diff --git a/src/hypnos/embedding/__init__.py b/src/hypnos/embedding/__init__.py
index b9f6666..b63b432 100644
--- a/src/hypnos/embedding/__init__.py
+++ b/src/hypnos/embedding/__init__.py
@@ -100,9 +100,7 @@ def embed_edf(
     :func:`load_model` once and reuse the returned model/tokenizers.
     """
     model, tokenizers, meta = load_model(model_repo_or_path, device=device, dtype=dtype)
-    signals = preprocess_edf(
-        edf_path, meta, notch_freq=notch_freq, causal=causal, channel_aliases=channel_aliases
-    )
+    signals = preprocess_edf(edf_path, meta, notch_freq=notch_freq, causal=causal, channel_aliases=channel_aliases)
     tokens, modality_mask, channel_ids = tokenize(tokenizers, meta, signals, device=device)
     return embed(
         model,
diff --git a/src/hypnos/embedding/pipeline.py b/src/hypnos/embedding/pipeline.py
index dbe117b..2777ced 100644
--- a/src/hypnos/embedding/pipeline.py
+++ b/src/hypnos/embedding/pipeline.py
@@ -64,9 +64,7 @@ def preprocess_edf(
     all_channels = sorted({ch for m in metadata.modalities for ch in m.channels})
 
     with pyedflib.EdfReader(edf_path) as f:
-        resolved = load_psg_channels(
-            f, all_channels, drop_unreferenced=True, channel_aliases=channel_aliases
-        )
+        resolved = load_psg_channels(f, all_channels, drop_unreferenced=True, channel_aliases=channel_aliases)
 
     signals: dict[str, np.ndarray] = {}
     for m in metadata.modalities: