A Python implementation of the correlation based glimpse proportion (CGP) index proposed in [1] for speech intelligibility prediction. Tested against original MATLAB implementation. Supports batched inputs.
pip install python-cgp
import cgp
import soundfile as sf
# assuming single-channel files
x, fs = sf.read("path/to/input/signal")
y, fs = sf.read("path/to/reference/signal")
z = cgp.cgp(x, y, fs)For batched inputs:
import cgp
import numpy as np
import soundfile as sf
# assuming single-channel files
x_1, fs = sf.read("path/to/input/signal/1")
y_1, fs = sf.read("path/to/reference/signal/1")
x_2, fs = sf.read("path/to/input/signal/2")
y_2, fs = sf.read("path/to/reference/signal/2")
# x_1 and x_2 might have different lengths so we pad before creating the batch
lengths = [len(x_1), len(x_2)]
n = max(lengths)
x_1 = np.pad(x_1, (0, n - len(x_1)))
y_1 = np.pad(y_1, (0, n - len(y_1)))
x_2 = np.pad(x_2, (0, n - len(x_2)))
y_2 = np.pad(y_2, (0, n - len(y_2)))
# create the batch
x = np.stack([x_1, x_2])
y = np.stack([y_1, y_2])
# provide the lengths argument to ensure the same result as for unbatched processing
z = cgp.cgp(x, y, fs, axis=1, lengths=lengths)This implementation corrects an indexing error in the original MATLAB code in removeSilentFrames.m which causes the last frame in the voice activity detection stage to be discarded.
This means the results can differ slightly from the original implementation.
To obtain the same results as the original implementation, set the _discard_last_frame argument to True.
The paper claims CGP takes substantially less time to execute compared to baselines such as ESTOI [2].
Below is a comparison with pystoi for unbatched inputs.
TODO
[1] A. Alghamdi, L. Moen, W.-Y. Chan, D. Fogerty and J. Jensen, "Correlation based glimpse proportion index", in Proc. WASPAA, 2023.
[2] J. Jensen and C. H. Taal, "An algorithm for predicting the intelligibility of speech masked by modulated noise maskers", in IEEE/ACM Trans. Audio, Speech, Lang. Process., 2016.