def __getitem__(self, index):
x = self.load_sample(self.files[index])
x = VoiceActivityDetector.from_picture_to_tensor(x)
if self.mode == 'test':
return x
else:
label_id = VoiceActivityDetector.LABEL_TO_IDX[self.labels[index]]
return x, label_id
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('model_path',
type=str,
help='The path where model is stored')
parser.add_argument('audio_path',
type=str,
help='The path to the audio file to be processed')
args = parser.parse_args()
print(f'Processing on: {VoiceActivityDetector.DEVICE}')
# ========================================================
detector = VoiceActivityDetector()
detector.load(args.model_path)
rate, signal, labels = load_labeled_audio(args.audio_path)
signal = signal[int(0 * rate):int(30 * rate)]
labels = labels[int(0 * rate):int(30 * rate)]
detector.setup(rate)
buffer_sizes = list(range(20, 151, 5)) # ms
ratios = []
for buffer_size in buffer_sizes:
print(f'buffer_size = {buffer_size}')
st = time.time()
parser.add_argument('--net-window-size',
type=float,
default=0.05,
help='Window size of neural network in seconds')
parser.add_argument(
'--net-step-size-ratio',
type=float,
default=0.5,
help=
'Step size ratio of neural network: percentage of window size for neural network in [0, 1]'
)
parser.add_argument('model_path',
type=str,
help='The path where model will be saved')
parser.add_argument('--arc',
type=str,
default='cnn',
help='Architecture type of neural network')
args = parser.parse_args()
params: dict = copy.deepcopy(vars(args))
params.pop('model_path')
params.pop('arc')
detector = VoiceActivityDetector(params)
print(f'Saving model...\n{detector}')
detector.save(args.model_path)
print('Done')
)
parser.add_argument(
'--cuda',
type=bool,
default=True
)
args = parser.parse_args()
if args.cuda and torch.cuda.is_available():
VoiceActivityDetector.DEVICE = torch.device('cuda')
else:
VoiceActivityDetector.DEVICE = torch.device('cpu')
print(f'Processing on: {VoiceActivityDetector.DEVICE}')
detector = VoiceActivityDetector()
detector.load(args.model_path)
dataset_dir = Path(args.dataset_dir)
dataset_paths = sorted(list(dataset_dir.rglob('*.png')))
labels = [path.parent.name for path in dataset_paths]
X_train, X_val, y_train, y_val = train_test_split(
dataset_paths,
labels,
test_size=args.val_ratio,
shuffle=True
)
train_dataset = TrainVadDataset(X_train, mode='train')
val_dataset = TrainVadDataset(X_val, mode='val')
default=0.05,
help='The buffer size for audio pieces in seconds')
args = parser.parse_args()
print(args.cuda)
if args.cuda and torch.cuda.is_available():
print('???')
VoiceActivityDetector.DEVICE = torch.device('cuda')
else:
VoiceActivityDetector.DEVICE = torch.device('cpu')
print(f'Processing on: {VoiceActivityDetector.DEVICE}')
# ========================================================
detector = VoiceActivityDetector()
detector.load(args.model_path)
rate, signal, labels = load_labeled_audio(args.audio_path)
# signal = signal[int(500 * rate): int(750 * rate)]
# labels = labels[int(500 * rate): int(750 * rate)]
ts = np.linspace(0, len(signal) / rate, num=len(signal))
detector.setup(rate)
stream_buffer = StreamBuffer(rate)
buffer_size_f = int(np.ceil(rate * args.buffer_size))
signal_size_f = len(signal)
def __getitem__(self, index):
pxl_l = self.pxl_ls[index]
x = spectrogram[:, pxl_l:pxl_l + sample_pxl_width, :]
x = Image.fromarray(x)
x = VoiceActivityDetector.from_picture_to_tensor(x)
return x
'--mat-output-path',
type=str,
help='The path to the .mat file where labels will be stored')
parser.add_argument('--device',
type=str,
help='Device type for computations',
default='cuda')
parser.add_argument(
'--statistics-path',
type=str,
help=
'The path to the file where statistics of processing will be stored')
args = parser.parse_args()
# ========================================================
detector = VoiceActivityDetector()
detector.load(args.model_path)
rate, signal, labels = load_labeled_audio(args.audio_path)
ts = np.linspace(0, len(signal) / rate, num=len(signal))
# ========================================================
spectrogram = build_spectrogram(
signal,
rate,
n_filters=detector.params['n_filters'],
window_size_s=detector.params['window_size'],
step_size_ratio=detector.params['step_size_ratio'])
# ========================================================
net_window_size_f = int(rate * detector.params['net_window_size'])