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inference.py
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inference.py
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import argparse
import os
import cv2
import librosa
import numpy as np
import soundfile as sf
import torch
from tqdm import tqdm
from lib import dataset
from lib import nets
from lib import spec_utils
class VocalRemover(object):
def __init__(self, model, device, window_size):
self.model = model
self.offset = model.offset
self.device = device
self.window_size = window_size
def _execute(self, X_mag_pad, roi_size, n_window):
self.model.eval()
with torch.no_grad():
preds = []
for i in tqdm(range(n_window)):
start = i * roi_size
X_mag_window = X_mag_pad[None, :, :, start:start + self.window_size]
X_mag_window = torch.from_numpy(X_mag_window).to(self.device)
pred = self.model.predict(X_mag_window)
pred = pred.detach().cpu().numpy()
preds.append(pred[0])
pred = np.concatenate(preds, axis=2)
return pred
def preprocess(self, X_spec):
X_mag = np.abs(X_spec)
X_phase = np.angle(X_spec)
return X_mag, X_phase
def inference(self, X_spec):
X_mag, X_phase = self.preprocess(X_spec)
coef = X_mag.max()
X_mag_pre = X_mag / coef
n_frame = X_mag_pre.shape[2]
pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.window_size, self.offset)
n_window = int(np.ceil(n_frame / roi_size))
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
pred = self._execute(X_mag_pad, roi_size, n_window)
pred = pred[:, :, :n_frame]
return pred * coef, X_mag, np.exp(1.j * X_phase)
def inference_tta(self, X_spec):
X_mag, X_phase = self.preprocess(X_spec)
coef = X_mag.max()
X_mag_pre = X_mag / coef
n_frame = X_mag_pre.shape[2]
pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.window_size, self.offset)
n_window = int(np.ceil(n_frame / roi_size))
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
pred = self._execute(X_mag_pad, roi_size, n_window)
pred = pred[:, :, :n_frame]
pad_l += roi_size // 2
pad_r += roi_size // 2
n_window += 1
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
pred_tta = self._execute(X_mag_pad, roi_size, n_window)
pred_tta = pred_tta[:, :, roi_size // 2:]
pred_tta = pred_tta[:, :, :n_frame]
return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
def main():
p = argparse.ArgumentParser()
p.add_argument('--gpu', '-g', type=int, default=-1)
p.add_argument('--pretrained_model', '-P', type=str, default='models/baseline.pth')
p.add_argument('--input', '-i', required=True)
p.add_argument('--sr', '-r', type=int, default=44100)
p.add_argument('--n_fft', '-f', type=int, default=2048)
p.add_argument('--hop_length', '-l', type=int, default=1024)
p.add_argument('--window_size', '-w', type=int, default=512)
p.add_argument('--output_image', '-I', action='store_true')
p.add_argument('--postprocess', '-p', action='store_true')
p.add_argument('--tta', '-t', action='store_true')
args = p.parse_args()
print('loading model...', end=' ')
device = torch.device('cpu')
model = nets.CascadedASPPNet(args.n_fft)
model.load_state_dict(torch.load(args.pretrained_model, map_location=device))
if torch.cuda.is_available() and args.gpu >= 0:
device = torch.device('cuda:{}'.format(args.gpu))
model.to(device)
print('done')
print('loading wave source...', end=' ')
X, sr = librosa.load(
args.input, args.sr, False, dtype=np.float32, res_type='kaiser_fast')
basename = os.path.splitext(os.path.basename(args.input))[0]
print('done')
if X.ndim == 1:
X = np.asarray([X, X])
print('stft of wave source...', end=' ')
X = spec_utils.wave_to_spectrogram(X, args.hop_length, args.n_fft)
print('done')
vr = VocalRemover(model, device, args.window_size)
if args.tta:
pred, X_mag, X_phase = vr.inference_tta(X)
else:
pred, X_mag, X_phase = vr.inference(X)
if args.postprocess:
print('post processing...', end=' ')
pred_inv = np.clip(X_mag - pred, 0, np.inf)
pred = spec_utils.mask_silence(pred, pred_inv)
print('done')
print('inverse stft of instruments...', end=' ')
y_spec = pred * X_phase
wave = spec_utils.spectrogram_to_wave(y_spec, hop_length=args.hop_length)
print('done')
sf.write('{}_Instruments.wav'.format(basename), wave.T, sr)
print('inverse stft of vocals...', end=' ')
v_spec = np.clip(X_mag - pred, 0, np.inf) * X_phase
wave = spec_utils.spectrogram_to_wave(v_spec, hop_length=args.hop_length)
print('done')
sf.write('{}_Vocals.wav'.format(basename), wave.T, sr)
if args.output_image:
with open('{}_Instruments.jpg'.format(basename), mode='wb') as f:
image = spec_utils.spectrogram_to_image(y_spec)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
with open('{}_Vocals.jpg'.format(basename), mode='wb') as f:
image = spec_utils.spectrogram_to_image(v_spec)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
if __name__ == '__main__':
main()