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svc_inference.py
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svc_inference.py
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import sys,os
sys.path.append(os.path.dirname(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 model.generator import Generator
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 main(args):
if (args.ppg == None):
args.ppg = "svc_tmp.ppg.npy"
print(
f"Auto run : python whisper/inference.py -w {args.wave} -p {args.ppg}")
os.system(f"python whisper/inference.py -w {args.wave} -p {args.ppg}")
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 = Generator(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)
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)
len_pit = pit.size()[0]
len_ppg = ppg.size()[0]
len_min = min(len_pit, len_ppg)
pit = pit[:len_min]
ppg = ppg[:len_min, :]
with torch.no_grad():
spk = spk.unsqueeze(0).to(device)
ppg = ppg.unsqueeze(0).to(device)
pit = pit.unsqueeze(0).to(device)
audio = model.inference(spk, ppg, pit)
audio = audio.cpu().detach().numpy()
pitwav = model.pitch2wav(pit)
pitwav = pitwav.cpu().detach().numpy()
write("svc_out.wav", hp.audio.sampling_rate, audio)
write("svc_out_pitch.wav", hp.audio.sampling_rate, pitwav)
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('--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)