shehzeen / waveguard_defense Goto Github PK

When running the code, the lpc defense does not work for me: running the code always emits .wav files that are completely silent.

The command I am running is python Defender/defender_multiple.py --in_dir /tmp/a/ --out_base /tmp/ --defender_type lpc --defender_hp 10. When I run this I see the input is non-zero (INput wav -0.8821411 0.92477417) but the output is completely zero (LPC min max 0.0 0.0).

I've traced it down to the fact that the voiced_unvoiced function looks like it always returning an array consisting entirely of 0s. This function depends on only two libraries (numpy and scipy.signal) so I don't believe it's a dependency issue.

The input I'm using is the test input here.

If you'd like to unit test this, then I've provided a numpy file here so that if you run

from lpc import voiced_unvoiced
print(voiced_unvoiced(np.load("lpc.npy")))

then you should get the output [0 0 0 0 0 . . . 0 0 0].

I think that this is an error, but I'm not certain. Am I doing something wrong here? Maybe the inputs should be pre-processed in some way ahead of time? Or the hyperparameters are wrong (I don't think this is the case as I've tried all values in [2..100]).

To evaluate the defense AUC, the README instructs me to run this command:

python evaluate_detector.py --in_orig <DIR CONTAINING ORIGINAL UNDEFENDED AUDIO> \
                            --in_orig_def <DIR CONTAINING ORIGINAL DEFENDED AUDIO> \
                            --in_adv <DIR CONTAINING ADVERSARIAL UNDEFENDED AUDIO> \
                            --in_adv_def <DIR CONTAINING ADVERSARIAL DEFENDED AUDIO>

How do I generate the folder contents of what I pass to --in_adv and --in_adv_def? I'm particularly interested in an implementation of the adaptive attack algorithm from the paper.

Hello, I'm studying your article. I encountered an error when reading the code. In the file spectral.py, I variable error. In addition, what version of deepspeech do you use?
if axis is None:
ndims = t.get_shape().ndims
if ndims is None:
raise ValueError('Cannot run on tensor with dynamic ndims')
dims = []
for i in range(ndims):
try:
dim = int(t.get_shape()[i])
except:
dim = tf.shape(t)[i]
dims.append(dim)
return dims
else:
try:
dim = int(t.get_shape()[axis])
except:
dim = tf.shape(t)[i] // The variable I here is not clear
return dim