diff --git a/TTS/tts/layers/generic/transformer.py b/TTS/tts/layers/generic/transformer.py
index 12f0bbb0..2fe9bcc4 100644
--- a/TTS/tts/layers/generic/transformer.py
+++ b/TTS/tts/layers/generic/transformer.py
@@ -70,15 +70,17 @@ class FFTransformerBlock(nn.Module):
 
 
 class FFTDurationPredictor:
-    def __init__(self, in_channels, hidden_channels, num_heads, num_layers, dropout_p=0.1, cond_channels=None):
+    def __init__(self, in_channels, hidden_channels, num_heads, num_layers, dropout_p=0.1, cond_channels=None):  # pylint: disable=unused-argument
         self.fft = FFTransformerBlock(in_channels, num_heads, hidden_channels, num_layers, dropout_p)
         self.proj = nn.Linear(in_channels, 1)
 
-    def forward(self, x, mask=None, g=None):
+    def forward(self, x, mask=None, g=None):  # pylint: disable=unused-argument
         """
         Shapes:
             - x: :math:`[B, C, T]`
             - mask:  :math:`[B, 1, T]`
+
+        TODO: Handle the cond input
         """
         x = self.fft(x, mask=mask)
         x = self.proj(x)
diff --git a/TTS/tts/layers/losses.py b/TTS/tts/layers/losses.py
index 100b8fb3..a2fd7635 100644
--- a/TTS/tts/layers/losses.py
+++ b/TTS/tts/layers/losses.py
@@ -707,7 +707,8 @@ class FastPitchLoss(nn.Module):
         self.aligner_loss_alpha = c.aligner_loss_alpha
         self.binary_alignment_loss_alpha = c.binary_align_loss_alpha
 
-    def _binary_alignment_loss(self, alignment_hard, alignment_soft):
+    @staticmethod
+    def _binary_alignment_loss(alignment_hard, alignment_soft):
         """Binary loss that forces soft alignments to match the hard alignments as
         explained in `https://arxiv.org/pdf/2108.10447.pdf`.
         """
diff --git a/TTS/tts/models/base_tts.py b/TTS/tts/models/base_tts.py
index 653143cd..06c7cb2b 100644
--- a/TTS/tts/models/base_tts.py
+++ b/TTS/tts/models/base_tts.py
@@ -252,13 +252,18 @@ class BaseTTS(BaseModel):
             # compute pitch frames and write to files.
             if config.compute_f0 and rank in [None, 0]:
                 if not os.path.exists(config.f0_cache_path):
-                    dataset.pitch_extractor.compute_pitch(config.get("f0_cache_path", None), config.num_loader_workers)
-                dataset.pitch_extractor.load_pitch_stats(config.get("f0_cache_path", None))
+                    dataset.pitch_extractor.compute_pitch(
+                        ap, config.get("f0_cache_path", None), config.num_loader_workers
+                    )
 
             # halt DDP processes for the main process to finish computing the F0 cache
             if num_gpus > 1:
                 dist.barrier()
 
+            # load pitch stats computed above by all the workers
+            if config.compute_f0:
+                dataset.pitch_extractor.load_pitch_stats(config.get("f0_cache_path", None))
+
             # sampler for DDP
             sampler = DistributedSampler(dataset) if num_gpus > 1 else None