ref: b923fd1e2811a4bf6a4ea1461a550c8d15143f01
parent: c40add59af065f4fdf80048f2dad91d6b4480114
author: Jean-Marc Valin <jmvalin@amazon.com>
date: Thu Dec 21 13:01:57 EST 2023
lossgen: better training, README.md
--- /dev/null
+++ b/dnn/torch/lossgen/README.md
@@ -1,0 +1,27 @@
+#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).
--- a/dnn/torch/lossgen/train_lossgen.py
+++ b/dnn/torch/lossgen/train_lossgen.py
@@ -27,9 +27,11 @@
return self.nb_sequences
def __getitem__(self, index):
- r0 = np.random.normal(scale=.02, size=(1,1)).astype('float32')- r1 = np.random.normal(scale=.02, size=(self.sequence_length,1)).astype('float32')- return [self.loss[index, :, :], self.perc[index, :, :]+r0+r1]
+ r0 = np.random.normal(scale=.1, size=(1,1)).astype('float32')+ r1 = np.random.normal(scale=.1, size=(self.sequence_length,1)).astype('float32')+ perc = self.perc[index, :, :]
+ perc = perc + (r0+r1)*perc*(1-perc)
+ return [self.loss[index, :, :], perc]
adam_betas = [0.8, 0.98]
@@ -61,7 +63,7 @@
if __name__ == '__main__':
model.to(device)
-
+ states = None
for epoch in range(1, epochs + 1):
running_loss = 0
@@ -73,7 +75,8 @@
loss = loss.to(device)
perc = perc.to(device)
- out, _ = model(loss, perc)
+ out, states = model(loss, perc, states=states)
+ states = [state.detach() for state in states]
out = torch.sigmoid(out[:,:-1,:])
target = loss[:,1:,:]
--
⑨