diff --git a/configs/experiment/ljspeech_no_prior_loss.yaml b/configs/experiment/ljspeech_no_prior_loss.yaml
new file mode 100644
index 0000000..6181950
--- /dev/null
+++ b/configs/experiment/ljspeech_no_prior_loss.yaml
@@ -0,0 +1,17 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: ljspeech.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["ljspeech"]
+
+run_name: ljspeech
+
+model:
+  prior_loss: false
diff --git a/configs/model/matcha.yaml b/configs/model/matcha.yaml
index 4700855..36f6eaf 100644
--- a/configs/model/matcha.yaml
+++ b/configs/model/matcha.yaml
@@ -12,3 +12,4 @@ spk_emb_dim: 64
 n_feats: 80
 data_statistics: ${data.data_statistics}
 out_size: null # Must be divisible by 4
+prior_loss: true
diff --git a/matcha/models/matcha_tts.py b/matcha/models/matcha_tts.py
index 6feb9e7..64b2c07 100644
--- a/matcha/models/matcha_tts.py
+++ b/matcha/models/matcha_tts.py
@@ -34,6 +34,7 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         out_size,
         optimizer=None,
         scheduler=None,
+        prior_loss=True,
     ):
         super().__init__()
 
@@ -44,6 +45,7 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         self.spk_emb_dim = spk_emb_dim
         self.n_feats = n_feats
         self.out_size = out_size
+        self.prior_loss = prior_loss
 
         if n_spks > 1:
             self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim)
@@ -228,7 +230,10 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         # Compute loss of the decoder
         diff_loss, _ = self.decoder.compute_loss(x1=y, mask=y_mask, mu=mu_y, spks=spks, cond=cond)
 
-        prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask)
-        prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats)
+        if self.prior_loss:
+            prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask)
+            prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats)
+        else:
+            prior_loss = 0
 
         return dur_loss, prior_loss, diff_loss