diff --git a/whisper_timestamped/transcribe.py b/whisper_timestamped/transcribe.py
index 6233257..266e64c 100755
--- a/whisper_timestamped/transcribe.py
+++ b/whisper_timestamped/transcribe.py
@@ -76,7 +76,7 @@ def transcribe_timestamped(
     refine_whisper_precision=0.5,
     min_word_duration=0.02, # Was 0.04 before 1.11
     plot_word_alignment=False,
-    word_alignement_most_top_layers=None, # Was 6 before 1.9
+    word_alignment_most_top_layers=None, # Was 6 before 1.9
     remove_empty_words=False,
     use_backend_timestamps=False,
 
@@ -214,7 +214,7 @@ def transcribe_timestamped(
     assert refine_whisper_precision >= 0 and refine_whisper_precision / AUDIO_TIME_PER_TOKEN == round(refine_whisper_precision / AUDIO_TIME_PER_TOKEN), f"refine_whisper_precision must be a positive multiple of {AUDIO_TIME_PER_TOKEN}"
     refine_whisper_precision_nframes = round(refine_whisper_precision / AUDIO_TIME_PER_TOKEN)
     assert min_word_duration >= 0, f"min_word_duration must be a positive number"
-    assert word_alignement_most_top_layers is None or word_alignement_most_top_layers > 0, f"word_alignement_most_top_layers must be a strictly positive number"
+    assert word_alignment_most_top_layers is None or word_alignment_most_top_layers > 0, f"word_alignment_most_top_layers must be a strictly positive number"
 
     if isinstance(temperature, (list, tuple)) and len(temperature) == 1:
         temperature = temperature[0]
@@ -242,9 +242,9 @@ def transcribe_timestamped(
     time_precision = input_stride * HOP_LENGTH / SAMPLE_RATE
     assert time_precision == AUDIO_TIME_PER_TOKEN
 
-    alignment_heads = get_alignment_heads(model) if word_alignement_most_top_layers is None else None
-    if alignment_heads is None and word_alignement_most_top_layers is None:
-        word_alignement_most_top_layers = 6
+    alignment_heads = get_alignment_heads(model) if word_alignment_most_top_layers is None else None
+    if alignment_heads is None and word_alignment_most_top_layers is None:
+        word_alignment_most_top_layers = 6
 
     alignment_options = dict(
             remove_punctuation_from_words=remove_punctuation_from_words,
@@ -253,7 +253,7 @@ def transcribe_timestamped(
             detect_disfluencies=detect_disfluencies,
             refine_whisper_precision_nframes=refine_whisper_precision_nframes,
             plot_word_alignment=plot_word_alignment,
-            word_alignement_most_top_layers=word_alignement_most_top_layers,
+            word_alignment_most_top_layers=word_alignment_most_top_layers,
             alignment_heads=alignment_heads,
     )
     whisper_options = dict(
@@ -351,7 +351,7 @@ def _transcribe_timestamped_efficient(
     refine_whisper_precision_nframes,
     alignment_heads,
     plot_word_alignment,
-    word_alignement_most_top_layers,
+    word_alignment_most_top_layers,
     detect_disfluencies,
     trust_whisper_timestamps,
     use_timestamps_for_alignment = True,
@@ -378,13 +378,13 @@ def _transcribe_timestamped_efficient(
 
     debug = logger.getEffectiveLevel() >= logging.DEBUG
 
-    word_alignement_most_top_layers = float("inf") if word_alignement_most_top_layers is None else word_alignement_most_top_layers
+    word_alignment_most_top_layers = float("inf") if word_alignment_most_top_layers is None else word_alignment_most_top_layers
 
     # The main outcome
     timestamped_word_segments = []  # list of timestamped word segments that have been collected so far
     # Main variables to be accumulated
     segment_tokens = [[]]              # list of lists of token indices that have been collected so far (one list per segment)
-    segment_attweights = [[] for _ in range(min(word_alignement_most_top_layers, len(model.decoder.blocks)))]
+    segment_attweights = [[] for _ in range(min(word_alignment_most_top_layers, len(model.decoder.blocks)))]
                                     # attention weights on the last segments
     segment_avglogprobs = []        # average log probability for each segment (actually of the corresponding chunk, as computed by whisper)
     segment_logprobs = []           # token log probabilities for each segment
@@ -875,7 +875,7 @@ def hook_output_logits(layer, ins, outs):
         nblocks = len(model.decoder.blocks)
         j = 0
         for i, block in enumerate(model.decoder.blocks):
-            if i < nblocks - word_alignement_most_top_layers:
+            if i < nblocks - word_alignment_most_top_layers:
                 continue
             all_hooks.append(
                 block.cross_attn.register_forward_hook(
@@ -995,7 +995,7 @@ def _transcribe_timestamped_naive(
     use_backend_timestamps,
     alignment_heads,
     plot_word_alignment,
-    word_alignement_most_top_layers,
+    word_alignment_most_top_layers,
     detect_disfluencies,
     trust_whisper_timestamps,
     min_word_duration,
@@ -1006,7 +1006,7 @@ def _transcribe_timestamped_naive(
     language = whisper_options["language"]
     refine_whisper_precision_sec = refine_whisper_precision_nframes * AUDIO_TIME_PER_TOKEN
 
-    word_alignement_most_top_layers = float("inf") if word_alignement_most_top_layers is None else word_alignement_most_top_layers
+    word_alignment_most_top_layers = float("inf") if word_alignment_most_top_layers is None else word_alignment_most_top_layers
 
     audio = get_audio_tensor(audio)
     audio_duration = audio.shape[-1] / SAMPLE_RATE
@@ -1077,7 +1077,7 @@ def hook_output_logits(layer, ins, outs):
 
     n_mels = model.dims.n_mels if hasattr(model.dims, "n_mels") else 80
 
-    attention_weights = [[] for _ in range(min(word_alignement_most_top_layers, len(model.decoder.blocks)))]
+    attention_weights = [[] for _ in range(min(word_alignment_most_top_layers, len(model.decoder.blocks)))]
 
     try:
 
@@ -1087,7 +1087,7 @@ def hook_output_logits(layer, ins, outs):
         nblocks = len(model.decoder.blocks)
         j = 0
         for i, block in enumerate(model.decoder.blocks):
-            if i < nblocks - word_alignement_most_top_layers:
+            if i < nblocks - word_alignment_most_top_layers:
                 continue
             def hook(layer, ins, outs, index=j):
                 if is_transformer_model(model):