diff --git a/moonshine/demo/live_captions.py b/moonshine/demo/live_captions.py
index e8dcf39..91d14c6 100644
--- a/moonshine/demo/live_captions.py
+++ b/moonshine/demo/live_captions.py
@@ -23,15 +23,12 @@
 
 CHUNK_SIZE = 512  # Silero VAD requirement with sampling rate 16000.
 LOOKBACK_CHUNKS = 5
-MARKER_LENGTH = 6
 MAX_LINE_LENGTH = 80
 
 # These affect live caption updating - adjust for your platform speed and model.
 MAX_SPEECH_SECS = 15
 MIN_REFRESH_SECS = 0.2
 
-VERBOSE = False
-
 
 class Transcriber(object):
     def __init__(self, model_name, rate=16000):
@@ -39,9 +36,10 @@ def __init__(self, model_name, rate=16000):
             raise ValueError("Moonshine supports sampling rate 16000 Hz.")
         self.model = MoonshineOnnxModel(model_name=model_name)
         self.rate = rate
-        assets_dir = f"{os.path.join(os.path.dirname(__file__), '..', 'assets')}"
-        tokenizer_file = f"{assets_dir}{os.sep}tokenizer.json"
-        self.tokenizer = Tokenizer.from_file(str(tokenizer_file))
+        tokenizer_path = os.path.join(
+            MOONSHINE_DEMO_DIR, "..", "assets", "tokenizer.json"
+        )
+        self.tokenizer = Tokenizer.from_file(tokenizer_path)
 
         self.inference_secs = 0
         self.number_inferences = 0
@@ -72,32 +70,35 @@ def input_callback(data, frames, time, status):
     return input_callback
 
 
-def end_recording(speech, marker=""):
+def end_recording(speech):
     """Transcribes, caches and prints the caption.  Clears speech buffer."""
-    if len(marker) != MARKER_LENGTH:
-        raise ValueError("Unexpected marker length.")
     text = transcribe(speech)
-    caption_cache.append(text + " " + marker)
-    print_captions(text + (" " + marker) if VERBOSE else "", True)
+    caption_cache.append(text)
+    print_captions(text, new_cached_caption=True)
     speech *= 0.0
 
 
 def print_captions(text, new_cached_caption=False):
     """Prints right justified on same line, prepending cached captions."""
     print("\r" + " " * MAX_LINE_LENGTH, end="", flush=True)
-    if len(text) > MAX_LINE_LENGTH:
-        text = text[-MAX_LINE_LENGTH:]
-    elif text != "\n":
+    if len(text) < MAX_LINE_LENGTH:
         for caption in caption_cache[::-1]:
-            text = (caption[:-MARKER_LENGTH] if not VERBOSE else caption + " ") + text
+            text = caption + " " + text
             if len(text) > MAX_LINE_LENGTH:
                 break
-        if len(text) > MAX_LINE_LENGTH:
-            text = text[-MAX_LINE_LENGTH:]
+    if len(text) > MAX_LINE_LENGTH:
+        text = text[-MAX_LINE_LENGTH:]
     text = " " * (MAX_LINE_LENGTH - len(text)) + text
     print("\r" + text, end="", flush=True)
 
 
+def soft_reset(vad_iterator):
+    """Soft resets Silero VADIterator without affecting VAD model state."""
+    vad_iterator.triggered = False
+    vad_iterator.temp_end = 0
+    vad_iterator.current_sample = 0
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         prog="live_captions",
@@ -145,7 +146,7 @@ def print_captions(text, new_cached_caption=False):
         try:
             while True:
                 chunk, status = q.get()
-                if VERBOSE and status:
+                if status:
                     print(status)
 
                 speech = np.concatenate((speech, chunk))
@@ -160,18 +161,15 @@ def print_captions(text, new_cached_caption=False):
 
                     if "end" in speech_dict and recording:
                         recording = False
-                        end_recording(speech, "<STOP>")
+                        end_recording(speech)
 
                 elif recording:
                     # Possible speech truncation can cause hallucination.
 
                     if (len(speech) / SAMPLING_RATE) > MAX_SPEECH_SECS:
                         recording = False
-                        end_recording(speech, "<SNIP>")
-                        # Soft reset without affecting VAD model state.
-                        vad_iterator.triggered = False
-                        vad_iterator.temp_end = 0
-                        vad_iterator.current_sample = 0
+                        end_recording(speech)
+                        soft_reset(vad_iterator)
 
                     if (time.time() - start_time) > MIN_REFRESH_SECS:
                         print_captions(transcribe(speech))
@@ -184,7 +182,7 @@ def print_captions(text, new_cached_caption=False):
                 while not q.empty():
                     chunk, _ = q.get()
                     speech = np.concatenate((speech, chunk))
-                end_recording(speech, "<END.>")
+                end_recording(speech)
 
             print(f"""
 
@@ -196,7 +194,4 @@ def print_captions(text, new_cached_caption=False):
   model realtime factor :  {(transcribe.speech_secs / transcribe.inference_secs):0.2f}x
 """)
             if caption_cache:
-                print("Cached captions.")
-                for caption in caption_cache:
-                    print(caption[:-MARKER_LENGTH], end="", flush=True)
-                print("")
+                print(f"Cached captions.\n{' '.join(caption_cache)}")