blob: 26abc9eba2ce80e7320fd0cb95a93960986e47ce (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
|
#Packet loss simulator
This code is an attempt at simulating better packet loss scenarios. The most common way of simulating
packet loss is to use a random sequence where each packet loss event is uncorrelated with previous events.
That is a simplistic model since we know that losses often occur in bursts. This model uses real data
to build a generative model for packet loss.
We use the training data provided for the Audio Deep Packet Loss Concealment Challenge, which is available at:
http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/test\_train.tar.gz
To create the training data, run:
`./process_data.sh /<path>/test_train/train/lossy_signals/`
That will create an ascii loss\_sorted.txt file with all loss data sorted in increasing packet loss
percentage. Then just run:
`python ./train_lossgen.py`
to train a model
To generate a sequence, run
`python3 ./test_lossgen.py <checkpoint> <percentage> output.txt --length 10000`
where <checkpoint> is the .pth model file and <percentage> is the amount of loss (e.g. 0.2 for 20% loss).
|
