diff --git a/.gitignore b/.gitignore
index cfeb86f..484d5cc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,4 +17,8 @@ sa.json
 
 # Local files
 *.txt
-*.log
\ No newline at end of file
+*.log
+
+# Agents
+.gemini/*
+.superpowers/*
\ No newline at end of file
diff --git a/README.md b/README.md
index 8c62c3f..f74d2db 100644
--- a/README.md
+++ b/README.md
@@ -44,7 +44,7 @@ uv run monitor.py
 
 ## 2. Twitch Downloader (`twitch_download.py`)
 
-Download VODs from Twitch using `yt-dlp` (optimized for archiving).
+Download VODs from Twitch using `yt-dlp` and `chat_utils`.
 
 ### Usage
 ```bash
@@ -52,22 +52,37 @@ uv run twitch_download.py "TWITCH_VOD_URL"
 ```
 
 ### Features
-- **Optimized Video**: Downloads `480p` (or best ≤ 480p) to `original.mp4` (small size for processing).
-- **Direct Audio**: Downloads `Audio Only` stream directly to `audio.mp4` (no re-encoding if possible).
-- **High Speed**: Uses 10 concurrent threads for downloading.
+- **Optimized Video**: Downloads `480p` (or best ≤ 480p) to `original.mp4`.
+- **Chat Download**: Automatically fetches full Twitch chat logs using the GQL API (bypasses 404 errors).
+- **Direct Audio**: Extracts audio directly to `audio.mp4`.
 - **Auto-Pipeline**:
-    1. Downloads Video & Audio.
+    1. Downloads Video, Audio, and Chat.
     2. Transcribes Audio (generating `transcript.srt`).
-    3. Splits SRT by hour.
-    4. Triggers N8N workflow (`analyze`).
+    3. Triggers N8N workflow (`analyze`).
 
-### Options
-- `--root_dir`: Base directory.
-- `--audio` / `--no-audio`: Toggle audio download.
+---
+
+## 3. Chat Downloader (`chat_utils.py`)
+
+A robust Twitch chat downloader using the modern GQL API.
+
+### Usage
+```bash
+# Download entire chat
+uv run python chat_utils.py "TWITCH_VOD_URL" chat.json
+
+# Download specific range (e.g., first hour)
+uv run python chat_utils.py "TWITCH_VOD_URL" chat.json 0 60
+```
+
+### Features
+- **GQL Powered**: Uses Twitch's internal GraphQL API for high reliability.
+- **Range Support**: Can jump to any timestamp using `--start_min` and `--duration_min`.
+- **Format Compatible**: Produces a structured JSON chat log.
 
 ---
 
-## 3. YouTube Downloader (`yt_download.py`)
+## 4. YouTube Downloader (`yt_download.py`)
 
 Download videos from YouTube.
 
@@ -84,7 +99,7 @@ uv run yt_download.py "YOUTUBE_URL"
 
 ---
 
-## 4. Transcription Tool (`transcript.py`)
+## 5. Transcription Tool (`transcript.py`)
 
 Generate SRT subtitles using **AssemblyAI** (default) or **faster-whisper**, with translation support using **Google Translate** (default) or **Ollama**.
 
@@ -110,7 +125,7 @@ uv run transcript.py /path/to/video.mp4 --zh_output "zh.srt"
 
 ---
 
-## 5. Generic Translator (`translate.py`)
+## 6. Generic Translator (`translate.py`)
 
 Simple CLI tool to translate text/files using Google Translate.
 
@@ -125,7 +140,7 @@ uv run translate.py path/to/file.txt
 
 ---
 
-## 6. Batch Clipper (`crop.py`)
+## 7. Batch Clipper (`crop.py`)
 
 Process a long video into multiple clips based on a list.
 
@@ -142,7 +157,7 @@ uv run crop.py /path/to/RootFolder
 
 ---
 
-## 7. Interactive Crop UI (`main.py`)
+## 8. Interactive Crop UI (`main.py`)
 
 Visual tool to determine FFmpeg crop parameters.
 
diff --git a/chat_utils.py b/chat_utils.py
new file mode 100644
index 0000000..f21ffaa
--- /dev/null
+++ b/chat_utils.py
@@ -0,0 +1,146 @@
+import requests
+import json
+import os
+import time
+import sys
+
+def download_chat(video_url_or_id, output_path, start_min=None, duration_min=None):
+    """
+    Downloads Twitch chat logs using the GQL API (same approach as lay295/TwitchDownloader).
+    Handles pagination and maps output to a format compatible with analyzer.py.
+    """
+    # Extract video ID
+    video_id = video_url_or_id
+    if "/" in video_url_or_id:
+        video_id = video_url_or_id.rstrip("/").split("/")[-1]
+    
+    # Check if video_id is just numbers
+    if not video_id.isdigit():
+        print(f"Error: Could not extract a valid video ID from {video_url_or_id}")
+        return False
+
+    url = "https://gql.twitch.tv/gql"
+    client_id = "kd1unb4b3q4t58fwlpcbzcbnm76a8fp"
+    sha256_hash = "b70a3591ff0f4e0313d126c6a1502d79a1c02baebb288227c582044aa76adf6a"
+    
+    headers = {
+        "Client-Id": client_id,
+        "Content-Type": "application/json"
+    }
+
+    start_seconds = (start_min * 60) if start_min is not None else 0
+    end_seconds = (start_seconds + (duration_min * 60)) if duration_min is not None else float('inf')
+
+    all_comments = []
+    cursor = None
+    
+    print(f"[*] Starting chat download for video {video_id}...")
+    if start_min is not None:
+        print(f"[*] Filtering for range: {start_min}m to {start_min + (duration_min or 0)}m")
+
+    while True:
+        variables = {
+            "videoID": video_id
+        }
+        
+        if cursor:
+            variables["cursor"] = cursor
+        else:
+            variables["contentOffsetSeconds"] = start_seconds
+
+        payload = [{
+            "operationName": "VideoCommentsByOffsetOrCursor",
+            "variables": variables,
+            "extensions": {
+                "persistedQuery": {
+                    "version": 1,
+                    "sha256Hash": sha256_hash
+                }
+            }
+        }]
+
+        try:
+            response = requests.post(url, json=payload, headers=headers, timeout=10)
+            response.raise_for_status()
+            data = response.json()
+            if isinstance(data, list):
+                data = data[0]
+            
+            if "errors" in data:
+                print(f"[!] GQL Error: {data['errors']}")
+                break
+                
+            video_data = data.get("data", {}).get("video", {})
+            if not video_data:
+                print("[!] No video data found in response.")
+                break
+                
+            comments_data = video_data.get("comments", {})
+            edges = comments_data.get("edges", [])
+            
+            if not edges:
+                break
+                
+            last_offset = 0
+            for edge in edges:
+                node = edge.get("node", {})
+                offset = node.get("contentOffsetSeconds", 0)
+                last_offset = offset
+                
+                # Check if we passed the end_seconds (if specified)
+                if offset > end_seconds:
+                    break
+                
+                if offset >= start_seconds:
+                    # Map to format expected by analyzer.py (rechat-like)
+                    all_comments.append({
+                        "content_offset_seconds": offset,
+                        "message": node.get("message", {}),
+                        "commenter": node.get("commenter", {})
+                    })
+
+            # Check if we broke early due to end_seconds
+            if last_offset > end_seconds:
+                break
+                
+            # Get cursor for next page
+            page_info = comments_data.get("pageInfo", {})
+            if page_info.get("hasNextPage"):
+                cursor = edges[-1].get("cursor")
+                # Simple progress report
+                print(f"[*] Collected {len(all_comments)} comments... (Current time: {int(last_offset//60)}m)", end='\r')
+            else:
+                break
+                
+            # Rate limiting / polite pause
+            time.sleep(0.1)
+
+        except Exception as e:
+            print(f"\n[!] Error during download: {e}")
+            # Could implement retry here
+            break
+
+    print(f"\n[*] Download complete. Total comments: {len(all_comments)}")
+    
+    if not all_comments:
+        print("[!] No comments found for the specified range.")
+        return False
+
+    try:
+        with open(output_path, 'w', encoding='utf-8') as f:
+            json.dump(all_comments, f, ensure_ascii=False, indent=2)
+        print(f"[*] Chat log saved to {output_path}")
+        return True
+    except Exception as e:
+        print(f"[!] Error saving chat file: {e}")
+        return False
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print("Usage: python chat_utils.py <video_url_or_id> <output_path> [start_min] [duration_min]")
+    else:
+        v_id = sys.argv[1]
+        out = sys.argv[2]
+        s_min = int(sys.argv[3]) if len(sys.argv) > 3 else None
+        d_min = int(sys.argv[4]) if len(sys.argv) > 4 else None
+        download_chat(v_id, out, s_min, d_min)
diff --git a/crop.py b/crop.py
index c016ff2..f766afd 100644
--- a/crop.py
+++ b/crop.py
@@ -1,10 +1,9 @@
 import os
 import re
 import argparse
-
-
 from transcript import transcribe_video 
 import ffmpeg
+from facecam_utils import detect_facecam
 
 # ================= 配置區域 =================
 INPUT_FILE_NAME = "original.mp4"
@@ -190,22 +189,40 @@ def process(root_dir, crop_cam, crop_screen, start_arg=None, end_arg=None):
         with open(os.path.join(output_folder, "metadata.md"), "w", encoding="utf-8") as f:
             f.write(final_metadata)
 
+def resolve_cam_param(root_dir, cam_arg):
+    if cam_arg != "auto":
+        return cam_arg
+        
+    input_video_path = os.path.join(root_dir, INPUT_FILE_NAME)
+    if not os.path.exists(input_video_path):
+        print(f"Warning: {input_video_path} not found. Using default: {DEFAULT_CROP_CAM}")
+        return DEFAULT_CROP_CAM
+
+    print("Auto-detecting facecam (ML)...")
+    detected = detect_facecam(input_video_path)
+    if detected:
+        print(f"Detected: {detected}")
+        return detected
+    
+    print(f"Detection failed. Using default: {DEFAULT_CROP_CAM}")
+    return DEFAULT_CROP_CAM
+
 def main():
     parser = argparse.ArgumentParser(description="自動剪輯工具")
     parser.add_argument("root_dir", help="包含 crop_info.md 和 original.mp4 的根目錄路徑")
-    parser.add_argument("--cam", default=DEFAULT_CROP_CAM, help=f"Camera crop parameter (default: {DEFAULT_CROP_CAM})")
+    parser.add_argument("--cam", default=DEFAULT_CROP_CAM, help=f"Camera crop parameter (default: {DEFAULT_CROP_CAM}). Use 'auto' for ML detection.")
     parser.add_argument("--screen", default=DEFAULT_CROP_SCREEN, help=f"Screen crop parameter (default: {DEFAULT_CROP_SCREEN})")
     parser.add_argument("--start", help="Start time (e.g. 00:00:10). usage with --end")
     parser.add_argument("--end", help="End time (e.g. 00:00:20). usage with --start")
     
     args = parser.parse_args()
 
-    # Validation
     if (args.start and not args.end) or (args.end and not args.start):
         print("錯誤: --start 和 --end 必須同時提供")
         return
 
-    process(args.root_dir, args.cam, args.screen, start_arg=args.start, end_arg=args.end)
+    cam_param = resolve_cam_param(args.root_dir, args.cam)
+    process(args.root_dir, cam_param, args.screen, start_arg=args.start, end_arg=args.end)
 
 if __name__ == "__main__":
     main()
\ No newline at end of file
diff --git a/crop_info.csv b/crop_info.csv
new file mode 100644
index 0000000..d7c5cd5
--- /dev/null
+++ b/crop_info.csv
@@ -0,0 +1,2 @@
+Shorts Number,Start Timestamp,End Timestamp,Selling Point,Suggested Title/Hook (Including Hashtags),SEO Subtitle
+9,02:14:27,02:16:40,"Reaction to a woman complaining about a date who 'psychoanalyzed' her and correctly guessed she was on anxiety meds. Streamer sides with the guy.","Her Date Had Her ALL Figured Out! 😂 #dating #anxiety #funny #reaction","Streamer reacts to woman complaining about a date who psychoanalyzed her"
\ No newline at end of file
diff --git a/facecam_utils.py b/facecam_utils.py
new file mode 100644
index 0000000..b132b55
--- /dev/null
+++ b/facecam_utils.py
@@ -0,0 +1,118 @@
+from ultralytics import YOLO
+import cv2
+import collections
+import os
+from typing import Optional, List, Tuple, Dict
+
+def _get_person_regions(video_path: str, sample_rate: int, max_frames: int) -> Tuple[List[Tuple[int, int, int, int]], int, int]:
+    """Extracts person bounding boxes from the video using YOLO."""
+    model = YOLO('yolov8n.pt')
+    cap = cv2.VideoCapture(video_path)
+    
+    if not cap.isOpened():
+        raise IOError(f"Could not open video {video_path}")
+
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    person_regions = []
+    frame_count = 0
+    
+    while cap.isOpened() and frame_count < max_frames:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        if frame_count % sample_rate == 0:
+            results = model(frame, verbose=False, classes=[0]) # class 0 is person
+            for result in results:
+                for box in result.boxes:
+                    x1, y1, x2, y2 = box.xyxy[0].tolist()
+                    person_regions.append((int(x1), int(y1), int(x2 - x1), int(y2 - y1)))
+        
+        frame_count += 1
+            
+    cap.release()
+    return person_regions, width, height
+
+def _cluster_regions(regions: List[Tuple[int, int, int, int]], width: int, height: int) -> Optional[Dict]:
+    """Groups detected regions into clusters to find the most stable one (the facecam)."""
+    if not regions:
+        return None
+
+    clusters = []
+    for region in regions:
+        x, y, w, h = region
+        cx, cy = x + w/2, y + h/2
+        
+        found_cluster = False
+        for cluster in clusters:
+            last_x, last_y, last_w, last_h = cluster['last']
+            last_cx, last_cy = last_x + last_w/2, last_y + last_h/2
+            
+            # If center is within 10% of width/height
+            if abs(cx - last_cx) < width * 0.10 and abs(cy - last_cy) < height * 0.10:
+                cluster['count'] += 1
+                cluster['sum_x'] += x
+                cluster['sum_y'] += y
+                cluster['sum_w'] += w
+                cluster['sum_h'] += h
+                cluster['last'] = region
+                found_cluster = True
+                break
+        
+        if not found_cluster:
+            clusters.append({
+                'count': 1,
+                'sum_x': x, 'sum_y': y, 'sum_w': w, 'sum_h': h,
+                'last': region
+            })
+            
+    if not clusters:
+        return None
+        
+    clusters.sort(key=lambda x: x['count'], reverse=True)
+    return clusters[0]
+
+def _calculate_final_crop(best_cluster: Dict, width: int, height: int) -> str:
+    """Calculates the final FFmpeg crop string with padding and edge-snapping."""
+    count = best_cluster['count']
+    avg_x = int(best_cluster['sum_x'] / count)
+    avg_y = int(best_cluster['sum_y'] / count)
+    avg_w = int(best_cluster['sum_w'] / count)
+    avg_h = int(best_cluster['sum_h'] / count)
+    
+    # Expand the bounding box to capture the full facecam area
+    padding_x = int(avg_w * 0.2)
+    padding_y = int(avg_h * 0.2)
+    
+    final_x = max(0, avg_x - padding_x)
+    final_y = max(0, avg_y - padding_y)
+    final_w = min(width - final_x, avg_w + 2 * padding_x)
+    final_h = min(height - final_y, avg_h + 2 * padding_y)
+    
+    # Snap to edges if very close
+    if final_x < width * 0.05: final_x = 0
+    if final_y < height * 0.05: final_y = 0
+    if final_x + final_w > width * 0.95: final_w = width - final_x
+    if final_y + final_h > height * 0.95: final_h = height - final_y
+
+    return f"{final_w}:{final_h}:{final_x}:{final_y}"
+
+def detect_facecam(video_path: str, sample_rate: int = 10, max_frames: int = 300) -> Optional[str]:
+    """
+    Detects the facecam region in a video using YOLOv8 person detection.
+    
+    Returns a string in FFMPEG crop format "w:h:x:y" or None if not found.
+    """
+    try:
+        regions, width, height = _get_person_regions(video_path, sample_rate, max_frames)
+        best_cluster = _cluster_regions(regions, width, height)
+        
+        if best_cluster:
+            return _calculate_final_crop(best_cluster, width, height)
+            
+    except Exception as e:
+        print(f"Error during facecam detection: {e}")
+        
+    return None
diff --git a/ffmpeg.py b/ffmpeg.py
index fcf1b0a..d6044b2 100644
--- a/ffmpeg.py
+++ b/ffmpeg.py
@@ -49,24 +49,22 @@ def crop(input_video_path, start, end, output_folder=None, crop_cam="260:180:0:2
     path_raw = os.path.join(output_folder, "raw.mp4")
     path_audio = os.path.join(output_folder, "audio.wav")
 
-    ffmpeg_cmd = [
-        "ffmpeg", "-y", "-ss", start, "-to", end, "-i", input_video_path,
-        '-map_metadata', '0',
-        '-avoid_negative_ts', 'make_zero',
-        '-movflags', '+faststart',
-        "-filter_complex", 
-        # 1. Crop & Scale & Split
+    filter_complex = (
         f"[0:v]crop={crop_cam},scale=1080:640,split=2[cam_base][cam_stack]; "
         f"[0:v]crop={crop_screen},scale=1080:1280,split=2[screen_base][screen_stack]; "
-        
-        # 2. Stack
         f"[cam_stack][screen_stack]vstack=inputs=2[stacked_base]; "
-        
-        # 3. Apply Watermark
         f"[stacked_base]{STACKED_WATERMARK_FILTER}[stacked_out]; "
         f"[cam_base]{WATERMARK_FILTER}[cam_out]; "
         f"[screen_base]{WATERMARK_FILTER}[screen_out]; "
-        f"[0:v]{WATERMARK_FILTER}[raw_out]",
+        f"[0:v]{WATERMARK_FILTER}[raw_out]"
+    )
+
+    ffmpeg_cmd = [
+        "ffmpeg", "-y", "-ss", start, "-to", end, "-i", input_video_path,
+        "-map_metadata", "0",
+        "-avoid_negative_ts", "make_zero",
+        "-movflags", "+faststart",
+        "-filter_complex", filter_complex,
         
         # Output 1: Stacked
         "-map", "[stacked_out]", "-map", "0:a",
@@ -89,9 +87,7 @@ def crop(input_video_path, start, end, output_folder=None, crop_cam="260:180:0:2
         path_raw,
         
         # Output 5: Audio
-        "-map", "0:a",
-        "-vn",
-        path_audio
+        "-map", "0:a", "-vn", path_audio
     ]
     
     # print(f"Executing FFmpeg...")
diff --git a/main.py b/main.py
index 2cab2ed..4d58459 100644
--- a/main.py
+++ b/main.py
@@ -1,10 +1,11 @@
 import tkinter as tk
-from tkinter import filedialog, ttk
+from tkinter import filedialog, ttk, messagebox
 import cv2
 from PIL import Image, ImageTk
 import argparse
 import sys
 import os
+from facecam_utils import detect_facecam
 
 class FFmpegCropTool:
     def __init__(self, root, video_path=None, frame_num=1):
@@ -50,7 +51,9 @@ def setup_ui(self):
 
         ttk.Button(control_frame, text="Go", command=self.reload_frame).pack(side=tk.LEFT)
         
-        ttk.Button(control_frame, text="Reset Crops", command=self.reset_crops).pack(side=tk.LEFT, padx=20)
+        ttk.Button(control_frame, text="Auto Detect Facecam", command=self.auto_detect_facecam).pack(side=tk.LEFT, padx=10)
+        
+        ttk.Button(control_frame, text="Reset Crops", command=self.reset_crops).pack(side=tk.LEFT, padx=10)
         
         self.coord_label = ttk.Label(control_frame, text="Mouse: (0, 0)")
         self.coord_label.pack(side=tk.RIGHT, padx=10)
@@ -346,6 +349,45 @@ def reset_crops(self):
             widget.destroy()
         self.redraw_rectangles()
 
+    def auto_detect_facecam(self):
+        if not self.video_path:
+            messagebox.showwarning("Warning", "Please open a video first")
+            return
+            
+        self.root.config(cursor="watch")
+        self.root.update()
+        
+        # Add a temporary status label if it doesn't exist
+        status_label = ttk.Label(self.root, text="Auto-detecting facecam, please wait...")
+        status_label.pack(side=tk.BOTTOM, fill=tk.X)
+        self.root.update()
+        
+        try:
+            # We can use a smaller max_frames for faster detection in GUI
+            crop_str = detect_facecam(self.video_path, max_frames=150)
+            if crop_str:
+                w, h, x, y = map(int, crop_str.split(':'))
+                # Check if this crop already exists
+                exists = False
+                for (rx, ry, rw, rh) in self.rectangles:
+                    if rx == x and ry == y and rw == w and rh == h:
+                        exists = True
+                        break
+                
+                if not exists:
+                    self.rectangles.append((x, y, w, h))
+                    self.redraw_rectangles()
+                    self.update_output()
+                
+                messagebox.showinfo("Success", f"Detected facecam: {crop_str}")
+            else:
+                messagebox.showwarning("Failed", "Could not detect facecam automatically")
+        except Exception as e:
+            messagebox.showerror("Error", f"An error occurred during detection: {e}")
+        finally:
+            status_label.destroy()
+            self.root.config(cursor="")
+
 def main():
     parser = argparse.ArgumentParser(description="FFmpeg Crop Tool")
     parser.add_argument("--video_path", type=str, help="Path to video file")
diff --git a/pyproject.toml b/pyproject.toml
index a9a0e57..a52badb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,6 +7,7 @@ requires-python = ">=3.12"
 dependencies = [
     "argostranslate>=1.10.0",
     "assemblyai>=0.48.4",
+    "chat-downloader>=0.2.8",
     "deep-translator>=1.11.4",
     "faster-whisper>=1.2.1",
     "flask>=3.1.2",
@@ -14,6 +15,7 @@ dependencies = [
     "google-genai>=1.56.0",
     "ipython>=9.8.0",
     "mlx-whisper>=0.4.3",
+    "numpy>=2.0.2",
     "opencc>=1.1.9",
     "opencc-python-reimplemented>=0.1.7",
     "opencv-python>=4.12.0.88",
@@ -23,5 +25,7 @@ dependencies = [
     "requests>=2.32.5",
     "srt>=3.5.3",
     "streamlink>=8.1.0",
+    "tcd>=3.2.1",
+    "ultralytics>=8.4.24",
     "yt-dlp>=2025.12.8",
 ]
diff --git a/test_analyzer.py b/test_analyzer.py
new file mode 100644
index 0000000..b8e282f
--- /dev/null
+++ b/test_analyzer.py
@@ -0,0 +1,122 @@
+import unittest
+import numpy as np
+import os
+import subprocess
+import json
+from analyzer import extract_audio_peaks, analyze_chat_velocity
+
+class TestAudioAnalyzer(unittest.TestCase):
+    def setUp(self):
+        # Generate a 10-second test audio file with a peak between 5s and 6s
+        # We use a low volume sine wave and a high volume section
+        self.test_file = "test_audio_sample.wav"
+        if not os.path.exists(self.test_file):
+            cmd = [
+                "ffmpeg", "-y",
+                "-f", "lavfi",
+                "-i", "sine=f=440:d=10,volume='if(between(t,5,6), 1.0, 0.1)':eval=frame",
+                self.test_file
+            ]
+            subprocess.run(cmd, check=True, capture_output=True)
+
+    def tearDown(self):
+        if os.path.exists(self.test_file):
+            os.remove(self.test_file)
+
+    def test_extract_audio_peaks(self):
+        # We expect a peak around 5s or 6s
+        peaks = extract_audio_peaks(self.test_file, threshold_factor=2.0, window_size=1.0)
+        
+        # Verify peaks is a list
+        self.assertIsInstance(peaks, list)
+        
+        # We expect at least one peak in the 5-6s range
+        # Depending on how the window and rolling average are calculated, it should be around there
+        found_in_range = any(5.0 <= p[0] <= 7.0 for p in peaks)
+        self.assertTrue(found_in_range, f"Expected peak between 5s and 7s, found: {peaks}")
+        
+        # Verify no peaks in the quiet part (0-4s)
+        found_in_quiet = any(0.0 <= p[0] <= 4.0 for p in peaks)
+        self.assertFalse(found_in_quiet, f"Found unexpected peaks in quiet section: {peaks}")
+
+class TestChatAnalyzer(unittest.TestCase):
+    def setUp(self):
+        self.test_json = "test_rechat.json"
+        
+        # Mock rechat data
+        # Quiet period: 0-20s (1 message every 5s)
+        # Spike: 25-35s (10 messages in 10s, some with emotes)
+        # Quiet period: 40-60s (1 message every 5s)
+        data = []
+        for t in range(0, 21, 5):
+            data.append({"content_offset_seconds": float(t), "message": {"body": "hello"}})
+            
+        # Spike at 30s
+        for t in range(25, 36):
+            msg = "Pog" if t % 2 == 0 else "wow"
+            data.append({"content_offset_seconds": float(t), "message": {"body": msg}})
+            
+        for t in range(40, 61, 5):
+            data.append({"content_offset_seconds": float(t), "message": {"body": "hello"}})
+            
+        with open(self.test_json, "w") as f:
+            json.dump(data, f)
+
+    def tearDown(self):
+        if os.path.exists(self.test_json):
+            os.remove(self.test_json)
+
+    def test_analyze_chat_velocity(self):
+        # window_size=10, threshold_factor=1.5
+        spikes = analyze_chat_velocity(self.test_json, window_size=10, threshold_factor=1.5)
+        
+        # Verify spikes is a list
+        self.assertIsInstance(spikes, list)
+        
+        # We expect a spike around 30s
+        # 25-35s has 11 messages. Weighted (5 * 2.0 + 6 * 1.0) = 16.0
+        # Quiet periods have 2-3 messages per 10s window. Weighted = 2.0-3.0
+        # Baseline should be around 2.0-3.0. 16.0 is way above 1.5 * baseline.
+        found_spike = any(25.0 <= s[0] <= 35.0 for s in spikes)
+        self.assertTrue(found_spike, f"Expected spike around 30s, found: {spikes}")
+        
+        # Verify no spikes in quiet periods
+        found_in_quiet = any(0.0 <= s[0] <= 20.0 or 45.0 <= s[0] <= 60.0 for s in spikes)
+        self.assertFalse(found_in_quiet, f"Found unexpected spikes in quiet section: {spikes}")
+
+class TestSignalFusion(unittest.TestCase):
+    def test_find_segments_basic(self):
+        from analyzer import find_segments
+        # Mock peaks and spikes with scores
+        audio_peaks = [(10.0, 2.5), (100.0, 3.0)] # (timestamp, score)
+        chat_spikes = [(12.0, 2.0), (200.0, 4.0)]
+        
+        # 10.0 (audio) and 12.0 (chat) are within 15s -> one event
+        # 100.0 (audio) is alone -> one event
+        # 200.0 (chat) is alone -> one event
+        
+        segments = find_segments(audio_peaks, chat_spikes, video_duration=3600)
+        
+        # Should have 3 segments
+        self.assertEqual(len(segments), 3)
+        
+        # First segment: start 5s before first spike (10.0 - 5 = 5.0), 
+        # end 10s after last signal (12.0 + 10 = 22.0)
+        # Score: 2.5 + 2.0 = 4.5
+        self.assertEqual(segments[0]['start'], "00:00:05")
+        self.assertEqual(segments[0]['end'], "00:00:22")
+        self.assertAlmostEqual(segments[0]['score'], 4.5)
+        self.assertIn("Audio + Chat", segments[0]['reason'])
+
+    def test_find_segments_ranking(self):
+        from analyzer import find_segments
+        # Create many spikes in the first hour
+        audio_peaks = [(float(i * 60), 2.0) for i in range(20)]
+        chat_spikes = []
+        
+        # Should return top 10 for the first hour
+        segments = find_segments(audio_peaks, chat_spikes, video_duration=3600)
+        self.assertEqual(len(segments), 10)
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test_e2e_mocked.py b/test_e2e_mocked.py
new file mode 100644
index 0000000..38048d8
--- /dev/null
+++ b/test_e2e_mocked.py
@@ -0,0 +1,73 @@
+import unittest
+import os
+import json
+import shutil
+import subprocess
+import sys
+from twitch_download import main as twitch_main
+
+class TestE2EMocked(unittest.TestCase):
+    def setUp(self):
+        self.root_dir = "test_e2e_output"
+        if not os.path.exists(self.root_dir):
+            os.makedirs(self.root_dir)
+
+    def tearDown(self):
+        if os.path.exists(self.root_dir):
+            shutil.rmtree(self.root_dir)
+
+    def test_full_flow_with_preexisting_video(self):
+        # We'll mock the parts that require internet
+        import unittest.mock as mock
+        
+        with mock.patch('yt_dlp.YoutubeDL') as mock_ydl:
+            # Mock metadata
+            instance = mock_ydl.return_value.__enter__.return_value
+            instance.extract_info.return_value = {
+                'uploader': 'testuser',
+                'upload_date': '20230101',
+                'timestamp': 1672531200,
+                'title': 'Test VOD',
+                'description': 'Test Description',
+                'duration': 60
+            }
+            
+            date_str = "2023-01-01T08_00_00" 
+            video_dir = os.path.join(self.root_dir, f"Twitch_VOD_testuser_{date_str}")
+            os.makedirs(video_dir, exist_ok=True)
+            
+            # Create dummy original.mp4
+            video_path = os.path.join(video_dir, "original.mp4")
+            cmd = [
+                "ffmpeg", "-y",
+                "-f", "lavfi", "-i", "testsrc=duration=5:size=160x120:rate=1",
+                "-f", "lavfi", "-i", "sine=f=440:d=5",
+                video_path
+            ]
+            subprocess.run(cmd, check=True, capture_output=True)
+            
+            # Create dummy rechat.json
+            chat_path = os.path.join(video_dir, "original.mp4.rechat.json")
+            rechat_data = [
+                {"content_offset_seconds": 1.0, "message": {"body": "Pog"}},
+                {"content_offset_seconds": 2.0, "message": {"body": "KEKW"}},
+                {"content_offset_seconds": 3.0, "message": {"body": "LUL"}}
+            ]
+            with open(chat_path, "w") as f:
+                json.dump(rechat_data, f)
+            
+            with mock.patch('n8n.trigger') as mock_n8n, \
+                 mock.patch('transcript.transcribe_video') as mock_transcribe, \
+                 mock.patch('transcript.split_srt_by_hour') as mock_split, \
+                 mock.patch('chat_utils.download_chat') as mock_chat:
+                
+                # Mock sys.argv to simulate command line call
+                with mock.patch.object(sys, 'argv', ["twitch_download.py", "https://www.twitch.tv/videos/123456789", "--root_dir", self.root_dir]):
+                    twitch_main()
+                
+                # Check if segments.json was created
+                segments_path = os.path.join(video_dir, "segments.json")
+                self.assertTrue(os.path.exists(segments_path), "segments.json should be created by the integrated analyzer")
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test_integration.py b/test_integration.py
new file mode 100644
index 0000000..8d4a8ac
--- /dev/null
+++ b/test_integration.py
@@ -0,0 +1,53 @@
+import unittest
+import os
+import json
+import shutil
+import subprocess
+from analyzer import analyze_video # This will fail if not defined yet
+
+class TestIntegration(unittest.TestCase):
+    def setUp(self):
+        self.test_dir = "test_integration_output"
+        if not os.path.exists(self.test_dir):
+            os.makedirs(self.test_dir)
+        
+        self.video_path = os.path.join(self.test_dir, "original.mp4")
+        self.chat_path = os.path.join(self.test_dir, "original.mp4.rechat.json")
+        
+        # Create a dummy video file using ffmpeg (1 second)
+        cmd = [
+            "ffmpeg", "-y",
+            "-f", "lavfi", "-i", "testsrc=duration=1:size=160x120:rate=1",
+            "-f", "lavfi", "-i", "sine=f=440:d=1",
+            self.video_path
+        ]
+        subprocess.run(cmd, check=True, capture_output=True)
+        
+        # Create a dummy rechat file
+        rechat_data = [
+            {"content_offset_seconds": 0.5, "message": {"body": "Pog"}}
+        ]
+        with open(self.chat_path, "w") as f:
+            json.dump(rechat_data, f)
+
+    def tearDown(self):
+        if os.path.exists(self.test_dir):
+            shutil.rmtree(self.test_dir)
+
+    def test_analyze_video_integration(self):
+        # This function should be called by twitch_download.py
+        # Here we test it directly first
+        analyze_video(self.video_path, self.chat_path)
+        
+        segments_path = os.path.join(self.test_dir, "segments.json")
+        self.assertTrue(os.path.exists(segments_path), "segments.json should be created")
+        
+        with open(segments_path, "r") as f:
+            segments = json.load(f)
+            self.assertIsInstance(segments, list)
+            # Since it's only 1 second, it might not find any "peaks" if the rolling average logic
+            # requires more data, but we can check if it at least runs without error
+            # and produces a valid json list.
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test_twitch_download.py b/test_twitch_download.py
new file mode 100644
index 0000000..dda42fc
--- /dev/null
+++ b/test_twitch_download.py
@@ -0,0 +1,56 @@
+import unittest
+from unittest.mock import patch, MagicMock
+import os
+import twitch_download
+
+class TestTwitchDownload(unittest.TestCase):
+    @patch('chat_utils.download_chat')
+    @patch('analyzer.analyze_video')
+    @patch('yt_dlp.YoutubeDL')
+    @patch('subprocess.run')
+    @patch('transcript.transcribe_video')
+    @patch('n8n.trigger')
+    def test_download_video_calls_chat_utils(self, mock_n8n, mock_transcribe, mock_subprocess, mock_yt_dlp, mock_analyze, mock_download_chat):
+        # Setup
+        url = "https://www.twitch.tv/videos/12345678"
+        mock_instance = mock_yt_dlp.return_value.__enter__.return_value
+        
+        # Mock extract_info to return some metadata
+        mock_instance.extract_info.return_value = {
+            'uploader': 'test_user',
+            'upload_date': '20230101',
+            'timestamp': 1672531200,
+            'title': 'Test VOD',
+            'description': 'Test Description',
+            'duration': 3600
+        }
+        
+        # We need a side effect for exists to handle the multiple checks
+        # 1. output_dir (video_dir)
+        # 2. output_original (original.mp4)
+        # 3. rechat_path
+        # 4. output_audio
+        # 5. srt_output
+        def exists_side_effect(path):
+            if "original.mp4.rechat.json" in path: return False # Force chat download
+            if "original.mp4" in path: return True # Pretend video exists after "download"
+            if "metadata.md" in path: return True
+            return False
+            
+        # Run
+        with patch('os.path.exists', side_effect=exists_side_effect):
+            with patch('os.makedirs'):
+                with patch('builtins.open', unittest.mock.mock_open()):
+                    try:
+                        twitch_download.download_video(url, root_dir="test_root", audio=False)
+                    except SystemExit:
+                        pass 
+        
+        # Verify
+        mock_download_chat.assert_called_once()
+        args, kwargs = mock_download_chat.call_args
+        self.assertEqual(args[0], url)
+        self.assertTrue(args[1].endswith("original.mp4.rechat.json"))
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/twitch_download.py b/twitch_download.py
index bb7ea29..e9ce8c6 100644
--- a/twitch_download.py
+++ b/twitch_download.py
@@ -9,6 +9,7 @@
 import pathlib
 import transcript
 import n8n
+import chat_utils
 import yt_dlp
 import datetime
 
@@ -221,7 +222,7 @@ def download_video(url, root_dir=".", audio=True, start_min=None, duration_min=N
             'concurrent_fragment_downloads': 16, 
             'external_downloader': {'m3u8': 'native'},
             'retries': 10,
-            ' fragment_retries': 10,
+            'fragment_retries': 10,
             'ignoreerrors': True,
             'enable_file_urls': True,
         }
@@ -246,7 +247,13 @@ def download_video(url, root_dir=".", audio=True, start_min=None, duration_min=N
             print("Error: Video download finished but output file missing.")
             sys.exit(1)
 
-    # 2.2 Audio Download (Direct Audio_Only)
+    # 2.2 Chat Download (via chat_utils)
+    rechat_path = os.path.join(output_dir, "original.mp4.rechat.json")
+    if not os.path.exists(rechat_path):
+        print(f"Downloading chat for: {url}...")
+        chat_utils.download_chat(url, rechat_path, start_min, duration_min)
+
+    # 2.3 Audio Download (Direct Audio_Only)
     if audio and not os.path.exists(output_audio):
         # Extract Audio from Video using ffmpeg
         ffmpeg_cmd = [
@@ -283,23 +290,62 @@ def download_video(url, root_dir=".", audio=True, start_min=None, duration_min=N
         print("Metadata file exists.")
 
     print(f"Output saved in: {output_dir}")
+    return output_dir, trigger_folder
+
+def main():
+    parser = argparse.ArgumentParser(description="Twitch Downloader (via yt-dlp)")
+    parser.add_argument("url", help="Twitch VOD URL")
+    parser.add_argument("--root_dir", default=".", help="Root directory to create video folder in (default: current directory)")
+    parser.add_argument("--audio", action='store_true', default=True, help="Extract audio (default: True)")
+    parser.add_argument("--start_min", type=int, default=None, help="Start time in minutes for slicing")
+    parser.add_argument("--duration_min", type=int, default=None, help="Duration in minutes for slicing")
+    args = parser.parse_args()
+
+    output_dir, trigger_folder = download_video(
+        args.url, 
+        root_dir=args.root_dir, 
+        audio=args.audio, 
+        start_min=args.start_min, 
+        duration_min=args.duration_min
+    )
+
+    # Post-download logic moved out of download_video
+    output_audio = os.path.join(output_dir, "audio.mp4")
+    srt_output = os.path.join(output_dir, "transcript.srt")
+    output_original = os.path.join(output_dir, "original.mp4")
 
     # 4. Auto-Transcribe
     if not os.path.exists(srt_output):
-        print("Starting transcription...")
-        try:
-            transcript.transcribe_video(
-                input_file=output_audio,
-                output_file=srt_output,
-            )
-        except Exception as e:
-            print(f"Transcription failed: {str(e)}")
-
-        transcript.split_srt_by_hour(srt_output)
+        if os.path.exists(output_audio):
+            print("Starting transcription...")
+            try:
+                transcript.transcribe_video(
+                    input_file=output_audio,
+                    output_file=srt_output,
+                )
+                transcript.split_srt_by_hour(srt_output)
+            except Exception as e:
+                print(f"Transcription failed: {str(e)}")
+        else:
+            print("Audio file missing, skipping transcription.")
     else:
         print("Transcript already exists.")
 
-    # 5. N8N Trigger    
+    # # 5. Signal Analysis (Audio & Chat) - To be implemented in other PR
+    # rechat_path = os.path.join(output_dir, "original.mp4.rechat.json")
+    # if os.path.exists(output_original):
+    #     print(f"Running signal analysis for: {output_original}")
+    #     try:
+    #         analyzer.analyze_video(
+    #             video_path=output_original,
+    #             chat_json=rechat_path if os.path.exists(rechat_path) else None
+    #         )
+    #     except Exception as e:
+    #         print(f"Signal analysis failed: {e}")
+    # else:
+    #     print("Video missing, skipping analysis.")
+
+    # 6. N8N Trigger    
     try:
         n8n.trigger('analyze', trigger_folder)
     except Exception as e:
@@ -307,16 +353,5 @@ def download_video(url, root_dir=".", audio=True, start_min=None, duration_min=N
 
     print("\nDone!")
 
-def main():
-    parser = argparse.ArgumentParser(description="Twitch Downloader (via yt-dlp)")
-    parser.add_argument("url", help="Twitch VOD URL")
-    parser.add_argument("--root_dir", default=".", help="Root directory to create video folder in (default: current directory)")
-    parser.add_argument("--audio", action='store_true', default=True, help="Extract audio (default: True)")
-    parser.add_argument("--start_min", type=int, default=None, help="Start time in minutes for slicing")
-    parser.add_argument("--duration_min", type=int, default=None, help="Duration in minutes for slicing")
-    args = parser.parse_args()
-
-    download_video(args.url, root_dir=args.root_dir, audio=args.audio, start_min=args.start_min, duration_min=args.duration_min)
-
 if __name__ == "__main__":
     main()
diff --git a/uv.lock b/uv.lock
index cef1a0a..6ecca82 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,9 +2,21 @@ version = 1
 revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.14'",
-    "python_full_version == '3.13.*'",
-    "python_full_version < '3.13'",
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
 ]
 
 [[package]]
@@ -18,37 +30,38 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.12.0"
+version = "4.12.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266, upload-time = "2025-11-28T23:37:38.911Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362, upload-time = "2025-11-28T23:36:57.897Z" },
+    { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
 ]
 
 [[package]]
 name = "argostranslate"
-version = "1.10.0"
+version = "1.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "ctranslate2" },
+    { name = "minisbd" },
     { name = "packaging" },
     { name = "sacremoses" },
     { name = "sentencepiece" },
     { name = "spacy" },
     { name = "stanza" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/4f/ee/244eedf38763cadc60e8949995a38ec2f4826e4e38bc6cc3a63f77455ecd/argostranslate-1.10.0.tar.gz", hash = "sha256:237f0bdaef5a45aade034ac712c1d0c18fd42887fc08141808dcadde7bfd1f67", size = 38918, upload-time = "2025-10-21T09:36:14.419Z" }
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 dependencies = [
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     { name = "assemblyai" },
+    { name = "chat-downloader" },
     { name = "deep-translator" },
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@@ -1611,14 +2100,18 @@ dependencies = [
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     { name = "ipython" },
     { name = "mlx-whisper" },
+    { name = "numpy" },
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     { name = "pillow" },
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+    { name = "ultralytics", specifier = ">=8.4.24" },
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+    { url = "https://files.pythonhosted.org/packages/cd/13/5093bcb954878e50f7217fd2ab94282b53934022e4e4a03265582da83bf5/yt_dlp-2026.3.17-py3-none-any.whl", hash = "sha256:32992db94303a8a5d211a183f2174834fe7f8c29d83ed2e7a324eae97a8f26d8", size = 3315134, upload-time = "2026-03-17T23:42:57.863Z" },
 ]
diff --git a/yt_download.py b/yt_download.py
index cccf767..1142cf3 100644
--- a/yt_download.py
+++ b/yt_download.py
@@ -5,14 +5,14 @@
 import yt_dlp
 import subprocess
 from transcript import transcribe_video
+from typing import Optional, Dict
 
-def clean_filename(text):
-    """Remove invalid characters for folder names"""
-    # Replace invalid chars with underscore or empty
+def clean_filename(text: str) -> str:
+    """Remove invalid characters for folder names."""
     return re.sub(r'[\\/*?:"<>|]', "", text).strip()
 
-def validate_youtube_url(url):
-    """Simple validation for YouTube URLs"""
+def validate_youtube_url(url: str) -> bool:
+    """Simple validation for YouTube URLs."""
     youtube_regex = (
         r'(https?://)?(www\.)?'
         r'(youtube|youtu|youtube-nocookie)\.(com|be)/'
@@ -21,130 +21,102 @@ def validate_youtube_url(url):
     )
     return re.match(youtube_regex, url) is not None
 
-def download_video(url, root_dir=".", force_transcript=False, extract_audio=True):
+def _extract_audio(input_video: str, output_audio: str, use_copy: bool = False):
+    """Helper to extract audio from video using FFmpeg."""
+    if os.path.exists(output_audio):
+        return
+    
+    print(f"Extracting audio to {output_audio}...")
+    cmd = ["ffmpeg", "-y", "-i", input_video, "-vn"]
+    if use_copy:
+        cmd += ["-c:a", "copy"]
+    
+    try:
+        subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE)
+    except subprocess.CalledProcessError as e:
+        print(f"Audio extraction failed: {e}")
+
+def _download_chunked(url: str, output_dir: str, duration: int, extract_audio: bool):
+    """Downloads video in 1-hour chunks."""
+    for i, start_sec in enumerate(range(0, int(duration), 3600)):
+        end_sec = min(start_sec + 3600, duration)
+        chunk_name = f"{start_sec//60}-{int(end_sec//60)}"
+        chunk_dir = os.path.join(output_dir, chunk_name)
+        
+        if not os.path.exists(chunk_dir):
+            os.makedirs(chunk_dir)
+             
+        print(f"--> Processing Chunk {i+1}: {chunk_name} ({start_sec}-{end_sec}s)")
+        chunk_output = os.path.join(chunk_dir, "original.mp4")
+        
+        if not os.path.exists(chunk_output):
+            ydl_opts = {
+                'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
+                'outtmpl': chunk_output,
+                'merge_output_format': 'mp4',
+                'download_ranges': lambda _, _2, s=start_sec, e=end_sec: [{'start_time': s, 'end_time': e}],
+            }
+            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+                ydl.download([url])
+
+        if extract_audio:
+            _extract_audio(chunk_output, os.path.join(chunk_dir, "audio.wav"))
+
+def download_video(url: str, root_dir: str = ".", force_transcript: bool = False, extract_audio: bool = True):
     if not validate_youtube_url(url):
         print(f"Error: Invalid YouTube URL: {url}")
         sys.exit(1)
 
-    print(f"Processing URL: {url}")
-
-    # 1. Get Video Information (Metadata)
-    ydl_opts_meta = {
-        'quiet': True,
-        'no_warnings': True,
-    }
-    
+    # 1. Get Metadata
     try:
-        with yt_dlp.YoutubeDL(ydl_opts_meta) as ydl:
+        with yt_dlp.YoutubeDL({'quiet': True}) as ydl:
             info = ydl.extract_info(url, download=False)
             duration = info.get('duration', 0)
+            title = info.get('title', 'Untitled')
+            description = info.get('description', '')
     except Exception as e:
         print(f"Error fetching metadata: {e}")
         sys.exit(1)
 
-    title = info.get('title', 'Untitled')
-    description = info.get('description', '')
-    
-    safe_title = clean_filename(title)
-    safe_title = safe_title[:240] 
-
-    root_dir = os.path.abspath(root_dir)
-    output_dir = os.path.join(root_dir, safe_title)
-
-    transcript_output = os.path.join(output_dir, "transcript.srt")
-    
+    output_dir = os.path.join(os.path.abspath(root_dir), clean_filename(title)[:240])
     if not os.path.exists(output_dir):
         os.makedirs(output_dir)
-        print(f"Created directory: {output_dir}")
-    else:
-        print(f"Using existing directory: {output_dir}")
 
-    # 2. Download Video
-    output_template = os.path.join(output_dir, "original.mp4")
-    
-    ydl_opts_download = {
+    # 2. Chunked Download for long videos
+    if duration > 3600:
+        print(f"Long video ({duration}s). Switching to chunked download.")
+        _download_chunked(url, output_dir, duration, extract_audio)
+        return
+
+    # 3. Standard Download
+    video_output = os.path.join(output_dir, "original.mp4")
+    print(f"Downloading: {title}")
+    with yt_dlp.YoutubeDL({
         'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
-        'outtmpl': output_template,
+        'outtmpl': video_output,
         'merge_output_format': 'mp4',
-    }
+    }) as ydl:
+        ydl.download([url])
 
-    print(f"Downloading video '{title}'...")
-    try:
-        with yt_dlp.YoutubeDL(ydl_opts_download) as ydl:
-            ydl.download([url])
-    except Exception as e:
-        print(f"Download failed: {e}")
-        sys.exit(1)
+    # 4. Post-processing
+    with open(os.path.join(output_dir, "metadata.md"), "w", encoding="utf-8") as f:
+        f.write(f"```\nSource: {url}\nTitle: {title}\nDescription: {description}\n```\n")
 
-    # 3. Create Metadata File
-    metadata_path = os.path.join(output_dir, "metadata.md")
-    print(f"Writing metadata to: {metadata_path}")
-    
-    with open(metadata_path, "w", encoding="utf-8") as f:
-        f.write("```\n")
-        f.write(f"Source: {url}\n")
-        f.write(f"Title: {title}\n")
-        f.write(f"Description: {description}\n")
-        f.write("```\n")
-
-    print(f"Output saved in: {output_dir}")
-
-    # 2.5 Extract Audio (if requested or forced by transcript)
-    if force_transcript:
-        extract_audio = True
-
-    if extract_audio:
-        print(f"Extracting audio to audio.mp4...")
-        audio_path = os.path.join(output_dir, "audio.mp4")
-        # ffmpeg -i input.mp4 -vn -c:a copy output.mp4
-        cmd = [
-            "ffmpeg", "-y",
-            "-i", output_template,
-            "-vn",
-            "-c:a", "copy",
-            audio_path
-        ]
-        try:
-             # Run quietly
-             subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE)
-             print(f"Audio extracted: {audio_path}")
-        except subprocess.CalledProcessError as e:
-             print(f"Audio extraction failed: {e}")
-
-    # 4. Auto-Transcribe if Short
-    is_short = "/shorts/" in url or (duration > 0 and duration < 180)
-    
-    split_by_hour = True
-    if is_short:
-        split_by_hour = False
-    
+    if extract_audio or force_transcript:
+        _extract_audio(video_output, os.path.join(output_dir, "audio.mp4"), use_copy=True)
+
+    # 5. Transcription
+    is_short = "/shorts/" in url or (0 < duration < 180)
     if is_short or force_transcript:
-        reason = "Short video" if is_short else "Forced via flag"
-        print(f"\n[Auto-Transcribe] Triggered ({reason}).")
-        print("Starting transcription...")
-        try:
-            transcribe_video(
-                input_file=output_template,
-                output_file=transcript_output,
-                google_translate=True,
-                # zh_output="zh.srt",
-                # split_by_hour=split_by_hour,
-                # speaker_labels=True,
-                
-            )
-        except Exception as e:
-            print(f"Transcription failed: {str(e)}")
-    else:
-        print(f"\nVideo duration is {duration}s. Skipping auto-transcription (only for < 180s).")
-
-    print("\nDone!")
+        print(f"\nAuto-transcribing...")
+        transcribe_video(input_file=video_output, output_file=os.path.join(output_dir, "transcript.srt"), google_translate=True)
 
 def main():
     parser = argparse.ArgumentParser(description="YouTube Downloader")
     parser.add_argument("url", help="YouTube Video URL")
-    parser.add_argument("--root_dir", default=".", help="Root directory to create video folder in (default: current directory)")
-    parser.add_argument("--transcript", action="store_true", default=False, help="Force generate transcript")
-    parser.add_argument("--audio", action=argparse.BooleanOptionalAction, default=True, help="Extract audio stream (default: True)")
+    parser.add_argument("--root_dir", default=".", help="Root directory")
+    parser.add_argument("--transcript", action="store_true", help="Force transcript")
+    parser.add_argument("--audio", action=argparse.BooleanOptionalAction, default=True, help="Extract audio")
     args = parser.parse_args()
 
     download_video(args.url, root_dir=args.root_dir, force_transcript=args.transcript, extract_audio=args.audio)