From 088e025470d23e02310afa0fb422207f0589f298 Mon Sep 17 00:00:00 2001
From: Dom <97384583+tosemml@users.noreply.github.com>
Date: Tue, 15 Aug 2023 02:42:49 -0700
Subject: [PATCH 1/2] use np.cumsum

---
 src/training/dcc_tf.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/training/dcc_tf.py b/src/training/dcc_tf.py
index 46d915f..41c71eb 100644
--- a/src/training/dcc_tf.py
+++ b/src/training/dcc_tf.py
@@ -7,6 +7,8 @@
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
+import numpy as np
+
 from torchmetrics.functional import(
     scale_invariant_signal_noise_ratio as si_snr,
     signal_noise_ratio as snr,
@@ -80,10 +82,7 @@ def __init__(self, channels, num_layers, kernel_size=3):
                             for i in range(num_layers)]
 
         # Compute buffer start indices for each layer
-        self.buf_indices = [0]
-        for i in range(num_layers - 1):
-            self.buf_indices.append(
-                self.buf_indices[-1] + self.buf_lengths[i])
+        self.buf_indices = np.cumsum(self.buf_lengths[:(num_layers - 1)]).tolist()
 
         # Dilated causal conv layers aggregate previous context to obtain
         # contexful encoded input.

From c9b59da67b1dfcbcf7f6d9a3e0a659ce039d416d Mon Sep 17 00:00:00 2001
From: Dom <97384583+tosemml@users.noreply.github.com>
Date: Tue, 15 Aug 2023 02:46:59 -0700
Subject: [PATCH 2/2] Update dcc_tf.py

---
 src/training/dcc_tf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/training/dcc_tf.py b/src/training/dcc_tf.py
index 41c71eb..918c7e1 100644
--- a/src/training/dcc_tf.py
+++ b/src/training/dcc_tf.py
@@ -82,7 +82,7 @@ def __init__(self, channels, num_layers, kernel_size=3):
                             for i in range(num_layers)]
 
         # Compute buffer start indices for each layer
-        self.buf_indices = np.cumsum(self.buf_lengths[:(num_layers - 1)]).tolist()
+        self.buf_indices = [0] + np.cumsum(self.buf_lengths[:(num_layers - 1)]).tolist()
 
         # Dilated causal conv layers aggregate previous context to obtain
         # contexful encoded input.