diff --git a/kilosort/io.py b/kilosort/io.py
index 1ee595dd..acef4246 100644
--- a/kilosort/io.py
+++ b/kilosort/io.py
@@ -162,8 +162,8 @@ def save_to_phy(st, clu, tF, Wall, probe, ops, imin, results_dir=None,
     np.save((results_dir / 'whitening_mat_inv.npy'), whitening_mat_inv)
 
     # spike properties
-    spike_times = st[:,0] + imin  # shift by minimum sample index
-    spike_templates = st[:,1]
+    spike_times = st[:,0].astype('int64') + imin  # shift by minimum sample index
+    spike_templates = st[:,1].astype('int32')
     spike_clusters = clu
     xs, ys = compute_spike_positions(st, tF, ops)
     spike_positions = np.vstack([xs, ys]).T
diff --git a/kilosort/postprocessing.py b/kilosort/postprocessing.py
index c9b6c82a..4244d883 100644
--- a/kilosort/postprocessing.py
+++ b/kilosort/postprocessing.py
@@ -4,13 +4,19 @@
 import torch
 
 
-@njit
+@njit("(int64[:], int32[:], int32)")
 def remove_duplicates(spike_times, spike_clusters, dt=15):
     '''Removes same-cluster spikes that occur within `dt` samples.'''
     keep = np.zeros_like(spike_times, bool_)
     cluster_t0 = {}
-    for (i,t), c in zip(enumerate(spike_times), spike_clusters):
-        t0 = cluster_t0.get(c, t-dt)
+    for i in range(spike_times.size):
+        t = spike_times[i]
+        c = spike_clusters[i]
+        if c in cluster_t0:
+            t0 = cluster_t0[c]
+        else:
+            t0 = t - dt
+
         if t >= (t0 + dt):
             # Separate spike, reset t0 and keep spike
             cluster_t0[c] = t