]
vec = vec[:len_min, :]
ppg = ppg[:len_min, :]
with torch.no_grad():
spk = spk.unsqueeze(0).to(device)
source = pit.unsqueeze(0).to(device)
source = model.pitch2source(source)
pitwav = model.source2wav(source)
write("svc_out_pit.wav", hp.data.sampling_rate, pitwav)
hop_size = hp.data.hop_length
all_frame = len_min
hop_frame = 10
out_chunk = 2500 # 25 S
out_index = 0
out_audio = []
while (out_index < all_frame):
if (out_index == 0): # start frame
cut_s = 0
cut_s_out = 0
else:
cut_s = out_index - hop_frame
cut_s_out = hop_frame * hop_size
if (out_index + out_chunk + hop_frame > all_frame): # end frame
cut_e = all_frame
cut_e_out = -1
else:
cut_e = out_index + out_chunk + hop_frame
cut_e_out = -1 * hop_frame * hop_size
sub_ppg = ppg[cut_s:cut_e, :].unsqueeze(0).to(device)
sub_vec = vec[cut_s:cut_e, :].unsqueeze(0).to(device)
sub_pit = pit[cut_s:cut_e].unsqueeze(0).to(device)
sub_len = torch.LongTensor([cut_e - cut_s]).to(device)
sub_har = source[:, :, cut_s *
hop_size:cut_e * hop_size].to(device)
sub_out = model.inference(
sub_ppg, sub_vec, sub_pit, spk, sub_len, sub_har)
sub_out = sub_out[0, 0].data.cpu().detach().numpy()
sub_out = sub_out[cut_s_out:cut_e_out]
out_audio.extend(sub_out)
out_index = out_index + out_chunk
out_audio = np.asarray(out_audio)
return out_audio
def main(args):
if (args.ppg == None):
args.ppg = "svc_tmp.ppg.npy"
print(
f"Auto run : python whisper/inferencev2.py -w {args.wave} -p {args.ppg}")
os.system(f"python whisper/inferencev2.py -w {args.wave} -p {args.ppg}")
if (args.vec == None):
args.vec = "svc_tmp.vec.npy"
print(
f"Auto run : python hubert/inferencev2.py -w {args.wave} -v {args.vec}")
os.system(f"python hubert/inferencev2.py -w {args.wave} -v {args.vec}")
if (args.pit == None):
args.pit = "svc_tmp.pit.csv"
print(
f"Auto run : python pitch/inference.py -w {args.wave} -p {args.pit}")
os.system(f"python pitch/inference.py -w {args.wave} -p {args.pit}")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
hp = OmegaConf.load(args.config)
model = SynthesizerInfer(
hp.data.filter_length // 2 + 1,
hp.data.segment_size // hp.data.hop_length,
hp)
load_svc_model(args.model, model)
model.eval()
model.to(device)
spk = np.load(args.spk)
spk = torch.FloatTensor(spk)
ppg = np.load(args.ppg)
ppg = np.repeat(ppg, 2, 0) # 320 PPG -> 160 * 2
ppg = torch.FloatTensor(ppg)
# ppg = torch.zeros_like(ppg)
vec = np.load(args.vec)
vec = np.repeat(vec, 2, 0) # 320 PPG -> 160 * 2
vec = torch.FloatTensor(vec)
# vec = torch.zeros_like(vec)
pit = load_csv_pitch(args.pit)
print("pitch shift: ", args.shift)
if (args.shift == 0):
pass
else:
pit = np.array(pit)
source = pit[pit > 0]
source_ave = source.mean()
source_min = source.min()
source_max = source.max()
print(f"source pitch statics: mean={source_ave:0.1f}, \
min={source_min:0.1f}, max={source_max:0.1f}")
shift = args.shift
shift = 2 ** (shift / 12)
pit = pit * shift
pit = torch.FloatTensor(pit)
model_str = str(args.model)[:-4]
spk_str = str(args.spk)[:-8]
shift_str = str(args.shift)
name_str = str(args.wave)[:-4]
print(model_str,spk_str,shift_str)
# 从最后开始查找斜杠的索引
last_slash_index = spk_str.rfind('/')
if last_slash_index != -1:
# 进行切片操作，获取所需部分
sliced_part = spk_str[last_slash_index + 1:]
else:
# 如果没有找到斜杠，使用整个字符串
sliced_part = spk_str
print(model_str,spk_str,shift_str,sliced_part)
output_filename = name_str + "_" + "rmvpepit_" +"large-v2ppg_" + model_str + "_" + sliced_part + "_"+ shift_str
out_name = output_filename + ".wav"
out_audio = svc_infer(model, spk, pit, ppg, vec, hp, device)
write(out_name, hp.data.sampling_rate, out_audio)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True,
help="yaml file for config.")
parser.add_argument('--model', type=str, required=True,
help="path of model for evaluation")
parser.add_argument('--wave', type=str, required=True,
help="Path of raw audio.")
parser.add_argument('--spk', type=str, required=True,
help="Path of speaker.")
parser.add_argument('--ppg', type=str,
help="Path of content vector.")
parser.add_argument('--vec', type=str,
help="Path of hubert vector.")
parser.add_argument('--pit', type=str,
help="Path of pitch csv file.")
parser.add_argument('--shift', type=int, default=0,
help="Pitch shift key.")
args = parser.parse_args()
main(args)