diff --git a/dac/model/base.py b/dac/model/base.py
index 546b3cb..a6c93f9 100644
--- a/dac/model/base.py
+++ b/dac/model/base.py
@@ -204,8 +204,8 @@ def compress(
         range_fn = range if not verbose else tqdm.trange
 
         for i in range_fn(0, nt, hop):
-            x = audio_signal[..., i : i + n_samples]
-            x = x.zero_pad(0, max(0, n_samples - x.shape[-1]))
+            x = audio_signal[..., i : i + n_samples + self.hop_length] # crossfade pad
+            x = x.zero_pad(0, max(0, n_samples - x.shape[-1] + self.hop_length))
 
             audio_data = x.audio_data.to(self.device)
             audio_data = self.preprocess(audio_data, self.sample_rate)
@@ -271,8 +271,13 @@ def decompress(
             r = self.decode(z)
             recons.append(r.to(original_device))
 
-        recons = torch.cat(recons, dim=-1)
-        recons = AudioSignal(recons, self.sample_rate)
+        # recons = torch.cat(recons, dim=-1)
+        # recons = AudioSignal(recons, self.sample_rate)
+
+        chunks = recons[0]
+        for i in range(1, len(recons)):
+            chunks = self.crossfade_concat(chunks, recons[i], self.hop_length)
+        recons = AudioSignal(chunks, self.sample_rate)
 
         resample_fn = recons.resample
         loudness_fn = recons.loudness
@@ -292,3 +297,11 @@ def decompress(
 
         self.padding = original_padding
         return recons
+
+    @torch.no_grad()
+    def crossfade_concat(self, chunk1, chunk2, overlap):
+        fade_out = torch.cos(torch.linspace(0, torch.pi / 2, overlap)) ** 2
+        fade_in = torch.cos(torch.linspace(torch.pi / 2, 0, overlap)) ** 2
+        chunk2[..., :overlap] = chunk1[..., -overlap:] * fade_out + chunk2[..., :overlap] * fade_in
+        chunk = torch.cat((chunk1[..., :-overlap], chunk2), dim=-1)
+        return chunk