diff --git a/anchor/utils/whisper.py b/anchor/utils/whisper.py
index 312a48b..ed07752 100644
--- a/anchor/utils/whisper.py
+++ b/anchor/utils/whisper.py
@@ -4,6 +4,7 @@
 import os
 import torch
 import whisperx
+from collections import Counter
 from rich.console import Console
 from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeElapsedColumn
 from .ui import make_ui_console, CaptureProgress
@@ -49,6 +50,53 @@ def load_whisper_model(device, compute_type, language, model_size="large-v3"):
 
     return model
 
+def detect_robust_language(model, audio, batch_size):
+    """
+    Samples three 30-second snippets from the audio (15%, 50%, 85%)
+    and runs a quick transcription to determine the language.
+    Returns the most common detected language, or None if it fails.
+    """
+    sample_rate = 16000
+    duration_sec = len(audio) / sample_rate
+
+    # If the video is shorter than 2 minutes, let Whisper handle it natively
+    if duration_sec < 120:
+        return None
+
+    timestamps = [0.15, 0.50, 0.85]
+    guesses = []
+
+    for pct in timestamps:
+        start_sample = int(pct * duration_sec * sample_rate)
+        end_sample = start_sample + (30 * sample_rate)
+
+        # Ensure we don't go out of bounds
+        if end_sample > len(audio):
+            end_sample = len(audio)
+
+        audio_snippet = audio[start_sample:end_sample]
+
+        try:
+            # Run transcription silently to extract language guess
+            result = model.transcribe(
+                audio_snippet,
+                batch_size=batch_size,
+                language=None,
+                print_progress=False, # Mute progress bar
+                combined_progress=False
+            )
+            detected = result.get("language")
+            if detected and detected != "unknown":
+                guesses.append(detected)
+        except Exception:
+            continue
+
+    if not guesses:
+        return None
+
+    # Return the most frequent guess (majority voting)
+    return Counter(guesses).most_common(1)[0][0]
+
 def run_whisper_transcription(video_path, device, compute_type, batch_size, model, language=None):
     """Transcribes audio and aligns phonemes. Returns (whisper_data, detected_lang) or (None, None) on failure."""
     safe_console = Console(force_terminal=True)
@@ -60,8 +108,16 @@ def run_whisper_transcription(video_path, device, compute_type, batch_size, mode
 
     result = None
     current_batch_size = batch_size
+
     is_windows = (os.name != 'posix')
 
+    if language is None:
+        console.print("[dim]🔍 Auto-detecting language via three separate samples...[/dim]")
+        robust_lang = detect_robust_language(model, audio, current_batch_size)
+        if robust_lang:
+            language = robust_lang
+            console.print(f"[dim]🌐 Robust auto-detect determined: [bold cyan]{language.upper()}[/bold cyan][/dim]")
+
     while current_batch_size >= 1:
         try:
             sys.stdout.flush()