def get_model(hps_path, model_path): hps = Hps() hps.load(hps_path) hps_tuple = hps.get_tuple() solver = Solver(hps_tuple, None, None) solver.load_model(model_path) return solver
def get_model(
hps_path='./hps/vctk.json',
model_path='/storage/model/voice_conversion/vctk/clf/model.pkl-109999'
):
hps = Hps()
hps.load(hps_path)
hps_tuple = hps.get_tuple()
solver = Solver(hps_tuple, None)
solver.load_model(model_path)
return solver
parser.add_argument('-hps',
help='The path of hyper-parameter set',
default='vctk.json')
parser.add_argument('-model', '-m', help='The path of model checkpoint')
parser.add_argument('-source', '-s', help='The path of source .wav file')
parser.add_argument(
'-target',
'-t',
help=
'Target speaker id (integer). Same order as the speaker list when preprocessing (en_speaker_used.txt)'
)
parser.add_argument('-output', '-o', help='output .wav path')
parser.add_argument('-sample_rate', '-sr', default=16000, type=int)
parser.add_argument('--use_gen', default=True, action='store_true')
args = parser.parse_args()
hps = Hps()
hps.load(args.hps)
hps_tuple = hps.get_tuple()
solver = Solver(hps_tuple, None)
solver.load_model(args.model)
_, spec = get_spectrograms(args.source)
spec_expand = np.expand_dims(spec, axis=0)
spec_tensor = torch.from_numpy(spec_expand).type(torch.FloatTensor)
c = Variable(torch.from_numpy(np.array([int(args.target)]))).cuda()
result = solver.test_step(spec_tensor, c, gen=args.use_gen)
result = result.squeeze(axis=0).transpose((1, 0))
wav_data = spectrogram2wav(result)
write(args.output, rate=args.sample_rate, data=wav_data)