diff --git a/nemo/collections/tts/losses/audio_codec_loss.py b/nemo/collections/tts/losses/audio_codec_loss.py
index 6db3e30595c6..ba423827fc0b 100644
--- a/nemo/collections/tts/losses/audio_codec_loss.py
+++ b/nemo/collections/tts/losses/audio_codec_loss.py
@@ -512,3 +512,89 @@ def forward(self, disc_scores_real, disc_scores_gen):
         loss /= len(disc_scores_real)
 
         return loss
+
+
+class MMDLoss(Loss):
+    """
+    Maximum mean discrepancy (MMD) loss, as defined in https://arxiv.org/abs/2406.02315
+
+    Args:
+        num_codebooks: Number of codebooks.
+        codebok_dim: Dimension of a single codebook code.
+        kernel_radii: List of radii for Gaussian kernels
+        loss_scale: Scaling factor to apply to output loss.
+    """
+
+    def __init__(self, num_codebooks, codebook_dim, kernel_radii=(0.1, 1, 5, 10, 20, 50), loss_scale=1.0):
+        super().__init__()
+        self.num_codebooks = num_codebooks
+        self.codebook_dim = codebook_dim
+        self.kernel_radii = kernel_radii
+        self.loss_scale = loss_scale
+
+    @staticmethod
+    def _exp_kernel(dxx, r):
+        return torch.exp((-0.5 / r) * dxx).sum()
+
+    @staticmethod
+    def _shuffle_codebooks(x):
+        N, K, _ = x.size()
+        x_shuffled = torch.zeros_like(x)
+        for k in range(K):
+            batch_perm = torch.randperm(N, device=x.device)
+            x_shuffled[:, k, :] = x[batch_perm, k, :]
+        return x_shuffled
+
+    @property
+    def input_types(self):
+        return {
+            "codes": [NeuralType(('B', 'D', 'T'), VoidType())],
+        }
+
+    @property
+    def output_types(self):
+        return {
+            "loss": NeuralType(elements_type=LossType())
+        }
+
+    @typecheck()
+    def forward(self, codes):
+        B, D, T = codes.size()
+        N = B * T
+
+        # [B, K, C, T]
+        x = codes.reshape(B, self.num_codebooks, self.codebook_dim, T)
+        # [N, K, C]
+        x = rearrange(x, 'B K C T -> (B T) K C')
+        x_mean = x.mean(dim=(0,), keepdim=True)
+        x_stdev = torch.sqrt(x.var(dim=(0,), keepdim=True) + 1e-8)
+        x = (x - x_mean) / x_stdev
+        y = self._shuffle_codebooks(x)
+
+        # [N, D]
+        x = x.reshape([N, D])
+        y = y.reshape([N, D])
+
+        # [N, N]
+        xx = torch.mm(x, x.t())
+        yy = torch.mm(y, y.t())
+        zz = torch.mm(x, y.t())
+
+        rx = xx.diag().unsqueeze(0).expand_as(xx)
+        ry = yy.diag().unsqueeze(0).expand_as(yy)
+
+        dxx = rx.t() + rx - 2.0 * xx
+        dyy = ry.t() + ry - 2.0 * yy
+        dxy = rx.t() + ry - 2.0 * zz
+
+        loss = 0.0
+        coeff = -2.0 / N**2
+        denom = N * (N - 1)
+        for r in self.kernel_radii:
+            loss += (torch.utils.checkpoint.checkpoint(self._exp_kernel, dxx, r) - N) / denom
+            loss += coeff * torch.utils.checkpoint.checkpoint(self._exp_kernel, dxy, r)
+            loss += (torch.utils.checkpoint.checkpoint(self._exp_kernel, dyy, r) - N) / denom
+
+        loss = loss.clamp(min=0)
+        loss = self.loss_scale * loss
+        return loss
\ No newline at end of file
diff --git a/nemo/collections/tts/models/audio_codec.py b/nemo/collections/tts/models/audio_codec.py
index 33b9a80125b7..b33693b510b8 100644
--- a/nemo/collections/tts/models/audio_codec.py
+++ b/nemo/collections/tts/models/audio_codec.py
@@ -151,6 +151,13 @@ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
         else:
             raise ValueError(f'Unknown feature loss type {feature_loss_type}.')
 
+        if "mmd_loss" in cfg:
+            self.mmd_loss_fn = instantiate(cfg.mmd_loss)
+            self.mmd_loss_scale = cfg.get("mmd_loss_scale", 1.0)
+        else:
+            self.mmd_loss_fn = None
+            self.mmd_loss_scale = None
+
         # Codebook loss setup
         if self.vector_quantizer:
             self.commit_loss_scale = cfg.get("commit_loss_scale", 1.0)
@@ -470,7 +477,7 @@ def _process_batch(self, batch):
         # [B, T]
         audio_gen, _ = self.audio_decoder(inputs=encoded, input_len=encoded_len)
 
-        return audio, audio_len, audio_gen, commit_loss
+        return audio, audio_len, audio_gen, commit_loss, encoded
 
     @property
     def disc_update_prob(self) -> float:
@@ -487,7 +494,7 @@ def should_update_disc(self, batch_idx) -> bool:
     def training_step(self, batch, batch_idx):
         optim_gen, optim_disc = self.optimizers()
 
-        audio, audio_len, audio_gen, commit_loss = self._process_batch(batch)
+        audio, audio_len, audio_gen, commit_loss, codes = self._process_batch(batch)
 
         metrics = {
             "global_step": self.global_step,
@@ -547,6 +554,11 @@ def training_step(self, batch, batch_idx):
             metrics["g_loss_commit"] = commit_loss
             generator_losses.append(self.commit_loss_scale * commit_loss)
 
+        if self.mmd_loss_scale:
+            loss_mmd = self.mmd_loss_fn(codes=codes)
+            metrics["g_loss_mmd"] = loss_mmd
+            generator_losses.append(self.mmd_loss_scale * loss_mmd)
+
         # compute embeddings for speaker consistency loss
         if self.use_scl_loss:
             # concate generated and GT waveforms
@@ -592,7 +604,7 @@ def on_train_epoch_end(self):
         self.update_lr("epoch")
 
     def validation_step(self, batch, batch_idx):
-        audio, audio_len, audio_gen, _ = self._process_batch(batch)
+        audio, audio_len, audio_gen, _, _ = self._process_batch(batch)
 
         loss_mel_l1, loss_mel_l2 = self.mel_loss_fn(audio_real=audio, audio_gen=audio_gen, audio_len=audio_len)
         loss_stft = self.stft_loss_fn(audio_real=audio, audio_gen=audio_gen, audio_len=audio_len)