Add batch reasoning function

SignX/inference.sh

@@ -83,6 +83,54 @@ if [ "$#" -lt 1 ]; then
 fi
 
 VIDEO_PATH="$1"
+
+# Batch mode: if a directory is provided, iterate over supported video files.
+if [ -d "$VIDEO_PATH" ]; then
+    VIDEO_DIR=$(realpath "$VIDEO_PATH")
+    if [ -n "$2" ]; then
+        echo -e "${RED}Error: output path override is not supported in batch mode${NC}"
+        exit 1
+    fi
+
+    echo ""
+    echo "======================================================================"
+    echo "  Batch Inference Mode"
+    echo "======================================================================"
+    echo "  Directory: $VIDEO_DIR"
+    echo "  Outputs: stored per-video using default locations"
+    echo "======================================================================"
+    echo ""
+
+    mapfile -d '' VIDEO_FILES < <(find "$VIDEO_DIR" -maxdepth 1 -type f \( -iname '*.mp4' -o -iname '*.mov' -o -iname '*.avi' -o -iname '*.mkv' \) -print0 | sort -z)
+    if [ ${#VIDEO_FILES[@]} -eq 0 ]; then
+        echo -e "${RED}Error: no video files (.mp4/.mov/.avi/.mkv) found under $VIDEO_DIR${NC}"
+        exit 1
+    fi
+
+    batch_status=0
+    total=${#VIDEO_FILES[@]}
+    index=1
+    for video_file in "${VIDEO_FILES[@]}"; do
+        echo ""
+        echo ">>> [Batch] Processing ($index/$total): $video_file"
+        if bash "$SCRIPT_DIR/$(basename "${BASH_SOURCE[0]}")" "$video_file"; then
+            echo ">>> [Batch] Completed: $video_file"
+        else
+            echo ">>> [Batch] Failed: $video_file"
+            batch_status=1
+        fi
+        index=$((index + 1))
+    done
+
+    echo ""
+    if [ $batch_status -eq 0 ]; then
+        echo -e "${GREEN}✓ Batch inference finished without errors${NC}"
+    else
+        echo -e "${YELLOW}⚠ Batch inference finished with some failures (see logs above)${NC}"
+    fi
+    exit $batch_status
+fi
+
 if [ -z "$2" ]; then
     OUTPUT_PATH="$INFERENCE_ROOT/inference_output_$(date +%Y%m%d_%H%M%S)_$RANDOM.txt"
 else
@@ -404,6 +452,15 @@ if [ -f "$TEMP_DIR/prediction.txt" ]; then
                 rm -f "$MOVED_CLEAN_FILE"
             fi
 
+            # Preserve a copy of the input video inside the sample directory for reference
+            if [ -f "$VIDEO_PATH" ]; then
+                VIDEO_BASENAME=$(basename "$VIDEO_PATH")
+                DEST_VIDEO_PATH="${PRIMARY_SAMPLE_DIR}/${VIDEO_BASENAME}"
+                if [ ! -f "$DEST_VIDEO_PATH" ]; then
+                    cp "$VIDEO_PATH" "$DEST_VIDEO_PATH"
+                fi
+            fi
+
             OUTPUT_PATH="$TRANSLATION_FILE"
             OUTPUT_CLEAN_PATH="$TRANSLATION_FILE"
         fi

SignX/inference_output/detailed_prediction_20260102_202142/171921/171921.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b55f3fae16c90ecd7e799d2515aeea9af00df4efad003d84e6b0aba1a3527822
+size 102121

SignX/inference_output/detailed_prediction_20260102_202142/171921/analysis_report.txt

@@ -0,0 +1,41 @@
+================================================================================
+  Sign Language Recognition - Attention Analysis Report
+================================================================================
+
+Generated at: 2026-01-02 20:21:46
+
+Translation:
+--------------------------------------------------------------------------------
+IX-1p NOT LIKE PINEAPPLE fs-CREAM+CHEESE IX-1p
+
+Video info:
+--------------------------------------------------------------------------------
+Total feature frames: 25
+Word count: 6
+
+Attention tensor:
+--------------------------------------------------------------------------------
+Shape: (21, 25)
+  - Decoder steps: 21
+
+Word-to-frame details:
+================================================================================
+No.   Word                 Frames          Peak     Attn     Conf      
+--------------------------------------------------------------------------------
+1     IX-1p                3-3             3        0.390    medium    
+2     NOT                  5-5             5        0.532    high      
+3     LIKE                 7-7             7        0.626    high      
+4     PINEAPPLE            9-10            10       0.237    medium    
+5     fs-CREAM+CHEESE      12-13           12       0.113    low       
+6     IX-1p                0-24            24       0.211    medium    
+
+================================================================================
+
+Summary:
+--------------------------------------------------------------------------------
+Average attention weight: 0.352
+High-confidence words: 2 (33.3%)
+Medium-confidence words: 3 (50.0%)
+Low-confidence words: 1 (16.7%)
+
+================================================================================

SignX/inference_output/detailed_prediction_20260102_202142/171921/attention_keyframes/keyframes_index.txt

@@ -0,0 +1,30 @@
+Attention Keyframe Index
+============================================================
+
+Sample directory: /research/cbim/vast/sf895/code/Sign-X/output/huggingface_asllrp_repo/SignX/inference_output/detailed_prediction_20260102_202142/171921
+Video path: /common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/171921.mp4
+Total keyframes: 21
+
+Keyframe list:
+------------------------------------------------------------
+Gloss   0: keyframe_000_feat3_frame13_att0.390.jpg
+Gloss   1: keyframe_001_feat5_frame21_att0.532.jpg
+Gloss   2: keyframe_002_feat7_frame28_att0.626.jpg
+Gloss   3: keyframe_003_feat10_frame40_att0.246.jpg
+Gloss   4: keyframe_004_feat12_frame47_att0.118.jpg
+Gloss   5: keyframe_005_feat24_frame94_att0.213.jpg
+Gloss   6: keyframe_006_feat24_frame94_att0.249.jpg
+Gloss   7: keyframe_007_feat24_frame94_att0.240.jpg
+Gloss   8: keyframe_008_feat16_frame63_att0.108.jpg
+Gloss   9: keyframe_009_feat22_frame86_att0.200.jpg
+Gloss  10: keyframe_010_feat22_frame86_att0.177.jpg
+Gloss  11: keyframe_011_feat22_frame86_att0.210.jpg
+Gloss  12: keyframe_012_feat0_frame1_att0.317.jpg
+Gloss  13: keyframe_013_feat24_frame94_att0.266.jpg
+Gloss  14: keyframe_014_feat24_frame94_att0.358.jpg
+Gloss  15: keyframe_015_feat22_frame86_att0.251.jpg
+Gloss  16: keyframe_016_feat24_frame94_att0.249.jpg
+Gloss  17: keyframe_017_feat24_frame94_att0.210.jpg
+Gloss  18: keyframe_018_feat24_frame94_att0.321.jpg
+Gloss  19: keyframe_019_feat24_frame94_att0.325.jpg
+Gloss  20: keyframe_020_feat24_frame94_att0.214.jpg

SignX/inference_output/detailed_prediction_20260102_202142/171921/attention_weights.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e58f87c3e44e771f42094b907aa628b0da60088d0595a8e7ff5602ba250a7719
+size 2228

SignX/inference_output/detailed_prediction_20260102_202142/171921/debug_video_path.txt

@@ -0,0 +1,4 @@
+video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/171921.mp4'
+video_path type = <class 'str'>
+video_path is None: False
+bool(video_path): True

SignX/inference_output/detailed_prediction_20260102_202142/171921/feature_frame_mapping.json

@@ -0,0 +1,158 @@
+{
+  "original_frame_count": 96,
+  "feature_count": 25,
+  "downsampling_ratio": 3.84,
+  "fps": 24.0,
+  "mapping": [
+    {
+      "feature_index": 0,
+      "frame_start": 0,
+      "frame_end": 3,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 1,
+      "frame_start": 3,
+      "frame_end": 7,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 2,
+      "frame_start": 7,
+      "frame_end": 11,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 3,
+      "frame_start": 11,
+      "frame_end": 15,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 4,
+      "frame_start": 15,
+      "frame_end": 19,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 5,
+      "frame_start": 19,
+      "frame_end": 23,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 6,
+      "frame_start": 23,
+      "frame_end": 26,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 7,
+      "frame_start": 26,
+      "frame_end": 30,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 8,
+      "frame_start": 30,
+      "frame_end": 34,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 9,
+      "frame_start": 34,
+      "frame_end": 38,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 10,
+      "frame_start": 38,
+      "frame_end": 42,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 11,
+      "frame_start": 42,
+      "frame_end": 46,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 12,
+      "frame_start": 46,
+      "frame_end": 49,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 13,
+      "frame_start": 49,
+      "frame_end": 53,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 14,
+      "frame_start": 53,
+      "frame_end": 57,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 15,
+      "frame_start": 57,
+      "frame_end": 61,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 16,
+      "frame_start": 61,
+      "frame_end": 65,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 17,
+      "frame_start": 65,
+      "frame_end": 69,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 18,
+      "frame_start": 69,
+      "frame_end": 72,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 19,
+      "frame_start": 72,
+      "frame_end": 76,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 20,
+      "frame_start": 76,
+      "frame_end": 80,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 21,
+      "frame_start": 80,
+      "frame_end": 84,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 22,
+      "frame_start": 84,
+      "frame_end": 88,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 23,
+      "frame_start": 88,
+      "frame_end": 92,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 24,
+      "frame_start": 92,
+      "frame_end": 96,
+      "frame_count": 4
+    }
+  ]
+}

SignX/inference_output/detailed_prediction_20260102_202142/171921/frame_alignment.json

@@ -0,0 +1,68 @@
+{
+  "translation": "IX-1p NOT LIKE PINEAPPLE fs-CREAM+CHEESE IX-1p",
+  "words": [
+    "IX-1p",
+    "NOT",
+    "LIKE",
+    "PINEAPPLE",
+    "fs-CREAM+CHEESE",
+    "IX-1p"
+  ],
+  "total_video_frames": 25,
+  "frame_ranges": [
+    {
+      "word": "IX-1p",
+      "start_frame": 3,
+      "end_frame": 3,
+      "peak_frame": 3,
+      "avg_attention": 0.38998693227767944,
+      "confidence": "medium"
+    },
+    {
+      "word": "NOT",
+      "start_frame": 5,
+      "end_frame": 5,
+      "peak_frame": 5,
+      "avg_attention": 0.5316620469093323,
+      "confidence": "high"
+    },
+    {
+      "word": "LIKE",
+      "start_frame": 7,
+      "end_frame": 7,
+      "peak_frame": 7,
+      "avg_attention": 0.626325249671936,
+      "confidence": "high"
+    },
+    {
+      "word": "PINEAPPLE",
+      "start_frame": 9,
+      "end_frame": 10,
+      "peak_frame": 10,
+      "avg_attention": 0.2374260425567627,
+      "confidence": "medium"
+    },
+    {
+      "word": "fs-CREAM+CHEESE",
+      "start_frame": 12,
+      "end_frame": 13,
+      "peak_frame": 12,
+      "avg_attention": 0.11307986080646515,
+      "confidence": "low"
+    },
+    {
+      "word": "IX-1p",
+      "start_frame": 0,
+      "end_frame": 24,
+      "peak_frame": 24,
+      "avg_attention": 0.21095514297485352,
+      "confidence": "medium"
+    }
+  ],
+  "statistics": {
+    "avg_confidence": 0.35157254586617154,
+    "high_confidence_words": 2,
+    "medium_confidence_words": 3,
+    "low_confidence_words": 1
+  }
+}

SignX/inference_output/detailed_prediction_20260102_202142/171921/interactive_alignment.html

@@ -0,0 +1,579 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Interactive Word-Frame Alignment</title>
+    <style>
+        body {
+            font-family: 'Arial', sans-serif;
+            margin: 20px;
+            background-color: #f5f5f5;
+        }
+        .container {
+            max-width: 1800px;
+            margin: 0 auto;
+            background-color: white;
+            padding: 30px;
+            border-radius: 8px;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        h1 {
+            color: #333;
+            border-bottom: 3px solid #4CAF50;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats {
+            background-color: #E3F2FD;
+            padding: 15px;
+            border-radius: 5px;
+            margin-bottom: 20px;
+            border-left: 4px solid #2196F3;
+            font-size: 14px;
+        }
+        .controls {
+            background-color: #f9f9f9;
+            padding: 20px;
+            border-radius: 5px;
+            margin-bottom: 30px;
+            border: 1px solid #ddd;
+        }
+        .control-group {
+            margin-bottom: 15px;
+        }
+        label {
+            font-weight: bold;
+            display: inline-block;
+            width: 250px;
+            color: #555;
+        }
+        input[type="range"] {
+            width: 400px;
+            vertical-align: middle;
+        }
+        .value-display {
+            display: inline-block;
+            width: 80px;
+            font-family: monospace;
+            font-size: 14px;
+            color: #2196F3;
+            font-weight: bold;
+        }
+        .reset-btn {
+            margin-top: 15px;
+            padding: 10px 25px;
+            background-color: #2196F3;
+            color: white;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 14px;
+            font-weight: bold;
+        }
+        .reset-btn:hover {
+            background-color: #1976D2;
+        }
+        canvas {
+            border: 1px solid #999;
+            display: block;
+            margin: 20px auto;
+            background: white;
+        }
+        .legend {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #fff;
+            border: 1px solid #ddd;
+            border-radius: 5px;
+        }
+        .legend-item {
+            display: inline-block;
+            margin-right: 25px;
+            font-size: 13px;
+            margin-bottom: 10px;
+        }
+        .color-box {
+            display: inline-block;
+            width: 30px;
+            height: 15px;
+            margin-right: 8px;
+            vertical-align: middle;
+            border: 1px solid #666;
+        }
+        .info-panel {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #f9f9f9;
+            border-radius: 5px;
+            border: 1px solid #ddd;
+        }
+        .confidence {
+            display: inline-block;
+            padding: 3px 10px;
+            border-radius: 10px;
+            font-weight: bold;
+            font-size: 11px;
+            text-transform: uppercase;
+        }
+        .confidence.high {
+            background-color: #4CAF50;
+            color: white;
+        }
+        .confidence.medium {
+            background-color: #FF9800;
+            color: white;
+        }
+        .confidence.low {
+            background-color: #f44336;
+            color: white;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1>
+
+        <div class="stats">
+            <strong>Translation:</strong> IX-1p NOT LIKE PINEAPPLE fs-CREAM+CHEESE IX-1p<br>
+            <strong>Total Words:</strong> 6 |
+            <strong>Total Features:</strong> 25
+        </div>
+
+        <div class="controls">
+            <h3>⚙️ Threshold Controls</h3>
+
+            <div class="control-group">
+                <label for="peak-threshold">Peak Threshold (% of max):</label>
+                <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1">
+                <span class="value-display" id="peak-threshold-value">90%</span>
+                <br>
+                <small style="margin-left: 255px; color: #666;">
+                    A frame is considered “significant” if its attention ≥ (peak × threshold%)
+                </small>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-high">High Confidence (avg attn >):</label>
+                <input type="range" id="confidence-high" min="0" max="100" value="50" step="1">
+                <span class="value-display" id="confidence-high-value">0.50</span>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-medium">Medium Confidence (avg attn >):</label>
+                <input type="range" id="confidence-medium" min="0" max="100" value="20" step="1">
+                <span class="value-display" id="confidence-medium-value">0.20</span>
+            </div>
+
+            <button class="reset-btn" onclick="resetDefaults()">
+                Reset to Defaults
+            </button>
+        </div>
+
+        <div>
+            <h3>Word-to-Frame Alignment</h3>
+            <p style="color: #666; font-size: 13px;">
+                Each word appears as a colored block. Width = frame span, ★ = peak frame, waveform = attention trace.
+            </p>
+            <canvas id="alignment-canvas" width="1600" height="600"></canvas>
+
+            <h3 style="margin-top: 30px;">Timeline Progress Bar</h3>
+            <canvas id="timeline-canvas" width="1600" height="100"></canvas>
+
+            <div class="legend">
+                <strong>Legend:</strong><br><br>
+                <div class="legend-item">
+                    <span class="confidence high">High</span>
+                    <span class="confidence medium">Medium</span>
+                    <span class="confidence low">Low</span>
+                    Confidence Levels (opacity reflects confidence)
+                </div>
+                <div class="legend-item">
+                    <span style="color: red; font-size: 20px;">★</span>
+                    Peak Frame (highest attention)
+                </div>
+                <div class="legend-item">
+                    <span style="color: blue;">━</span>
+                    Attention Waveform (within word region)
+                </div>
+            </div>
+        </div>
+
+        <div class="info-panel">
+            <h3>Alignment Details</h3>
+            <div id="alignment-details"></div>
+        </div>
+    </div>
+
+    <script>
+        // Attention data from Python
+        const attentionData = [{"word": "IX-1p", "word_idx": 0, "weights": [0.05238496512174606, 0.0976874902844429, 0.22361433506011963, 0.38998693227767944, 0.17873288691043854, 0.006679870188236237, 0.0036295060999691486, 0.0008571264916099608, 0.0010585205163806677, 0.0008011514437384903, 0.0013172859326004982, 0.001322466996498406, 0.0014414831530302763, 0.0014764397637918591, 0.0014997144462540746, 0.0015036852564662695, 0.0017976462841033936, 0.001069137710146606, 0.0007240657578222454, 0.0011783612426370382, 0.00439043901860714, 0.006896136794239283, 0.011946831829845905, 0.005145119037479162, 0.0028583481907844543]}, {"word": "NOT", "word_idx": 1, "weights": [0.05014055222272873, 0.03570036590099335, 0.024646738544106483, 0.0310512688010931, 0.05971718579530716, 0.5316620469093323, 0.23449379205703735, 0.00529087008908391, 0.0027947064954787493, 0.000875027384608984, 0.0007378943264484406, 0.0004647437308449298, 0.0004135824856348336, 0.000486223550979048, 0.0004921917570754886, 0.0003303340054117143, 0.00015648282715119421, 0.00021145731443539262, 0.00020821671932935715, 0.00010684868902899325, 0.0002003382396651432, 0.00031938249594531953, 0.0009686668636277318, 0.005626516416668892, 0.012904567644000053]}, {"word": "LIKE", "word_idx": 2, "weights": [0.00841811764985323, 0.004028731491416693, 0.0020009633153676987, 0.0016266442835330963, 0.003222851548343897, 0.005865377373993397, 0.007347611710429192, 0.626325249671936, 0.30912983417510986, 0.012665261514484882, 0.006091661285609007, 0.003784160129725933, 0.0020876466296613216, 0.001558956690132618, 0.0008166478946805, 0.0007060404750518501, 0.0006176729220896959, 0.000939459539949894, 0.0002989305939991027, 8.548794721718878e-05, 5.798463826067746e-05, 8.46595867187716e-05, 0.00022771398653276265, 0.000744526507332921, 0.0012678124476224184]}, {"word": "PINEAPPLE", "word_idx": 3, "weights": [0.018990369513630867, 0.005772765725851059, 0.0014627730706706643, 0.0005106691969558597, 0.0005122806178405881, 0.0006774469511583447, 0.0012575318105518818, 0.006952387746423483, 0.02189759910106659, 0.22889001667499542, 0.24596205353736877, 0.1395777314901352, 0.0990370512008667, 0.06233914941549301, 0.03861977905035019, 0.03933922201395035, 0.03362319990992546, 0.02999911829829216, 0.015454845502972603, 0.0040846471674740314, 0.000792931008618325, 0.00038262357702478766, 0.000193710409803316, 0.0007411144906654954, 0.002928979927673936]}, {"word": "fs-CREAM+CHEESE", "word_idx": 4, "weights": [0.06216610223054886, 0.02511739730834961, 0.008011610247194767, 0.0022258462850004435, 0.0013015446020290256, 0.0005411158781498671, 0.0006731877219863236, 0.0020649773068726063, 0.005022048018872738, 0.04262514039874077, 0.05756526440382004, 0.07348567247390747, 0.11768292635679245, 0.10847678780555725, 0.09763970226049423, 0.0874117985367775, 0.06481091678142548, 0.035931553691625595, 0.033074185252189636, 0.033235128968954086, 0.015178116038441658, 0.008458501659333706, 0.004052826203405857, 0.023612597957253456, 0.08963505923748016]}, {"word": "IX-1p", "word_idx": 5, "weights": [0.2090553492307663, 0.07592105120420456, 0.02048420161008835, 0.005190532188862562, 0.003573325462639332, 0.0031765794847160578, 0.002643104176968336, 0.009272502735257149, 0.014098540879786015, 0.03167528286576271, 0.036420486867427826, 0.04336267337203026, 0.03958175703883171, 0.03629697114229202, 0.032348908483982086, 0.02785353548824787, 0.025757484138011932, 0.041921358555555344, 0.04051389917731285, 0.021882373839616776, 0.009477143175899982, 0.006930893752723932, 0.004549332894384861, 0.04515770822763443, 0.21285495162010193]}];
+        const numGlosses = 6;
+        const numFeatures = 25;
+
+        // Colors for different words (matching matplotlib tab20)
+        const colors = [
+            '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
+            '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',
+            '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',
+            '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5'
+        ];
+
+        // Get controls
+        const peakThresholdSlider = document.getElementById('peak-threshold');
+        const peakThresholdValue = document.getElementById('peak-threshold-value');
+        const confidenceHighSlider = document.getElementById('confidence-high');
+        const confidenceHighValue = document.getElementById('confidence-high-value');
+        const confidenceMediumSlider = document.getElementById('confidence-medium');
+        const confidenceMediumValue = document.getElementById('confidence-medium-value');
+        const alignmentCanvas = document.getElementById('alignment-canvas');
+        const timelineCanvas = document.getElementById('timeline-canvas');
+        const alignmentCtx = alignmentCanvas.getContext('2d');
+        const timelineCtx = timelineCanvas.getContext('2d');
+
+        // Update displays when sliders change
+        peakThresholdSlider.oninput = function() {
+            peakThresholdValue.textContent = this.value + '%';
+            updateVisualization();
+        };
+
+        confidenceHighSlider.oninput = function() {
+            confidenceHighValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        confidenceMediumSlider.oninput = function() {
+            confidenceMediumValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        function resetDefaults() {
+            peakThresholdSlider.value = 90;
+            confidenceHighSlider.value = 50;
+            confidenceMediumSlider.value = 20;
+            peakThresholdValue.textContent = '90%';
+            confidenceHighValue.textContent = '0.50';
+            confidenceMediumValue.textContent = '0.20';
+            updateVisualization();
+        }
+
+        function calculateAlignment(weights, peakThreshold) {
+            // Find peak
+            let peakIdx = 0;
+            let peakWeight = weights[0];
+            for (let i = 1; i < weights.length; i++) {
+                if (weights[i] > peakWeight) {
+                    peakWeight = weights[i];
+                    peakIdx = i;
+                }
+            }
+
+            // Find significant frames
+            const threshold = peakWeight * (peakThreshold / 100);
+            let startIdx = peakIdx;
+            let endIdx = peakIdx;
+            let sumWeight = 0;
+            let count = 0;
+
+            for (let i = 0; i < weights.length; i++) {
+                if (weights[i] >= threshold) {
+                    if (i < startIdx) startIdx = i;
+                    if (i > endIdx) endIdx = i;
+                    sumWeight += weights[i];
+                    count++;
+                }
+            }
+
+            const avgWeight = count > 0 ? sumWeight / count : peakWeight;
+
+            return {
+                startIdx: startIdx,
+                endIdx: endIdx,
+                peakIdx: peakIdx,
+                peakWeight: peakWeight,
+                avgWeight: avgWeight,
+                threshold: threshold
+            };
+        }
+
+        function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {
+            if (avgWeight > highThreshold) return 'high';
+            if (avgWeight > mediumThreshold) return 'medium';
+            return 'low';
+        }
+
+        function drawAlignmentChart() {
+            const peakThreshold = parseInt(peakThresholdSlider.value);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            // Canvas dimensions
+            const width = alignmentCanvas.width;
+            const height = alignmentCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const topMargin = 60;
+            const bottomMargin = 80;
+
+            const plotWidth = width - leftMargin - rightMargin;
+            const plotHeight = height - topMargin - bottomMargin;
+
+            const rowHeight = plotHeight / numGlosses;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            alignmentCtx.clearRect(0, 0, width, height);
+
+            // Draw title
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 18px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30);
+            alignmentCtx.font = '13px Arial';
+            alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48);
+
+            // Calculate alignments
+            const alignments = [];
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const data = attentionData[wordIdx];
+                const alignment = calculateAlignment(data.weights, peakThreshold);
+                alignment.word = data.word;
+                alignment.wordIdx = wordIdx;
+                alignment.weights = data.weights;
+                alignments.push(alignment);
+            }
+
+            // Draw grid
+            alignmentCtx.strokeStyle = '#e0e0e0';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numFeatures; i++) {
+                const x = leftMargin + i * featureWidth;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(x, topMargin);
+                alignmentCtx.lineTo(x, topMargin + plotHeight);
+                alignmentCtx.stroke();
+            }
+
+            // Draw word regions
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const y = topMargin + wordIdx * rowHeight;
+
+                // Alpha based on confidence
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                // Draw rectangle for word region
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                alignmentCtx.fillStyle = colors[wordIdx % 20];
+                alignmentCtx.globalAlpha = alpha;
+                alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8);
+                alignmentCtx.globalAlpha = 1.0;
+
+                // Draw border
+                alignmentCtx.strokeStyle = '#000';
+                alignmentCtx.lineWidth = 2;
+                alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8);
+
+                // Draw attention waveform inside rectangle
+                alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.beginPath();
+                for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {
+                    const x = leftMargin + i * featureWidth + featureWidth / 2;
+                    const weight = alignment.weights[i];
+                    const maxWeight = alignment.peakWeight;
+                    const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility
+                    const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6);
+
+                    if (i === alignment.startIdx) {
+                        alignmentCtx.moveTo(x, waveY);
+                    } else {
+                        alignmentCtx.lineTo(x, waveY);
+                    }
+                }
+                alignmentCtx.stroke();
+
+                // Draw word label
+                const labelX = startX + rectWidth / 2;
+                const labelY = y + rowHeight * 0.4;
+
+                alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)';
+                alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24);
+                alignmentCtx.fillStyle = '#fff';
+                alignmentCtx.font = 'bold 13px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, labelX, labelY);
+
+                // Mark peak frame with star
+                const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2;
+                const peakY = y + rowHeight * 0.4;
+
+                // Draw star
+                alignmentCtx.fillStyle = '#ff0000';
+                alignmentCtx.strokeStyle = '#ffff00';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.font = '20px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.strokeText('★', peakX, peakY);
+                alignmentCtx.fillText('★', peakX, peakY);
+
+                // Y-axis label (word names)
+                alignmentCtx.fillStyle = '#333';
+                alignmentCtx.font = '12px Arial';
+                alignmentCtx.textAlign = 'right';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4);
+            }
+
+            // Draw horizontal grid lines
+            alignmentCtx.strokeStyle = '#ccc';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numGlosses; i++) {
+                const y = topMargin + i * rowHeight;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(leftMargin, y);
+                alignmentCtx.lineTo(leftMargin + plotWidth, y);
+                alignmentCtx.stroke();
+            }
+
+            // Draw axes
+            alignmentCtx.strokeStyle = '#000';
+            alignmentCtx.lineWidth = 2;
+            alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight);
+
+            // X-axis labels (frame indices)
+            alignmentCtx.fillStyle = '#000';
+            alignmentCtx.font = '11px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.textBaseline = 'top';
+            for (let i = 0; i < numFeatures; i++) {
+                const x = leftMargin + i * featureWidth + featureWidth / 2;
+                alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10);
+            }
+
+            // Axis titles
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 14px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20);
+
+            alignmentCtx.save();
+            alignmentCtx.translate(30, topMargin + plotHeight / 2);
+            alignmentCtx.rotate(-Math.PI / 2);
+            alignmentCtx.fillText('Generated Word', 0, 0);
+            alignmentCtx.restore();
+
+            return alignments;
+        }
+
+        function drawTimeline(alignments) {
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            const width = timelineCanvas.width;
+            const height = timelineCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const plotWidth = width - leftMargin - rightMargin;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            timelineCtx.clearRect(0, 0, width, height);
+
+            // Background bar
+            timelineCtx.fillStyle = '#ddd';
+            timelineCtx.fillRect(leftMargin, 30, plotWidth, 40);
+            timelineCtx.strokeStyle = '#000';
+            timelineCtx.lineWidth = 2;
+            timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40);
+
+            // Draw word regions on timeline
+            for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                timelineCtx.fillStyle = colors[wordIdx % 20];
+                timelineCtx.globalAlpha = alpha;
+                timelineCtx.fillRect(startX, 30, rectWidth, 40);
+                timelineCtx.globalAlpha = 1.0;
+                timelineCtx.strokeStyle = '#000';
+                timelineCtx.lineWidth = 0.5;
+                timelineCtx.strokeRect(startX, 30, rectWidth, 40);
+            }
+
+            // Title
+            timelineCtx.fillStyle = '#333';
+            timelineCtx.font = 'bold 13px Arial';
+            timelineCtx.textAlign = 'left';
+            timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20);
+        }
+
+        function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {
+            const panel = document.getElementById('alignment-details');
+            let html = '<table style="width: 100%; border-collapse: collapse;">';
+            html += '<tr style="background: #f0f0f0; font-weight: bold;">';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>';
+            html += '</tr>';
+
+            for (const align of alignments) {
+                const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold);
+                const span = align.endIdx - align.startIdx + 1;
+
+                html += '<tr>';
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${align.word}</strong></td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.startIdx} → ${align.endIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.peakIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${span}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.avgWeight.toFixed(4)}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${confidence}">${confidence}</span></td>`;
+                html += '</tr>';
+            }
+
+            html += '</table>';
+            panel.innerHTML = html;
+        }
+
+        function updateVisualization() {
+            const alignments = drawAlignmentChart();
+            drawTimeline(alignments);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+            updateDetailsPanel(alignments, highThreshold, mediumThreshold);
+        }
+
+        // Event listeners for sliders
+        peakSlider.addEventListener('input', function() {
+            peakValue.textContent = peakSlider.value + '%';
+            updateVisualization();
+        });
+
+        confidenceHighSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceHighSlider.value) / 100;
+            confidenceHighValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        confidenceMediumSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceMediumSlider.value) / 100;
+            confidenceMediumValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        // Initial visualization
+        updateVisualization();
+    </script>
+</body>
+</html>

SignX/inference_output/detailed_prediction_20260102_202142/171921/translation.txt

@@ -0,0 +1,3 @@
+With BPE: IX-1p NOT LIKE PINEAPPLE fs-@@ CREA@@ M@@ +@@ CHEESE IX-1p
+Clean: IX-1p NOT LIKE PINEAPPLE fs-CREAM+CHEESE IX-1p
+Ground Truth: IX-1p NOT LIKE PINEAPPLE fs-CREAM+CHEESE IX-1p

SignX/inference_output/detailed_prediction_20260102_202302/173238/173238.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48c605e2c0dbd04fe25871a7b2e270615d02a198567f3f903c3ae7da68bcf8ca
+size 98921

SignX/inference_output/detailed_prediction_20260102_202302/173238/analysis_report.txt

@@ -0,0 +1,40 @@
+================================================================================
+  Sign Language Recognition - Attention Analysis Report
+================================================================================
+
+Generated at: 2026-01-02 20:23:07
+
+Translation:
+--------------------------------------------------------------------------------
+#NO IX-1p USE CORRECT FOR
+
+Video info:
+--------------------------------------------------------------------------------
+Total feature frames: 25
+Word count: 5
+
+Attention tensor:
+--------------------------------------------------------------------------------
+Shape: (24, 25)
+  - Decoder steps: 24
+
+Word-to-frame details:
+================================================================================
+No.   Word                 Frames          Peak     Attn     Conf      
+--------------------------------------------------------------------------------
+1     #NO                  7-7             7        0.532    high      
+2     IX-1p                0-7             7        0.116    low       
+3     USE                  0-24            24       0.153    low       
+4     CORRECT              11-11           11       0.336    medium    
+5     FOR                  12-13           12       0.427    medium    
+
+================================================================================
+
+Summary:
+--------------------------------------------------------------------------------
+Average attention weight: 0.313
+High-confidence words: 1 (20.0%)
+Medium-confidence words: 2 (40.0%)
+Low-confidence words: 2 (40.0%)
+
+================================================================================

SignX/inference_output/detailed_prediction_20260102_202302/173238/attention_keyframes/keyframes_index.txt

@@ -0,0 +1,33 @@
+Attention Keyframe Index
+============================================================
+
+Sample directory: /research/cbim/vast/sf895/code/Sign-X/output/huggingface_asllrp_repo/SignX/inference_output/detailed_prediction_20260102_202302/173238
+Video path: /common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/173238.mp4
+Total keyframes: 24
+
+Keyframe list:
+------------------------------------------------------------
+Gloss   0: keyframe_000_feat7_frame28_att0.532.jpg
+Gloss   1: keyframe_001_feat7_frame28_att0.120.jpg
+Gloss   2: keyframe_002_feat24_frame92_att0.156.jpg
+Gloss   3: keyframe_003_feat11_frame43_att0.336.jpg
+Gloss   4: keyframe_004_feat12_frame46_att0.448.jpg
+Gloss   5: keyframe_005_feat15_frame58_att0.293.jpg
+Gloss   6: keyframe_006_feat15_frame58_att0.212.jpg
+Gloss   7: keyframe_007_feat16_frame61_att0.140.jpg
+Gloss   8: keyframe_008_feat22_frame84_att0.093.jpg
+Gloss   9: keyframe_009_feat16_frame61_att0.149.jpg
+Gloss  10: keyframe_010_feat22_frame84_att0.095.jpg
+Gloss  11: keyframe_011_feat20_frame76_att0.127.jpg
+Gloss  12: keyframe_012_feat21_frame80_att0.131.jpg
+Gloss  13: keyframe_013_feat1_frame5_att0.101.jpg
+Gloss  14: keyframe_014_feat22_frame84_att0.113.jpg
+Gloss  15: keyframe_015_feat22_frame84_att0.122.jpg
+Gloss  16: keyframe_016_feat22_frame84_att0.122.jpg
+Gloss  17: keyframe_017_feat22_frame84_att0.139.jpg
+Gloss  18: keyframe_018_feat1_frame5_att0.119.jpg
+Gloss  19: keyframe_019_feat22_frame84_att0.120.jpg
+Gloss  20: keyframe_020_feat22_frame84_att0.112.jpg
+Gloss  21: keyframe_021_feat1_frame5_att0.118.jpg
+Gloss  22: keyframe_022_feat1_frame5_att0.121.jpg
+Gloss  23: keyframe_023_feat22_frame84_att0.124.jpg

SignX/inference_output/detailed_prediction_20260102_202302/173238/attention_weights.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7433f9d84fa778550beaf67774d607f501c246976980d5c06143af061d2a5fbf
+size 2528

SignX/inference_output/detailed_prediction_20260102_202302/173238/debug_video_path.txt

@@ -0,0 +1,4 @@
+video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/173238.mp4'
+video_path type = <class 'str'>
+video_path is None: False
+bool(video_path): True

SignX/inference_output/detailed_prediction_20260102_202302/173238/feature_frame_mapping.json

@@ -0,0 +1,158 @@
+{
+  "original_frame_count": 94,
+  "feature_count": 25,
+  "downsampling_ratio": 3.76,
+  "fps": 24.0,
+  "mapping": [
+    {
+      "feature_index": 0,
+      "frame_start": 0,
+      "frame_end": 3,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 1,
+      "frame_start": 3,
+      "frame_end": 7,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 2,
+      "frame_start": 7,
+      "frame_end": 11,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 3,
+      "frame_start": 11,
+      "frame_end": 15,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 4,
+      "frame_start": 15,
+      "frame_end": 18,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 5,
+      "frame_start": 18,
+      "frame_end": 22,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 6,
+      "frame_start": 22,
+      "frame_end": 26,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 7,
+      "frame_start": 26,
+      "frame_end": 30,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 8,
+      "frame_start": 30,
+      "frame_end": 33,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 9,
+      "frame_start": 33,
+      "frame_end": 37,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 10,
+      "frame_start": 37,
+      "frame_end": 41,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 11,
+      "frame_start": 41,
+      "frame_end": 45,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 12,
+      "frame_start": 45,
+      "frame_end": 48,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 13,
+      "frame_start": 48,
+      "frame_end": 52,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 14,
+      "frame_start": 52,
+      "frame_end": 56,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 15,
+      "frame_start": 56,
+      "frame_end": 60,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 16,
+      "frame_start": 60,
+      "frame_end": 63,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 17,
+      "frame_start": 63,
+      "frame_end": 67,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 18,
+      "frame_start": 67,
+      "frame_end": 71,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 19,
+      "frame_start": 71,
+      "frame_end": 75,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 20,
+      "frame_start": 75,
+      "frame_end": 78,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 21,
+      "frame_start": 78,
+      "frame_end": 82,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 22,
+      "frame_start": 82,
+      "frame_end": 86,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 23,
+      "frame_start": 86,
+      "frame_end": 90,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 24,
+      "frame_start": 90,
+      "frame_end": 94,
+      "frame_count": 4
+    }
+  ]
+}

SignX/inference_output/detailed_prediction_20260102_202302/173238/frame_alignment.json

@@ -0,0 +1,59 @@
+{
+  "translation": "#NO IX-1p USE CORRECT FOR",
+  "words": [
+    "#NO",
+    "IX-1p",
+    "USE",
+    "CORRECT",
+    "FOR"
+  ],
+  "total_video_frames": 25,
+  "frame_ranges": [
+    {
+      "word": "#NO",
+      "start_frame": 7,
+      "end_frame": 7,
+      "peak_frame": 7,
+      "avg_attention": 0.5316407680511475,
+      "confidence": "high"
+    },
+    {
+      "word": "IX-1p",
+      "start_frame": 0,
+      "end_frame": 7,
+      "peak_frame": 7,
+      "avg_attention": 0.1155116930603981,
+      "confidence": "low"
+    },
+    {
+      "word": "USE",
+      "start_frame": 0,
+      "end_frame": 24,
+      "peak_frame": 24,
+      "avg_attention": 0.15302085876464844,
+      "confidence": "low"
+    },
+    {
+      "word": "CORRECT",
+      "start_frame": 11,
+      "end_frame": 11,
+      "peak_frame": 11,
+      "avg_attention": 0.33560681343078613,
+      "confidence": "medium"
+    },
+    {
+      "word": "FOR",
+      "start_frame": 12,
+      "end_frame": 13,
+      "peak_frame": 12,
+      "avg_attention": 0.42703279852867126,
+      "confidence": "medium"
+    }
+  ],
+  "statistics": {
+    "avg_confidence": 0.3125625863671303,
+    "high_confidence_words": 1,
+    "medium_confidence_words": 2,
+    "low_confidence_words": 2
+  }
+}

SignX/inference_output/detailed_prediction_20260102_202302/173238/interactive_alignment.html

@@ -0,0 +1,579 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Interactive Word-Frame Alignment</title>
+    <style>
+        body {
+            font-family: 'Arial', sans-serif;
+            margin: 20px;
+            background-color: #f5f5f5;
+        }
+        .container {
+            max-width: 1800px;
+            margin: 0 auto;
+            background-color: white;
+            padding: 30px;
+            border-radius: 8px;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        h1 {
+            color: #333;
+            border-bottom: 3px solid #4CAF50;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats {
+            background-color: #E3F2FD;
+            padding: 15px;
+            border-radius: 5px;
+            margin-bottom: 20px;
+            border-left: 4px solid #2196F3;
+            font-size: 14px;
+        }
+        .controls {
+            background-color: #f9f9f9;
+            padding: 20px;
+            border-radius: 5px;
+            margin-bottom: 30px;
+            border: 1px solid #ddd;
+        }
+        .control-group {
+            margin-bottom: 15px;
+        }
+        label {
+            font-weight: bold;
+            display: inline-block;
+            width: 250px;
+            color: #555;
+        }
+        input[type="range"] {
+            width: 400px;
+            vertical-align: middle;
+        }
+        .value-display {
+            display: inline-block;
+            width: 80px;
+            font-family: monospace;
+            font-size: 14px;
+            color: #2196F3;
+            font-weight: bold;
+        }
+        .reset-btn {
+            margin-top: 15px;
+            padding: 10px 25px;
+            background-color: #2196F3;
+            color: white;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 14px;
+            font-weight: bold;
+        }
+        .reset-btn:hover {
+            background-color: #1976D2;
+        }
+        canvas {
+            border: 1px solid #999;
+            display: block;
+            margin: 20px auto;
+            background: white;
+        }
+        .legend {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #fff;
+            border: 1px solid #ddd;
+            border-radius: 5px;
+        }
+        .legend-item {
+            display: inline-block;
+            margin-right: 25px;
+            font-size: 13px;
+            margin-bottom: 10px;
+        }
+        .color-box {
+            display: inline-block;
+            width: 30px;
+            height: 15px;
+            margin-right: 8px;
+            vertical-align: middle;
+            border: 1px solid #666;
+        }
+        .info-panel {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #f9f9f9;
+            border-radius: 5px;
+            border: 1px solid #ddd;
+        }
+        .confidence {
+            display: inline-block;
+            padding: 3px 10px;
+            border-radius: 10px;
+            font-weight: bold;
+            font-size: 11px;
+            text-transform: uppercase;
+        }
+        .confidence.high {
+            background-color: #4CAF50;
+            color: white;
+        }
+        .confidence.medium {
+            background-color: #FF9800;
+            color: white;
+        }
+        .confidence.low {
+            background-color: #f44336;
+            color: white;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1>
+
+        <div class="stats">
+            <strong>Translation:</strong> #NO IX-1p USE CORRECT FOR<br>
+            <strong>Total Words:</strong> 5 |
+            <strong>Total Features:</strong> 25
+        </div>
+
+        <div class="controls">
+            <h3>⚙️ Threshold Controls</h3>
+
+            <div class="control-group">
+                <label for="peak-threshold">Peak Threshold (% of max):</label>
+                <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1">
+                <span class="value-display" id="peak-threshold-value">90%</span>
+                <br>
+                <small style="margin-left: 255px; color: #666;">
+                    A frame is considered “significant” if its attention ≥ (peak × threshold%)
+                </small>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-high">High Confidence (avg attn >):</label>
+                <input type="range" id="confidence-high" min="0" max="100" value="50" step="1">
+                <span class="value-display" id="confidence-high-value">0.50</span>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-medium">Medium Confidence (avg attn >):</label>
+                <input type="range" id="confidence-medium" min="0" max="100" value="20" step="1">
+                <span class="value-display" id="confidence-medium-value">0.20</span>
+            </div>
+
+            <button class="reset-btn" onclick="resetDefaults()">
+                Reset to Defaults
+            </button>
+        </div>
+
+        <div>
+            <h3>Word-to-Frame Alignment</h3>
+            <p style="color: #666; font-size: 13px;">
+                Each word appears as a colored block. Width = frame span, ★ = peak frame, waveform = attention trace.
+            </p>
+            <canvas id="alignment-canvas" width="1600" height="600"></canvas>
+
+            <h3 style="margin-top: 30px;">Timeline Progress Bar</h3>
+            <canvas id="timeline-canvas" width="1600" height="100"></canvas>
+
+            <div class="legend">
+                <strong>Legend:</strong><br><br>
+                <div class="legend-item">
+                    <span class="confidence high">High</span>
+                    <span class="confidence medium">Medium</span>
+                    <span class="confidence low">Low</span>
+                    Confidence Levels (opacity reflects confidence)
+                </div>
+                <div class="legend-item">
+                    <span style="color: red; font-size: 20px;">★</span>
+                    Peak Frame (highest attention)
+                </div>
+                <div class="legend-item">
+                    <span style="color: blue;">━</span>
+                    Attention Waveform (within word region)
+                </div>
+            </div>
+        </div>
+
+        <div class="info-panel">
+            <h3>Alignment Details</h3>
+            <div id="alignment-details"></div>
+        </div>
+    </div>
+
+    <script>
+        // Attention data from Python
+        const attentionData = [{"word": "#NO", "word_idx": 0, "weights": [0.012782563455402851, 0.011981594376266003, 0.01150900311768055, 0.010720917023718357, 0.009659628383815289, 0.015060730278491974, 0.22624146938323975, 0.5316407680511475, 0.13897353410720825, 0.009811749681830406, 0.005086212884634733, 0.006459908559918404, 0.0025683066342025995, 0.0020257264841347933, 0.0009967696387320757, 0.0005196572747081518, 0.0011819832725450397, 0.00012239675561431795, 3.519998426781967e-05, 9.520346793578938e-05, 0.00018389770411886275, 0.0002500070841051638, 0.00037205920671112835, 0.000725841848179698, 0.0009948475053533912]}, {"word": "IX-1p", "word_idx": 1, "weights": [0.1154966726899147, 0.11839839816093445, 0.10858778655529022, 0.09947863966226578, 0.09086362272500992, 0.09146849811077118, 0.07603903114795685, 0.11956391483545303, 0.07710319012403488, 0.019207479432225227, 0.00366093497723341, 0.0016571247251704335, 0.004489341285079718, 0.002439837669953704, 0.001201692852191627, 0.0005549773923121393, 0.0009384832228533924, 0.0005504037253558636, 0.0003465348854660988, 0.0008819004287943244, 0.0016493259463459253, 0.0022654831409454346, 0.00884854607284069, 0.023778975009918213, 0.030529193580150604]}, {"word": "USE", "word_idx": 2, "weights": [0.14988766610622406, 0.12994979321956635, 0.10789304971694946, 0.08856625854969025, 0.07732779532670975, 0.04263059422373772, 0.004822988994419575, 0.00905913207679987, 0.015443230979144573, 0.03147038072347641, 0.014399666339159012, 0.008924762718379498, 0.00825969036668539, 0.003495264332741499, 0.0015463761519640684, 0.0006412258371710777, 0.0011047126026824117, 0.0005361629882827401, 0.0005916748195886612, 0.0014564881566911936, 0.002115124138072133, 0.002776387147605419, 0.021915478631854057, 0.1190321147441864, 0.15615403652191162]}, {"word": "CORRECT", "word_idx": 3, "weights": [0.06937894970178604, 0.051489513367414474, 0.0445125512778759, 0.03950398042798042, 0.037877995520830154, 0.02452961727976799, 0.007906158454716206, 0.016188515350222588, 0.02675747498869896, 0.07620300352573395, 0.19777104258537292, 0.33560681343078613, 0.005275052040815353, 0.005951147526502609, 0.007349252700805664, 0.004849528893828392, 0.007685650140047073, 0.001965776551514864, 0.0007187770679593086, 0.0009715624619275331, 0.0008626979542896152, 0.0013258870458230376, 0.005855972412973642, 0.013570844195783138, 0.01589221879839897]}, {"word": "FOR", "word_idx": 4, "weights": [0.00033176588476635516, 0.00020028719154652208, 0.0001781086903065443, 0.00017031899187713861, 0.00017076355288736522, 0.00019543150847312063, 0.0007411232218146324, 0.0010241027921438217, 0.0017877332866191864, 0.00046761787962168455, 0.0013574255863204598, 0.006706785876303911, 0.44760289788246155, 0.406462699174881, 0.06950433552265167, 0.02205367386341095, 0.03949587419629097, 0.0011918380623683333, 7.672667561564595e-05, 9.517547732684761e-05, 5.0555227062432095e-05, 1.5771540347486734e-05, 1.0371734788350295e-05, 4.547540083876811e-05, 6.314207712421194e-05]}];
+        const numGlosses = 5;
+        const numFeatures = 25;
+
+        // Colors for different words (matching matplotlib tab20)
+        const colors = [
+            '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
+            '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',
+            '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',
+            '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5'
+        ];
+
+        // Get controls
+        const peakThresholdSlider = document.getElementById('peak-threshold');
+        const peakThresholdValue = document.getElementById('peak-threshold-value');
+        const confidenceHighSlider = document.getElementById('confidence-high');
+        const confidenceHighValue = document.getElementById('confidence-high-value');
+        const confidenceMediumSlider = document.getElementById('confidence-medium');
+        const confidenceMediumValue = document.getElementById('confidence-medium-value');
+        const alignmentCanvas = document.getElementById('alignment-canvas');
+        const timelineCanvas = document.getElementById('timeline-canvas');
+        const alignmentCtx = alignmentCanvas.getContext('2d');
+        const timelineCtx = timelineCanvas.getContext('2d');
+
+        // Update displays when sliders change
+        peakThresholdSlider.oninput = function() {
+            peakThresholdValue.textContent = this.value + '%';
+            updateVisualization();
+        };
+
+        confidenceHighSlider.oninput = function() {
+            confidenceHighValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        confidenceMediumSlider.oninput = function() {
+            confidenceMediumValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        function resetDefaults() {
+            peakThresholdSlider.value = 90;
+            confidenceHighSlider.value = 50;
+            confidenceMediumSlider.value = 20;
+            peakThresholdValue.textContent = '90%';
+            confidenceHighValue.textContent = '0.50';
+            confidenceMediumValue.textContent = '0.20';
+            updateVisualization();
+        }
+
+        function calculateAlignment(weights, peakThreshold) {
+            // Find peak
+            let peakIdx = 0;
+            let peakWeight = weights[0];
+            for (let i = 1; i < weights.length; i++) {
+                if (weights[i] > peakWeight) {
+                    peakWeight = weights[i];
+                    peakIdx = i;
+                }
+            }
+
+            // Find significant frames
+            const threshold = peakWeight * (peakThreshold / 100);
+            let startIdx = peakIdx;
+            let endIdx = peakIdx;
+            let sumWeight = 0;
+            let count = 0;
+
+            for (let i = 0; i < weights.length; i++) {
+                if (weights[i] >= threshold) {
+                    if (i < startIdx) startIdx = i;
+                    if (i > endIdx) endIdx = i;
+                    sumWeight += weights[i];
+                    count++;
+                }
+            }
+
+            const avgWeight = count > 0 ? sumWeight / count : peakWeight;
+
+            return {
+                startIdx: startIdx,
+                endIdx: endIdx,
+                peakIdx: peakIdx,
+                peakWeight: peakWeight,
+                avgWeight: avgWeight,
+                threshold: threshold
+            };
+        }
+
+        function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {
+            if (avgWeight > highThreshold) return 'high';
+            if (avgWeight > mediumThreshold) return 'medium';
+            return 'low';
+        }
+
+        function drawAlignmentChart() {
+            const peakThreshold = parseInt(peakThresholdSlider.value);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            // Canvas dimensions
+            const width = alignmentCanvas.width;
+            const height = alignmentCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const topMargin = 60;
+            const bottomMargin = 80;
+
+            const plotWidth = width - leftMargin - rightMargin;
+            const plotHeight = height - topMargin - bottomMargin;
+
+            const rowHeight = plotHeight / numGlosses;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            alignmentCtx.clearRect(0, 0, width, height);
+
+            // Draw title
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 18px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30);
+            alignmentCtx.font = '13px Arial';
+            alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48);
+
+            // Calculate alignments
+            const alignments = [];
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const data = attentionData[wordIdx];
+                const alignment = calculateAlignment(data.weights, peakThreshold);
+                alignment.word = data.word;
+                alignment.wordIdx = wordIdx;
+                alignment.weights = data.weights;
+                alignments.push(alignment);
+            }
+
+            // Draw grid
+            alignmentCtx.strokeStyle = '#e0e0e0';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numFeatures; i++) {
+                const x = leftMargin + i * featureWidth;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(x, topMargin);
+                alignmentCtx.lineTo(x, topMargin + plotHeight);
+                alignmentCtx.stroke();
+            }
+
+            // Draw word regions
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const y = topMargin + wordIdx * rowHeight;
+
+                // Alpha based on confidence
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                // Draw rectangle for word region
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                alignmentCtx.fillStyle = colors[wordIdx % 20];
+                alignmentCtx.globalAlpha = alpha;
+                alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8);
+                alignmentCtx.globalAlpha = 1.0;
+
+                // Draw border
+                alignmentCtx.strokeStyle = '#000';
+                alignmentCtx.lineWidth = 2;
+                alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8);
+
+                // Draw attention waveform inside rectangle
+                alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.beginPath();
+                for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {
+                    const x = leftMargin + i * featureWidth + featureWidth / 2;
+                    const weight = alignment.weights[i];
+                    const maxWeight = alignment.peakWeight;
+                    const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility
+                    const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6);
+
+                    if (i === alignment.startIdx) {
+                        alignmentCtx.moveTo(x, waveY);
+                    } else {
+                        alignmentCtx.lineTo(x, waveY);
+                    }
+                }
+                alignmentCtx.stroke();
+
+                // Draw word label
+                const labelX = startX + rectWidth / 2;
+                const labelY = y + rowHeight * 0.4;
+
+                alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)';
+                alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24);
+                alignmentCtx.fillStyle = '#fff';
+                alignmentCtx.font = 'bold 13px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, labelX, labelY);
+
+                // Mark peak frame with star
+                const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2;
+                const peakY = y + rowHeight * 0.4;
+
+                // Draw star
+                alignmentCtx.fillStyle = '#ff0000';
+                alignmentCtx.strokeStyle = '#ffff00';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.font = '20px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.strokeText('★', peakX, peakY);
+                alignmentCtx.fillText('★', peakX, peakY);
+
+                // Y-axis label (word names)
+                alignmentCtx.fillStyle = '#333';
+                alignmentCtx.font = '12px Arial';
+                alignmentCtx.textAlign = 'right';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4);
+            }
+
+            // Draw horizontal grid lines
+            alignmentCtx.strokeStyle = '#ccc';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numGlosses; i++) {
+                const y = topMargin + i * rowHeight;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(leftMargin, y);
+                alignmentCtx.lineTo(leftMargin + plotWidth, y);
+                alignmentCtx.stroke();
+            }
+
+            // Draw axes
+            alignmentCtx.strokeStyle = '#000';
+            alignmentCtx.lineWidth = 2;
+            alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight);
+
+            // X-axis labels (frame indices)
+            alignmentCtx.fillStyle = '#000';
+            alignmentCtx.font = '11px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.textBaseline = 'top';
+            for (let i = 0; i < numFeatures; i++) {
+                const x = leftMargin + i * featureWidth + featureWidth / 2;
+                alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10);
+            }
+
+            // Axis titles
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 14px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20);
+
+            alignmentCtx.save();
+            alignmentCtx.translate(30, topMargin + plotHeight / 2);
+            alignmentCtx.rotate(-Math.PI / 2);
+            alignmentCtx.fillText('Generated Word', 0, 0);
+            alignmentCtx.restore();
+
+            return alignments;
+        }
+
+        function drawTimeline(alignments) {
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            const width = timelineCanvas.width;
+            const height = timelineCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const plotWidth = width - leftMargin - rightMargin;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            timelineCtx.clearRect(0, 0, width, height);
+
+            // Background bar
+            timelineCtx.fillStyle = '#ddd';
+            timelineCtx.fillRect(leftMargin, 30, plotWidth, 40);
+            timelineCtx.strokeStyle = '#000';
+            timelineCtx.lineWidth = 2;
+            timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40);
+
+            // Draw word regions on timeline
+            for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                timelineCtx.fillStyle = colors[wordIdx % 20];
+                timelineCtx.globalAlpha = alpha;
+                timelineCtx.fillRect(startX, 30, rectWidth, 40);
+                timelineCtx.globalAlpha = 1.0;
+                timelineCtx.strokeStyle = '#000';
+                timelineCtx.lineWidth = 0.5;
+                timelineCtx.strokeRect(startX, 30, rectWidth, 40);
+            }
+
+            // Title
+            timelineCtx.fillStyle = '#333';
+            timelineCtx.font = 'bold 13px Arial';
+            timelineCtx.textAlign = 'left';
+            timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20);
+        }
+
+        function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {
+            const panel = document.getElementById('alignment-details');
+            let html = '<table style="width: 100%; border-collapse: collapse;">';
+            html += '<tr style="background: #f0f0f0; font-weight: bold;">';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>';
+            html += '</tr>';
+
+            for (const align of alignments) {
+                const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold);
+                const span = align.endIdx - align.startIdx + 1;
+
+                html += '<tr>';
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${align.word}</strong></td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.startIdx} → ${align.endIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.peakIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${span}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.avgWeight.toFixed(4)}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${confidence}">${confidence}</span></td>`;
+                html += '</tr>';
+            }
+
+            html += '</table>';
+            panel.innerHTML = html;
+        }
+
+        function updateVisualization() {
+            const alignments = drawAlignmentChart();
+            drawTimeline(alignments);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+            updateDetailsPanel(alignments, highThreshold, mediumThreshold);
+        }
+
+        // Event listeners for sliders
+        peakSlider.addEventListener('input', function() {
+            peakValue.textContent = peakSlider.value + '%';
+            updateVisualization();
+        });
+
+        confidenceHighSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceHighSlider.value) / 100;
+            confidenceHighValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        confidenceMediumSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceMediumSlider.value) / 100;
+            confidenceMediumValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        // Initial visualization
+        updateVisualization();
+    </script>
+</body>
+</html>

SignX/inference_output/detailed_prediction_20260102_202302/173238/translation.txt

@@ -0,0 +1,3 @@
+With BPE: #@@ N@@ O IX-1p USE CORRECT F@@ O@@ R
+Clean: #NO IX-1p USE CORRECT FOR
+Ground Truth: #NO IX-1p USE CORRECT KEY

SignX/inference_output/detailed_prediction_20260102_202418/173745/173745.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:289b2fa3e751823c99b78a40e38ff799dfb7e28cdc53053b045c5e9dbb15d7fb
+size 494802

SignX/inference_output/detailed_prediction_20260102_202418/173745/analysis_report.txt

@@ -0,0 +1,37 @@
+================================================================================
+  Sign Language Recognition - Attention Analysis Report
+================================================================================
+
+Generated at: 2026-01-02 20:24:24
+
+Translation:
+--------------------------------------------------------------------------------
+CAR BREAK-DOWN
+
+Video info:
+--------------------------------------------------------------------------------
+Total feature frames: 19
+Word count: 2
+
+Attention tensor:
+--------------------------------------------------------------------------------
+Shape: (23, 19)
+  - Decoder steps: 23
+
+Word-to-frame details:
+================================================================================
+No.   Word                 Frames          Peak     Attn     Conf      
+--------------------------------------------------------------------------------
+1     CAR                  4-4             4        0.233    medium    
+2     BREAK-DOWN           0-1             0        0.144    low       
+
+================================================================================
+
+Summary:
+--------------------------------------------------------------------------------
+Average attention weight: 0.189
+High-confidence words: 0 (0.0%)
+Medium-confidence words: 1 (50.0%)
+Low-confidence words: 1 (50.0%)
+
+================================================================================

SignX/inference_output/detailed_prediction_20260102_202418/173745/attention_keyframes/keyframes_index.txt

@@ -0,0 +1,32 @@
+Attention Keyframe Index
+============================================================
+
+Sample directory: /research/cbim/vast/sf895/code/Sign-X/output/huggingface_asllrp_repo/SignX/inference_output/detailed_prediction_20260102_202418/173745
+Video path: /common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/173745.mp4
+Total keyframes: 23
+
+Keyframe list:
+------------------------------------------------------------
+Gloss   0: keyframe_000_feat4_frame16_att0.233.jpg
+Gloss   1: keyframe_001_feat0_frame1_att0.149.jpg
+Gloss   2: keyframe_002_feat18_frame70_att0.165.jpg
+Gloss   3: keyframe_003_feat9_frame35_att0.221.jpg
+Gloss   4: keyframe_004_feat0_frame1_att0.123.jpg
+Gloss   5: keyframe_005_feat18_frame70_att0.145.jpg
+Gloss   6: keyframe_006_feat18_frame70_att0.115.jpg
+Gloss   7: keyframe_007_feat0_frame1_att0.131.jpg
+Gloss   8: keyframe_008_feat0_frame1_att0.154.jpg
+Gloss   9: keyframe_009_feat0_frame1_att0.214.jpg
+Gloss  10: keyframe_010_feat17_frame66_att0.090.jpg
+Gloss  11: keyframe_011_feat0_frame1_att0.191.jpg
+Gloss  12: keyframe_012_feat0_frame1_att0.152.jpg
+Gloss  13: keyframe_013_feat0_frame1_att0.198.jpg
+Gloss  14: keyframe_014_feat0_frame1_att0.255.jpg
+Gloss  15: keyframe_015_feat0_frame1_att0.268.jpg
+Gloss  16: keyframe_016_feat0_frame1_att0.197.jpg
+Gloss  17: keyframe_017_feat0_frame1_att0.193.jpg
+Gloss  18: keyframe_018_feat0_frame1_att0.186.jpg
+Gloss  19: keyframe_019_feat0_frame1_att0.222.jpg
+Gloss  20: keyframe_020_feat0_frame1_att0.263.jpg
+Gloss  21: keyframe_021_feat0_frame1_att0.254.jpg
+Gloss  22: keyframe_022_feat0_frame1_att0.204.jpg

SignX/inference_output/detailed_prediction_20260102_202418/173745/attention_weights.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebd77af65d3a9c36f3bdaa8b3f52b230fc8990fde9bdcf42c033e12e331a9c82
+size 1876

SignX/inference_output/detailed_prediction_20260102_202418/173745/debug_video_path.txt

@@ -0,0 +1,4 @@
+video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/173745.mp4'
+video_path type = <class 'str'>
+video_path is None: False
+bool(video_path): True

SignX/inference_output/detailed_prediction_20260102_202418/173745/frame_alignment.json

@@ -0,0 +1,32 @@
+{
+  "translation": "CAR BREAK-DOWN",
+  "words": [
+    "CAR",
+    "BREAK-DOWN"
+  ],
+  "total_video_frames": 19,
+  "frame_ranges": [
+    {
+      "word": "CAR",
+      "start_frame": 4,
+      "end_frame": 4,
+      "peak_frame": 4,
+      "avg_attention": 0.2333953082561493,
+      "confidence": "medium"
+    },
+    {
+      "word": "BREAK-DOWN",
+      "start_frame": 0,
+      "end_frame": 1,
+      "peak_frame": 0,
+      "avg_attention": 0.14435341954231262,
+      "confidence": "low"
+    }
+  ],
+  "statistics": {
+    "avg_confidence": 0.18887436389923096,
+    "high_confidence_words": 0,
+    "medium_confidence_words": 1,
+    "low_confidence_words": 1
+  }
+}

SignX/inference_output/detailed_prediction_20260102_202418/173745/interactive_alignment.html

@@ -0,0 +1,579 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Interactive Word-Frame Alignment</title>
+    <style>
+        body {
+            font-family: 'Arial', sans-serif;
+            margin: 20px;
+            background-color: #f5f5f5;
+        }
+        .container {
+            max-width: 1800px;
+            margin: 0 auto;
+            background-color: white;
+            padding: 30px;
+            border-radius: 8px;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        h1 {
+            color: #333;
+            border-bottom: 3px solid #4CAF50;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats {
+            background-color: #E3F2FD;
+            padding: 15px;
+            border-radius: 5px;
+            margin-bottom: 20px;
+            border-left: 4px solid #2196F3;
+            font-size: 14px;
+        }
+        .controls {
+            background-color: #f9f9f9;
+            padding: 20px;
+            border-radius: 5px;
+            margin-bottom: 30px;
+            border: 1px solid #ddd;
+        }
+        .control-group {
+            margin-bottom: 15px;
+        }
+        label {
+            font-weight: bold;
+            display: inline-block;
+            width: 250px;
+            color: #555;
+        }
+        input[type="range"] {
+            width: 400px;
+            vertical-align: middle;
+        }
+        .value-display {
+            display: inline-block;
+            width: 80px;
+            font-family: monospace;
+            font-size: 14px;
+            color: #2196F3;
+            font-weight: bold;
+        }
+        .reset-btn {
+            margin-top: 15px;
+            padding: 10px 25px;
+            background-color: #2196F3;
+            color: white;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 14px;
+            font-weight: bold;
+        }
+        .reset-btn:hover {
+            background-color: #1976D2;
+        }
+        canvas {
+            border: 1px solid #999;
+            display: block;
+            margin: 20px auto;
+            background: white;
+        }
+        .legend {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #fff;
+            border: 1px solid #ddd;
+            border-radius: 5px;
+        }
+        .legend-item {
+            display: inline-block;
+            margin-right: 25px;
+            font-size: 13px;
+            margin-bottom: 10px;
+        }
+        .color-box {
+            display: inline-block;
+            width: 30px;
+            height: 15px;
+            margin-right: 8px;
+            vertical-align: middle;
+            border: 1px solid #666;
+        }
+        .info-panel {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #f9f9f9;
+            border-radius: 5px;
+            border: 1px solid #ddd;
+        }
+        .confidence {
+            display: inline-block;
+            padding: 3px 10px;
+            border-radius: 10px;
+            font-weight: bold;
+            font-size: 11px;
+            text-transform: uppercase;
+        }
+        .confidence.high {
+            background-color: #4CAF50;
+            color: white;
+        }
+        .confidence.medium {
+            background-color: #FF9800;
+            color: white;
+        }
+        .confidence.low {
+            background-color: #f44336;
+            color: white;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1>
+
+        <div class="stats">
+            <strong>Translation:</strong> CAR BREAK-DOWN<br>
+            <strong>Total Words:</strong> 2 |
+            <strong>Total Features:</strong> 19
+        </div>
+
+        <div class="controls">
+            <h3>⚙️ Threshold Controls</h3>
+
+            <div class="control-group">
+                <label for="peak-threshold">Peak Threshold (% of max):</label>
+                <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1">
+                <span class="value-display" id="peak-threshold-value">90%</span>
+                <br>
+                <small style="margin-left: 255px; color: #666;">
+                    A frame is considered “significant” if its attention ≥ (peak × threshold%)
+                </small>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-high">High Confidence (avg attn >):</label>
+                <input type="range" id="confidence-high" min="0" max="100" value="50" step="1">
+                <span class="value-display" id="confidence-high-value">0.50</span>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-medium">Medium Confidence (avg attn >):</label>
+                <input type="range" id="confidence-medium" min="0" max="100" value="20" step="1">
+                <span class="value-display" id="confidence-medium-value">0.20</span>
+            </div>
+
+            <button class="reset-btn" onclick="resetDefaults()">
+                Reset to Defaults
+            </button>
+        </div>
+
+        <div>
+            <h3>Word-to-Frame Alignment</h3>
+            <p style="color: #666; font-size: 13px;">
+                Each word appears as a colored block. Width = frame span, ★ = peak frame, waveform = attention trace.
+            </p>
+            <canvas id="alignment-canvas" width="1600" height="600"></canvas>
+
+            <h3 style="margin-top: 30px;">Timeline Progress Bar</h3>
+            <canvas id="timeline-canvas" width="1600" height="100"></canvas>
+
+            <div class="legend">
+                <strong>Legend:</strong><br><br>
+                <div class="legend-item">
+                    <span class="confidence high">High</span>
+                    <span class="confidence medium">Medium</span>
+                    <span class="confidence low">Low</span>
+                    Confidence Levels (opacity reflects confidence)
+                </div>
+                <div class="legend-item">
+                    <span style="color: red; font-size: 20px;">★</span>
+                    Peak Frame (highest attention)
+                </div>
+                <div class="legend-item">
+                    <span style="color: blue;">━</span>
+                    Attention Waveform (within word region)
+                </div>
+            </div>
+        </div>
+
+        <div class="info-panel">
+            <h3>Alignment Details</h3>
+            <div id="alignment-details"></div>
+        </div>
+    </div>
+
+    <script>
+        // Attention data from Python
+        const attentionData = [{"word": "CAR", "word_idx": 0, "weights": [0.019201714545488358, 0.016010498628020287, 0.04009818658232689, 0.2021431028842926, 0.2333953082561493, 0.19581404328346252, 0.13894161581993103, 0.08961869776248932, 0.017304163426160812, 0.009614335373044014, 0.0044919578358531, 0.003000154159963131, 0.0018093630205839872, 0.002432898385450244, 0.0032934064511209726, 0.00507147703319788, 0.005474661942571402, 0.006064476445317268, 0.006219940260052681]}, {"word": "BREAK-DOWN", "word_idx": 1, "weights": [0.14899039268493652, 0.13971643149852753, 0.06643137335777283, 0.0284759271889925, 0.025709832087159157, 0.024263687431812286, 0.020394574850797653, 0.01726449280977249, 0.010557206347584724, 0.0072791315615177155, 0.00612180819734931, 0.0075492458418011665, 0.01024742890149355, 0.013071726076304913, 0.03896228224039078, 0.0870385617017746, 0.1054471880197525, 0.1188133955001831, 0.12366533279418945]}];
+        const numGlosses = 2;
+        const numFeatures = 19;
+
+        // Colors for different words (matching matplotlib tab20)
+        const colors = [
+            '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
+            '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',
+            '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',
+            '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5'
+        ];
+
+        // Get controls
+        const peakThresholdSlider = document.getElementById('peak-threshold');
+        const peakThresholdValue = document.getElementById('peak-threshold-value');
+        const confidenceHighSlider = document.getElementById('confidence-high');
+        const confidenceHighValue = document.getElementById('confidence-high-value');
+        const confidenceMediumSlider = document.getElementById('confidence-medium');
+        const confidenceMediumValue = document.getElementById('confidence-medium-value');
+        const alignmentCanvas = document.getElementById('alignment-canvas');
+        const timelineCanvas = document.getElementById('timeline-canvas');
+        const alignmentCtx = alignmentCanvas.getContext('2d');
+        const timelineCtx = timelineCanvas.getContext('2d');
+
+        // Update displays when sliders change
+        peakThresholdSlider.oninput = function() {
+            peakThresholdValue.textContent = this.value + '%';
+            updateVisualization();
+        };
+
+        confidenceHighSlider.oninput = function() {
+            confidenceHighValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        confidenceMediumSlider.oninput = function() {
+            confidenceMediumValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        function resetDefaults() {
+            peakThresholdSlider.value = 90;
+            confidenceHighSlider.value = 50;
+            confidenceMediumSlider.value = 20;
+            peakThresholdValue.textContent = '90%';
+            confidenceHighValue.textContent = '0.50';
+            confidenceMediumValue.textContent = '0.20';
+            updateVisualization();
+        }
+
+        function calculateAlignment(weights, peakThreshold) {
+            // Find peak
+            let peakIdx = 0;
+            let peakWeight = weights[0];
+            for (let i = 1; i < weights.length; i++) {
+                if (weights[i] > peakWeight) {
+                    peakWeight = weights[i];
+                    peakIdx = i;
+                }
+            }
+
+            // Find significant frames
+            const threshold = peakWeight * (peakThreshold / 100);
+            let startIdx = peakIdx;
+            let endIdx = peakIdx;
+            let sumWeight = 0;
+            let count = 0;
+
+            for (let i = 0; i < weights.length; i++) {
+                if (weights[i] >= threshold) {
+                    if (i < startIdx) startIdx = i;
+                    if (i > endIdx) endIdx = i;
+                    sumWeight += weights[i];
+                    count++;
+                }
+            }
+
+            const avgWeight = count > 0 ? sumWeight / count : peakWeight;
+
+            return {
+                startIdx: startIdx,
+                endIdx: endIdx,
+                peakIdx: peakIdx,
+                peakWeight: peakWeight,
+                avgWeight: avgWeight,
+                threshold: threshold
+            };
+        }
+
+        function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {
+            if (avgWeight > highThreshold) return 'high';
+            if (avgWeight > mediumThreshold) return 'medium';
+            return 'low';
+        }
+
+        function drawAlignmentChart() {
+            const peakThreshold = parseInt(peakThresholdSlider.value);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            // Canvas dimensions
+            const width = alignmentCanvas.width;
+            const height = alignmentCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const topMargin = 60;
+            const bottomMargin = 80;
+
+            const plotWidth = width - leftMargin - rightMargin;
+            const plotHeight = height - topMargin - bottomMargin;
+
+            const rowHeight = plotHeight / numGlosses;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            alignmentCtx.clearRect(0, 0, width, height);
+
+            // Draw title
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 18px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30);
+            alignmentCtx.font = '13px Arial';
+            alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48);
+
+            // Calculate alignments
+            const alignments = [];
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const data = attentionData[wordIdx];
+                const alignment = calculateAlignment(data.weights, peakThreshold);
+                alignment.word = data.word;
+                alignment.wordIdx = wordIdx;
+                alignment.weights = data.weights;
+                alignments.push(alignment);
+            }
+
+            // Draw grid
+            alignmentCtx.strokeStyle = '#e0e0e0';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numFeatures; i++) {
+                const x = leftMargin + i * featureWidth;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(x, topMargin);
+                alignmentCtx.lineTo(x, topMargin + plotHeight);
+                alignmentCtx.stroke();
+            }
+
+            // Draw word regions
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const y = topMargin + wordIdx * rowHeight;
+
+                // Alpha based on confidence
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                // Draw rectangle for word region
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                alignmentCtx.fillStyle = colors[wordIdx % 20];
+                alignmentCtx.globalAlpha = alpha;
+                alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8);
+                alignmentCtx.globalAlpha = 1.0;
+
+                // Draw border
+                alignmentCtx.strokeStyle = '#000';
+                alignmentCtx.lineWidth = 2;
+                alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8);
+
+                // Draw attention waveform inside rectangle
+                alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.beginPath();
+                for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {
+                    const x = leftMargin + i * featureWidth + featureWidth / 2;
+                    const weight = alignment.weights[i];
+                    const maxWeight = alignment.peakWeight;
+                    const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility
+                    const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6);
+
+                    if (i === alignment.startIdx) {
+                        alignmentCtx.moveTo(x, waveY);
+                    } else {
+                        alignmentCtx.lineTo(x, waveY);
+                    }
+                }
+                alignmentCtx.stroke();
+
+                // Draw word label
+                const labelX = startX + rectWidth / 2;
+                const labelY = y + rowHeight * 0.4;
+
+                alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)';
+                alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24);
+                alignmentCtx.fillStyle = '#fff';
+                alignmentCtx.font = 'bold 13px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, labelX, labelY);
+
+                // Mark peak frame with star
+                const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2;
+                const peakY = y + rowHeight * 0.4;
+
+                // Draw star
+                alignmentCtx.fillStyle = '#ff0000';
+                alignmentCtx.strokeStyle = '#ffff00';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.font = '20px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.strokeText('★', peakX, peakY);
+                alignmentCtx.fillText('★', peakX, peakY);
+
+                // Y-axis label (word names)
+                alignmentCtx.fillStyle = '#333';
+                alignmentCtx.font = '12px Arial';
+                alignmentCtx.textAlign = 'right';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4);
+            }
+
+            // Draw horizontal grid lines
+            alignmentCtx.strokeStyle = '#ccc';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numGlosses; i++) {
+                const y = topMargin + i * rowHeight;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(leftMargin, y);
+                alignmentCtx.lineTo(leftMargin + plotWidth, y);
+                alignmentCtx.stroke();
+            }
+
+            // Draw axes
+            alignmentCtx.strokeStyle = '#000';
+            alignmentCtx.lineWidth = 2;
+            alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight);
+
+            // X-axis labels (frame indices)
+            alignmentCtx.fillStyle = '#000';
+            alignmentCtx.font = '11px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.textBaseline = 'top';
+            for (let i = 0; i < numFeatures; i++) {
+                const x = leftMargin + i * featureWidth + featureWidth / 2;
+                alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10);
+            }
+
+            // Axis titles
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 14px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20);
+
+            alignmentCtx.save();
+            alignmentCtx.translate(30, topMargin + plotHeight / 2);
+            alignmentCtx.rotate(-Math.PI / 2);
+            alignmentCtx.fillText('Generated Word', 0, 0);
+            alignmentCtx.restore();
+
+            return alignments;
+        }
+
+        function drawTimeline(alignments) {
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            const width = timelineCanvas.width;
+            const height = timelineCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const plotWidth = width - leftMargin - rightMargin;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            timelineCtx.clearRect(0, 0, width, height);
+
+            // Background bar
+            timelineCtx.fillStyle = '#ddd';
+            timelineCtx.fillRect(leftMargin, 30, plotWidth, 40);
+            timelineCtx.strokeStyle = '#000';
+            timelineCtx.lineWidth = 2;
+            timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40);
+
+            // Draw word regions on timeline
+            for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                timelineCtx.fillStyle = colors[wordIdx % 20];
+                timelineCtx.globalAlpha = alpha;
+                timelineCtx.fillRect(startX, 30, rectWidth, 40);
+                timelineCtx.globalAlpha = 1.0;
+                timelineCtx.strokeStyle = '#000';
+                timelineCtx.lineWidth = 0.5;
+                timelineCtx.strokeRect(startX, 30, rectWidth, 40);
+            }
+
+            // Title
+            timelineCtx.fillStyle = '#333';
+            timelineCtx.font = 'bold 13px Arial';
+            timelineCtx.textAlign = 'left';
+            timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20);
+        }
+
+        function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {
+            const panel = document.getElementById('alignment-details');
+            let html = '<table style="width: 100%; border-collapse: collapse;">';
+            html += '<tr style="background: #f0f0f0; font-weight: bold;">';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>';
+            html += '</tr>';
+
+            for (const align of alignments) {
+                const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold);
+                const span = align.endIdx - align.startIdx + 1;
+
+                html += '<tr>';
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${align.word}</strong></td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.startIdx} → ${align.endIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.peakIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${span}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.avgWeight.toFixed(4)}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${confidence}">${confidence}</span></td>`;
+                html += '</tr>';
+            }
+
+            html += '</table>';
+            panel.innerHTML = html;
+        }
+
+        function updateVisualization() {
+            const alignments = drawAlignmentChart();
+            drawTimeline(alignments);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+            updateDetailsPanel(alignments, highThreshold, mediumThreshold);
+        }
+
+        // Event listeners for sliders
+        peakSlider.addEventListener('input', function() {
+            peakValue.textContent = peakSlider.value + '%';
+            updateVisualization();
+        });
+
+        confidenceHighSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceHighSlider.value) / 100;
+            confidenceHighValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        confidenceMediumSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceMediumSlider.value) / 100;
+            confidenceMediumValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        // Initial visualization
+        updateVisualization();
+    </script>
+</body>
+</html>

SignX/inference_output/detailed_prediction_20260102_202418/173745/translation.txt

@@ -0,0 +1,3 @@
+With BPE: C@@ A@@ R BREAK-DOWN
+Clean: CAR BREAK-DOWN
+Ground Truth: CAR BREAK-DOWN

SignX/inference_output/detailed_prediction_20260102_202534/23880856/23880856.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:555d737baa311faa5ff33afa7d8a6ca4c3090c41e8c47eaa39569e493dce7282
+size 87354