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- import paddle
- import argparse
- import numpy as np
- import librosa as li
- import soundfile as sf
-
- from ddsp import DDSP
- from ddsp.core import extract_loudness, extract_pitch
-
-
- def main():
- parser = argparse.ArgumentParser()
-
- parser.add_argument('--ckpt', '-c', type=str,
- default='./pretrained_models/violin/pretrained.pdparams')
- parser.add_argument('--input', '-i', type=str,
- default='./audios/singing.wav')
- parser.add_argument('--output', '-o', type=str,
- default='./audios/output.wav')
-
- args = parser.parse_known_args()[0]
-
- ckpt = args.ckpt
- audio_file = args.input
- output_file = args.output
-
- sampling_rate = 48000
- signal_length = 192000
- block_size = 512
-
- hidden_size = 512
- n_harmonic = 64
- n_bands = 65
-
- model = DDSP(
- hidden_size=hidden_size,
- n_harmonic=n_harmonic,
- n_bands=n_bands,
- sampling_rate=sampling_rate,
- block_size=block_size
- )
- params = paddle.load(ckpt)
- model.set_state_dict(params)
- model.eval()
-
- # Load wav float and padding to signal_length
- x, sr = li.load(audio_file, sampling_rate)
- N = (signal_length - len(x) % signal_length) % signal_length
- x = np.pad(x, (0, N))
-
- # get pitch data per block_size
- pitch = extract_pitch(x, sampling_rate, block_size)
-
- # get loudness data per block_size
- loudness = extract_loudness(x, sampling_rate, block_size)
-
- x = x.reshape(-1, signal_length).astype(np.float32)
- p = pitch.reshape(x.shape[0], -1).astype(np.float32)
- l = loudness.reshape(x.shape[0], -1).astype(np.float32)
-
- with paddle.no_grad():
- ps = paddle.to_tensor(p, dtype=paddle.float32)
- ls = paddle.to_tensor(l, dtype=paddle.float32)
-
- audios = []
- for p, l in zip(ps, ls):
- p = p.unsqueeze(-1)
- l = l.unsqueeze(-1)
- p = p.unsqueeze(0)
- l = l.unsqueeze(0)
-
- l = (l - ls.mean()) / ls.std()
-
- y = model(p, l).squeeze(-1)
-
- audios.append(y)
-
- audios = paddle.concat(audios, -1)
- audios = audios.reshape((-1,)).detach().numpy()
-
- sf.write(output_file, audios, sampling_rate)
-
-
- if __name__ == '__main__':
- main()
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