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import sys,os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
import argparse
from omegaconf import OmegaConf
from scipy.io.wavfile import write
from bigvgan.model.generator import Generator
from pitch import load_csv_pitch
def load_bigv_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):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
hp = OmegaConf.load(args.config)
model = Generator(hp)
load_bigv_model(args.model, model)
model.eval()
model.to(device)
mel = torch.load(args.mel)
pit = load_csv_pitch(args.pit)
pit = torch.FloatTensor(pit)
len_pit = pit.size()[0]
len_mel = mel.size()[1]
len_min = min(len_pit, len_mel)
pit = pit[:len_min]
mel = mel[:, :len_min]
with torch.no_grad():
mel = mel.unsqueeze(0).to(device)
pit = pit.unsqueeze(0).to(device)
audio = model.inference(mel, pit)
audio = audio.cpu().detach().numpy()
pitwav = model.pitch2wav(pit)
pitwav = pitwav.cpu().detach().numpy()
write("gvc_out.wav", hp.audio.sampling_rate, audio)
write("gvc_pitch.wav", hp.audio.sampling_rate, pitwav)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--mel', type=str,
help="Path of content vector.")
parser.add_argument('--pit', type=str,
help="Path of pitch csv file.")
args = parser.parse_args()
args.config = "./bigvgan/configs/nsf_bigvgan.yaml"
args.model = "./bigvgan_pretrain/nsf_bigvgan_pretrain_32K.pth"
main(args)
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