created remote transcriber

package/__init__.py

@@ -20,6 +20,7 @@ from .config import CHANNEL_NAME, TZ, DB
 from .hash_tags import get_top_nouns
 from .process_uploads import process_uploads
 from .transcribe_video import transcribe_to_srt
+from .transcribe_client import transcribe_to_srt_remote
 from .utils import get_chapters, get_video_id_from_chat, write_csv, run_command
 
 # 4. Expose Subpackages
@@ -36,7 +37,8 @@ __all__ = [
     "DB",
     "get_top_nouns",
     "process_uploads",
-    "transcribe_to_srt", 
+    "transcribe_to_srt",
+    "transcribe_to_srt_remote",
     "write_csv",
     "run_command",
     "get_chapters",

package/build_videos.py

@@ -3,7 +3,9 @@ from datetime import datetime
 
 import package.video_editing.build_shorts as yt_shorts
 import package.transcribe_video as transcribe_video
+import package.transcribe_client as transcribe_client
 import package.twitch.chat_vod as chat_vod
+import package.video_editing.highlights as highlights
 from package.config import DB as DB
 from package.config import TZ as TZ
 
@@ -22,19 +24,20 @@ def build_chat_video():
     print("====================================================")
 
 def build_transcribe():
-    unuploadeds = DB.get_unuploaded()
+    downloads = DB.get_downloaded()
 
-    for unuploaded in unuploadeds:
-        if unuploaded.clip_is:
+    for download in downloads:
+        if download.clip_is:
             target_dir = f"download/clips"
         else:
             target_dir = f"download/videos"
 
         print("====================================================")
-        print(f"🚀 Transcribe: {unuploaded.title}")
+        print(f"🚀 Transcribe: {download.title}")
         print("====================================================")
         
-        transcribe_video.transcribe_to_srt(f"{target_dir}/{unuploaded.id}/{unuploaded.id}.mp4")
+        #transcribe_video.transcribe_to_srt(f"{target_dir}/{unuploaded.id}/{unuploaded.id}.mp4", True)
+        transcribe_client.transcribe_to_srt_remote(f"{target_dir}/{download.id}/{download.id}.mp4")
 
     print("====================================================")
     print(f"✅ Processing: Transcribing Done.")
@@ -65,6 +68,32 @@ def build_shorts():
     print(f"✅ Processing: Clip to Youtube Short Done.")
     print("====================================================")
 
+def build_highlights():
+    lights = DB.get_downloaded()
+
+    top_txt = "TeamPGP Live on Twitch...\n Every Friday and Sunday @7:30 ET.\n twitch.tv/teampgp"
+    count = 1
+
+    for light in lights:
+        target_dir = f"download/clips/{light.id}/{light.id}.mp4"
+
+        print("====================================================")
+        print(f"🚀 Processing: Highlights {light.title}")
+        print("====================================================")
+        print(f"{count}/{len(lights)}")
+
+        created_at = datetime.strptime(light.created_at, "%Y-%m-%d %H:%M:%S%z")
+        created_at = created_at.astimezone(TZ)    
+
+        #ToDo
+        highlights.highlights(target_dir, [], 5, 8)
+        #yt_shorts.fit_to_9_16_letterbox(target_dir, top_txt, f"Clipped By: {light.creator_name},\n on {created_at.strftime("%Y-%m-%d %H:%M:%S %Z")}")
+        count += 1
+
+    print("====================================================")
+    print(f"✅ Processing: Clip to Youtube Short Done.")
+    print("====================================================")
+
 def build_videos():
     build_shorts()
     build_transcribe()

package/database/database.py

@@ -25,12 +25,14 @@ class TwitchVideo(Base):
     uploaded_yt = Column(Boolean, nullable=False, default=False)
     uploaded_yt_chats = Column(Boolean, nullable=False, default=False)
     uploaded_yt_shorts = Column(Boolean, nullable=False, default=False)
+    uploaded_tiktok = Column(Boolean, nullable=False, default=False)
 
     # This now actually points to twitch_videos.id
     related_video_id = Column(String, ForeignKey("twitch_videos.id"), nullable=True)
 
     # Relationship back to YoutubeInfo (uselist=True enforces a 1-to-many structure)
     youtube_info = relationship("YoutubeInfo", back_populates="twitch_video", uselist=True)
+    tiktok_info = relationship("TiktokInfo", back_populates="twitch_video", uselist=True)
     chapters = relationship("Chapter", back_populates="twitch_video", cascade="all, delete-orphan")
 
 # 3. Define the data table
@@ -61,6 +63,17 @@ class YoutubeInfo(Base):
     # Relationship linking back to the twitch video record
     twitch_video = relationship("TwitchVideo", back_populates="youtube_info")
 
+class TiktokInfo(Base):
+    __tablename__ = "tiktok_info"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    twitch_video_id = Column(String, ForeignKey("twitch_videos.id"), nullable=False)
+    tiktok_id = Column(String, nullable=True)
+    tiktok_date = Column(String, nullable=True)
+
+    # Relationship linking back to the twitch video record
+    twitch_video = relationship("TwitchVideo", back_populates="tiktok_info")
+
 class Chapter(Base):
     __tablename__ = "chapters"
 

package/process_uploads.py

@@ -21,7 +21,8 @@ def process_videos(video_type: bool = True, limit: int = 22):
     meta_data: Data = DB.get_data()[0]
 
     current_time = datetime.now(TZ)
-    release_time = datetime.strptime(meta_data.date_shorts, "%Y-%m-%d %H:%M:%S%z")
+    next_time = meta_data.date_videos if video_type else meta_data.date_shorts
+    release_time = datetime.strptime(next_time, "%Y-%m-%d %H:%M:%S%z")
 
     if current_time > release_time:
         release_time = release_time.replace(hour=12, minute=0, second=0, microsecond=0)
@@ -117,7 +118,8 @@ def process_videos(video_type: bool = True, limit: int = 22):
             youtube_info.yt_short_date = datetime.now(TZ)
 
         DB.insert(youtube_info)
-        release_time += timedelta(days=1)
+        #release_time += timedelta(days=1)
+        release_time += timedelta(hours=12) # release every 12 hours instead of every day
 
     # Update global release pointer once at end
     if video_type:

package/tiktok/__init__.py

@@ -0,0 +1,9 @@
+# package/tiktok/__init__.py
+
+# Expose components from individual modules using relative imports
+from .uploader import upload_to_tiktok
+
+# Explicitly define what is exposed when importing the subpackage
+__all__ = [
+    "upload_to_tiktok"
+]

package/tiktok/uploader.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+from tiktok_uploader.upload import upload_video
+from pathlib import Path
+import json
+import time
+from playwright.sync_api import sync_playwright
+
+COOKIES_FILE = "cookies.txt"
+
+def get_last_video_link(username):
+    """Grab newest video link from profile"""
+    url = f"https://www.tiktok.com/@{username}"
+    with sync_playwright() as p:
+        browser = p.chromium.launch(headless=True)
+        context = browser.new_context(storage_state=COOKIES_FILE) if Path(COOKIES_FILE).exists() else browser.new_context()
+        page = context.new_page()
+        page.goto(url, timeout=60000)
+        page.wait_for_selector('[data-e2e="user-post-item"] a', timeout=15000)
+        first_video = page.query_selector('[data-e2e="user-post-item"] a')
+        link = first_video.get_attribute('href') if first_video else None
+        browser.close()
+        return link
+
+def upload_to_tiktok(video_path, description, hashtags=[], username="YOUR_USERNAME_HERE"):
+    tags = " ".join([f"#{t}" for t in hashtags])
+    full_desc = f"{description}\n\n{tags}"
+    
+    failed = upload_video(
+        filename=str(video_path),
+        description=full_desc,
+        cookies=COOKIES_FILE,
+        headless=True,
+        browser="chrome"
+    )
+    
+    if len(failed) != 0:
+        print(f"❌ Failed {video_path}: {failed}")
+        return None
+
+    print(f"✅ Uploaded {video_path} - fetching link...")
+    time.sleep(8) # give TikTok time to process
+
+    # Try 3 times, TikTok takes ~10-30s to show new video
+    for i in range(3):
+        link = get_last_video_link(username)
+        if link:
+            print(f"🔗 {link}")
+            return link
+        print(f" Waiting for TikTok to publish... retry {i+1}/3")
+        time.sleep(10)
+    
+    return f"https://www.tiktok.com/@{username} (uploaded, but link not ready yet - check profile)"
+
+if __name__ == "__main__":
+    USERNAME = "your_tiktok_username" # <-- CHANGE THIS, no @
+    
+    with open("highlights.json") as f:
+        data = json.load(f)
+    
+    for h in data['highlights'][:3]:
+        video_file = f"highlights/highlight_{h['id']}_{h['start_timestamp'].replace(':','-')}.mp4"
+        if not Path(video_file).exists():
+            continue
+            
+        desc = h['transcript'][:150]
+        link = upload_to_tiktok(
+            video_file,
+            description=desc,
+            hashtags=["fyp", "clutch", "highlight", "pog"],
+            username=USERNAME
+        )
+        print(f"LINK: {link}\n")

package/transcribe_client.py

@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+
+#!/usr/bin/env python3
+import threading
+# --- HOTFIX for Python 3.13.5 _DeleteDummyThreadOnDel spam ---
+# The CPython fix in 3.13.4 didn't fully kill it for debugpy/asyncio cases
+if hasattr(threading, '_DeleteDummyThreadOnDel'):
+    threading._DeleteDummyThreadOnDel.__del__ = lambda self: None
+
+import os
+import shutil
+# ... rest of your imports
+import os
+import shutil
+import subprocess
+import json
+from pathlib import Path
+import wave
+import requests
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+CHUNK_DURATION_SEC = 600 # target chunk size
+OVERLAP_SEC = 8 # overlap to prevent cut words
+VAD_SEARCH_WINDOW = 15 # look for silence within 15s of target
+WORKER_URL = "http://192.168.5.55:8000/transcribe"
+MAX_WORKERS = 4 # perfect for 4070 Ti Super 16GB
+
+def get_duration(video_path: str) -> float:
+    cmd = ["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "json", video_path]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    return float(json.loads(result.stdout)["format"]["duration"])
+
+def find_silence_near(video_path: str, target_time: float) -> float:
+    """Find nearest silence within VAD_SEARCH_WINDOW of target_time"""
+    start_search = max(0, target_time - VAD_SEARCH_WINDOW/2)
+    cmd = [
+        "ffmpeg", "-ss", str(start_search), "-t", str(VAD_SEARCH_WINDOW),
+        "-i", video_path, "-af", "silencedetect=noise=-30dB:d=0.5",
+        "-f", "null", "-"
+    ]
+    result = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE, text=True)
+    # Parse last silence end before target
+    best = target_time
+    for line in result.stderr.splitlines():
+        if "silence_end:" in line:
+            try:
+                t = float(line.split("silence_end:")[1].split("|")[0].strip())
+                abs_t = start_search + t
+                if abs(abs_t - target_time) < abs(best - target_time):
+                    best = abs_t
+            except: pass
+    return best
+
+def extract_audio_and_chunk_smart(video_path: str, output_dir: Path):
+    print(f"🚀 Smart chunking: target {CHUNK_DURATION_SEC}s + {OVERLAP_SEC}s overlap")
+    output_dir.mkdir(parents=True, exist_ok=True)
+    total_dur = get_duration(video_path)
+    print(f"📏 Total duration: {total_dur/60:.1f} min")
+
+    chunks = []
+    start = 0.0
+    idx = 0
+
+    while start < total_dur:
+        # Find smart cut point
+        target_end = start + CHUNK_DURATION_SEC
+        if target_end >= total_dur:
+            end = total_dur
+        else:
+            end = find_silence_near(video_path, target_end)
+            # clamp
+            end = max(start + CHUNK_DURATION_SEC * 0.7, min(end, start + CHUNK_DURATION_SEC * 1.3))
+
+        chunk_path = output_dir / f"chunk_{idx:03d}.wav"
+        # Extract with overlap for next chunk
+        extract_end = end + (OVERLAP_SEC if end < total_dur else 0)
+
+        cmd = [
+            "ffmpeg", "-y", "-ss", str(start), "-to", str(extract_end),
+            "-i", video_path, "-vn", "-ac", "1", "-ar", "16000", "-acodec", "pcm_s16le",
+            str(chunk_path)
+        ]
+        subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True)
+
+        chunks.append((chunk_path, start, end)) # path, true_start, true_end (without overlap)
+        print(f" Chunk {idx}: {start:.1f}s -> {end:.1f}s (file to {extract_end:.1f}s)")
+
+        if end >= total_dur:
+            break
+        start = end - OVERLAP_SEC/2 # start next chunk with half overlap
+        idx += 1
+
+    return chunks
+
+def format_srt_time(seconds: float):
+    h = int(seconds // 3600)
+    m = int((seconds % 3600) // 60)
+    s = int(seconds % 60)
+    ms = int((seconds % 1) * 1000)
+    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"
+
+def send_chunk_for_transcription(args):
+    chunk_path, true_start, true_end, chunk_idx = args
+    try:
+        with open(chunk_path, 'rb') as f:
+            files = {'file': (chunk_path.name, f, 'audio/wav')}
+            resp = requests.post(WORKER_URL, files=files, timeout=1200)
+            resp.raise_for_status()
+            data = resp.json()
+            # Filter to only keep segments within true window (dedupe overlap)
+            filtered = []
+            for seg in data['segments']:
+                abs_start = true_start + seg['start']
+                # keep if it starts before the true_end (allow small spill)
+                if abs_start < true_end + OVERLAP_SEC/2:
+                    # if it's in overlap zone of NEXT chunk, only keep first half
+                    if abs_start < true_end - OVERLAP_SEC/2 or chunk_idx == 0:
+                        filtered.append(seg)
+                    elif seg['start'] < (true_end - true_start) - OVERLAP_SEC/2 + 1.0:
+                        filtered.append(seg)
+            return chunk_idx, true_start, filtered, None
+    except Exception as e:
+        return chunk_idx, true_start, None, str(e)
+
+def transcribe_to_srt_remote(video_path: str, force: bool = False):
+    video_path_obj = Path(video_path)
+    srt_path = video_path_obj.with_suffix(".srt")
+    temp_dir = video_path_obj.parent / f"temp_chunks_{video_path_obj.stem}"
+
+    if srt_path.exists() and not force:
+        print(f"❌ SRT already exists: {srt_path}")
+        return
+    if srt_path.exists():
+        srt_path.unlink()
+
+    audio_chunks = extract_audio_and_chunk_smart(str(video_path_obj), temp_dir)
+    jobs = [(path, s, e, idx) for idx, (path, s, e) in enumerate(audio_chunks)]
+
+    results = []
+    executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)
+    try:
+        futures = {executor.submit(send_chunk_for_transcription, job): job for job in jobs}
+        for future in as_completed(futures):
+            idx, offset, segments, error = future.result()
+            if error:
+                print(f"❌ Chunk {idx} failed: {error}")
+            else:
+                print(f"✅ Chunk {idx} done: {len(segments)} segs (offset {offset:.1f}s)")
+                results.append((idx, offset, segments))
+    finally:
+        executor.shutdown(wait=True)
+
+    results.sort(key=lambda x: x[0])
+
+    print("📝 Writing final SRT with smart overlap dedupe...")
+    global_idx = 1
+    with open(srt_path, "w", encoding="utf-8") as srt_file:
+        for _, time_offset, segments in results:
+            for seg in segments:
+                actual_start = seg['start'] + time_offset
+                actual_end = seg['end'] + time_offset
+                srt_file.write(f"{global_idx}\n{format_srt_time(actual_start)} --> {format_srt_time(actual_end)}\n{seg['text'].strip()}\n\n")
+                global_idx += 1
+
+    print(f"\n✅ SRT saved: {srt_path} - {global_idx-1} lines")
+    if temp_dir.exists():
+        shutil.rmtree(temp_dir)
+
+if __name__ == "__main__":
+    target_video = "download/videos/2813112936/2813112936.mp4"
+    transcribe_to_srt_remote(target_video, force=True)

package/transcribe_video.py

@@ -3,117 +3,117 @@ import os
 import shutil
 import subprocess
 from pathlib import Path
+import wave
 from faster_whisper import WhisperModel
-from faster_whisper.utils import format_timestamp
 
-# Prevent OpenMP thread conflicts from crashing the script
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
-# 5 minutes per chunk (300 seconds) keeps RAM usage low and stable
 CHUNK_DURATION_SEC = 300 
 
-def extract_audio_and_chunk(video_path: str, output_dir: Path) -> list:
-    """Extracts and splits audio into 5-minute chunks using a single FFmpeg pass."""
+def extract_audio_and_chunk(video_path: str, output_dir: Path) -> list[tuple[Path, float]]:
+    """Returns list of (chunk_path, start_offset_seconds)"""
     print("🚀 Extracting and chunking audio with FFmpeg...")
     output_dir.mkdir(parents=True, exist_ok=True)
     
-    # Segment format output: chunk_000.wav, chunk_001.wav, etc.
     chunk_pattern = str(output_dir / "chunk_%03d.wav")
     
     command = [
         "ffmpeg", "-y", "-i", video_path,
         "-vn", "-ac", "1", "-ar", "16000",
-        "-acodec", "pcm_s16le", "-sn", "-map_chapters", "-1",
+        "-acodec", "pcm_s16le",
         "-f", "segment", "-segment_time", str(CHUNK_DURATION_SEC),
+        "-reset_timestamps", "0", # keep each chunk starting at 0
         chunk_pattern
     ]
     
     result = subprocess.run(command, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE, text=True)
     if result.returncode != 0:
-        print(f"❌ FFmpeg Error Output:\n{result.stderr}")
-        raise RuntimeError("FFmpeg extraction and chunking failed.")
-        
-    # Return sorted list of generated chunk files
-    return sorted(list(output_dir.glob("chunk_*.wav")))
+        print(f"❌ FFmpeg Error:\n{result.stderr}")
+        raise RuntimeError("FFmpeg failed")
 
+    chunks = sorted(output_dir.glob("chunk_*.wav"))
+    
+    # FIX: Calculate real offset from actual file durations, not idx*300
+    offsets = []
+    current_offset = 0.0
+    for chunk_path in chunks:
+        offsets.append((chunk_path, current_offset))
+        with wave.open(str(chunk_path), 'rb') as wf:
+            duration = wf.getnframes() / wf.getframerate()
+            current_offset += duration
+            
+    return offsets
+
+def format_srt_time(seconds: float):
+    h = int(seconds // 3600)
+    m = int((seconds % 3600) // 60)
+    s = int(seconds % 60)
+    ms = int((seconds % 1) * 1000)
+    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"
 
 def transcribe_to_srt(video_path: str, force: bool = False):
     if not hasattr(transcribe_to_srt, "model"):
         print("🔧 Initializing C++ Engine...")
         transcribe_to_srt.model = WhisperModel(
-            "base", 
-            device="cpu", 
-            compute_type="int8", 
-            cpu_threads=0, # Let CTranslate2 auto-detect safe core counts
-            num_workers=1         
+            "distil-small.en",
+            device="cpu",
+            compute_type="int8",
+            cpu_threads=4, # 4 = physical cores on E3-1280 v5 = fastest
+            num_workers=1
         )
-        print("✅ C++ Model loaded successfully.")
-
+        print("✅ Model loaded.")
 
     video_path_obj = Path(video_path)
     srt_path = video_path_obj.with_suffix(".srt")
     temp_dir = video_path_obj.parent / f"temp_chunks_{video_path_obj.stem}"
 
+    if srt_path.exists() and not force:
+        print(f"❌ SRT already exists: {srt_path}")
+        return
     if srt_path.exists():
-        if not force:
-            print(f"❌ Error: Transcription file already exists: {srt_path}")
-            return
-        else:
-            srt_path.unlink()
+        srt_path.unlink()
 
     try:
-        # Step 1: Split audio into bite-sized pieces
         audio_chunks = extract_audio_and_chunk(str(video_path_obj), temp_dir)
-        if not audio_chunks:
-            print("❌ Error: No audio chunks were generated.")
-            return
+        print(f"📦 {len(audio_chunks)} chunks.")
+        print("🎙 Transcribing...")
 
-        print(f"📦 Successfully split audio into {len(audio_chunks)} chunks.")
-        print("🎙️ Starting safe chunk-by-chunk transcription...")
-
-        global_segment_index = 1
-        
+        global_idx = 1
         with open(srt_path, "w", encoding="utf-8") as srt_file:
-            for chunk_idx, chunk_path in enumerate(audio_chunks):
-                # Calculate the time offset for the current chunk
-                time_offset = chunk_idx * CHUNK_DURATION_SEC
-                print(f"\n⏳ Processing chunk {chunk_idx + 1}/{len(audio_chunks)} ({chunk_path.name})...")
+            for chunk_idx, (chunk_path, time_offset) in enumerate(audio_chunks):
+                print(f"\n⏳ Chunk {chunk_idx+1}/{len(audio_chunks)} offset={time_offset:.1f}s")
                 
-                segments_generator, info = transcribe_to_srt.model.transcribe(
+                segments, info = transcribe_to_srt.model.transcribe(
                     str(chunk_path), 
+                    language="en",
                     beam_size=1,
                     vad_filter=True,
-                    temperature=0.0
+                    vad_parameters=dict(min_silence_duration_ms=500),
+                    condition_on_previous_text=False # important for chunked mode
                 )
                 
-                # Consume chunk generator and shift timestamps instantly
-                for segment in segments_generator:
-                    # Shift timestamps relative to the original video timeline
-                    actual_start = segment.start + time_offset
-                    actual_end = segment.end + time_offset
+                for seg in segments:
+                    actual_start = seg.start + time_offset
+                    actual_end = seg.end + time_offset
                     
-                    start_str = format_timestamp(actual_start, always_include_hours=True)
-                    end_str = format_timestamp(actual_end, always_include_hours=True)
+                    # Use comma for SRT, not dot
+                    srt_file.write(f"{global_idx}\n{format_srt_time(actual_start)} --> {format_srt_time(actual_end)}\n{seg.text.strip()}\n\n")
+                    global_idx += 1
                     
-                    srt_file.write(f"{global_segment_index}\n{start_str} --> {end_str}\n{segment.text.strip()}\n\n")
-                    global_segment_index += 1
-                    
-                # Free up space as we go by deleting the processed chunk
                 chunk_path.unlink()
 
-        print(f"\n✅ All chunks combined! SRT subtitle file saved in: {srt_path}")
+        print(f"\n✅ SRT saved: {srt_path}")
 
     except Exception as e:
-        print(f"\n❌ Execution Error: {e}")
+        print(f"\n❌ Error: {e}")
+        import traceback; traceback.print_exc()
     finally:
-        # Clean up the temporary folder entirely
         if temp_dir.exists():
             shutil.rmtree(temp_dir)
 
-
 if __name__ == "__main__":
     target_video = "download/videos/2813112936/2813112936.mp4"
     if not os.path.exists(target_video):
-        print(f"❌ System Error: Target video file does not exist at path: {target_video}")
+        print(f"❌ Not found: {target_video}")
     else:
-        transcribe_to_srt(target_video, force=True)
+        transcribe_to_srt(target_video, force=True)

package/video_editing/highlights.py

@@ -0,0 +1,350 @@
+import re
+import cv2
+import numpy as np
+import argparse
+import json
+from pathlib import Path
+from moviepy import VideoFileClip
+
+# ---- CONFIG ----
+DEFAULT_KEYWORDS = [
+    # Pure Hype - the #1 highlight signal
+    "pog", "poggers", "pogchamp", "let's go", "lets go", "huge", "insane", "crazy",
+    "unbelievable", "no way", "way too good", "oh my god", "omg", "wow", "holy",
+    "holy shit", "holy fuck", "what the fuck", "wtf", "lfg", "clip that", "clip it",
+    "someone clip", "chat clip", "world record", "pb", "personal best",
+
+    # Chat interaction = something happened
+    "chat", "look at chat", "chat is going crazy", "we are so back", "we're so back",
+    "it's over", "ggs", "gg", "not like this", "like this", "AYAYA", "KEKW",
+    "monka", "pepega", "copium",
+
+    # Gaming clutch moments
+    "clutch", "ace", "1v4", "1v3", "1v2", "quadra", "penta", "pentakill",
+    "triple kill", "rampage", "unstoppable", "godlike", "wipe", "team wipe",
+    "no scope", "collat", "one shot", "one tap", "200 pumped", "knocked", "cracked",
+
+    # Reaction / laugh / fail
+    "bro", "dude", "what is happening", "what just happened", "there is no way",
+    "you're kidding", "stop", "no no no", "yes yes yes", "i can't", "i cant believe",
+    "this is insane", "this guy", "look at this", "wait wait wait"
+]
+
+# You can add your own: --keywords "goal, fight, knock out"
+import subprocess
+import tempfile
+import os
+from scipy.io import wavfile
+
+def get_av_scores(video_path, sample_fps=2):
+    print(f"-> Opening: {video_path}", flush=True)
+    if not os.path.exists(video_path):
+        raise FileNotFoundError(video_path)
+
+    with VideoFileClip(video_path) as clip:
+        duration = clip.duration
+        fps = clip.fps or 30
+        has_audio = clip.audio is not None
+
+    print(f" duration={duration:.2f}s fps={fps} has_audio={has_audio}", flush=True)
+    n_secs = int(duration)
+
+     # ---- 1. AUDIO (streaming, constant RAM) ----
+    print(f"[1/3] Audio energy...", flush=True)
+    audio_per_sec = np.zeros(n_secs)
+
+    if has_audio:
+        tmp_wav = tempfile.mktemp(suffix=".wav")
+        try:
+            print(f" extracting mono 16k wav with ffmpeg...", flush=True)
+            cmd = [
+                "ffmpeg", "-y",
+                "-i", video_path,
+                "-ar", "16000",
+                "-ac", "1",
+                "-c:a", "pcm_s16le",
+                tmp_wav
+            ]
+            result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=300)
+            if result.returncode!= 0:
+                print(f" ffmpeg failed: {result.stderr.decode()[:1000]}")
+            else:
+                wav_size = os.path.getsize(tmp_wav)
+                print(f" wav size: {wav_size} bytes, computing RMS streaming...", flush=True)
+
+                # STREAM instead of loading all at once
+                import wave
+                with wave.open(tmp_wav, 'rb') as wf:
+                    sr = wf.getframerate() # should be 16000
+                    n_frames = wf.getnframes()
+                    # sr frames = 1 second
+                    for i in range(n_secs):
+                        if wf.tell() >= n_frames:
+                            break
+                        # read 1 second
+                        frames = wf.readframes(sr)
+                        if not frames:
+                            break
+                        # int16 -> float without extra copy
+                        chunk = np.frombuffer(frames, dtype=np.int16).astype(np.float32) / 32768.0
+                        if len(chunk) > 0:
+                            audio_per_sec[i] = np.sqrt(np.mean(chunk**2))
+
+                        if i % 300 == 0: # log every 5 min
+                            print(f" audio {i}/{n_secs}s", flush=True)
+
+                if audio_per_sec.max() > 0:
+                    audio_per_sec = (audio_per_sec - audio_per_sec.min()) / (audio_per_sec.max() - audio_per_sec.min() + 1e-6)
+                print(f" audio done", flush=True)
+
+        except Exception as e:
+            print(f" audio error: {e}")
+            import traceback; traceback.print_exc()
+        finally:
+            if os.path.exists(tmp_wav):
+                os.remove(tmp_wav)
+    else:
+        print(" no audio track", flush=True)
+
+    # ---- 2. MOTION ----
+    print(f"[2/3] Motion analysis sample_fps={sample_fps}...", flush=True)
+    cap = cv2.VideoCapture(video_path)
+    motion_scores = []
+    prev_gray = None
+    frame_interval = int(fps / sample_fps) if fps > 0 else 15
+    num_samples = int(duration * sample_fps)
+
+    for i in range(num_samples):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, i * frame_interval)
+        ret, frame = cap.read()
+        if not ret:
+            continue
+        gray = cv2.cvtColor(cv2.resize(frame, (320, 180)), cv2.COLOR_BGR2GRAY)
+        if prev_gray is not None:
+            motion_scores.append(np.mean(cv2.absdiff(gray, prev_gray)) / 255.0)
+        else:
+            motion_scores.append(0)
+        prev_gray = gray
+        if i % 50 == 0:
+            print(f" motion {i}/{num_samples}", flush=True)
+    cap.release()
+
+    if len(motion_scores) > 1:
+        x = np.linspace(0, duration-1, len(motion_scores))
+        motion_per_sec = np.interp(np.arange(n_secs), x, motion_scores)
+        if motion_per_sec.max() > 0:
+            motion_per_sec = (motion_per_sec - motion_per_sec.min()) / (motion_per_sec.max() - motion_per_sec.min() + 1e-6)
+    else:
+        motion_per_sec = np.zeros(n_secs)
+
+    combined_av = 0.6 * audio_per_sec + 0.4 * motion_per_sec
+    return combined_av, duration
+
+def srt_time_to_seconds(time_str):
+    # 00:01:23,456 or 00:01:23.456
+    time_str = time_str.strip().replace('.', ',')
+    h, m, s_ms = time_str.split(':')
+    s, ms = s_ms.split(',')
+    return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 1000.0
+
+def load_text_from_srt(srt_path):
+    print(f"[3/3] Loading transcript from SRT: {srt_path}")
+    if not Path(srt_path).exists():
+        raise FileNotFoundError(f"SRT not found: {srt_path}")
+
+    # utf-8-sig strips BOM that Whisper sometimes adds
+    with open(srt_path, 'r', encoding='utf-8-sig') as f:
+        content = f.read().strip()
+
+    if not content:
+        raise ValueError(f"SRT file is empty: {srt_path}")
+
+    # Split by blank line - works for \n\n and \r\n\r\n
+    blocks = re.split(r'\r?\n\r?\n', content)
+    
+    segments = []
+    # Pattern inside each block only
+    time_pattern = re.compile(r'(\d{2}:\d{2}:\d{2}[,\.]\d{3})\s*-->\s*(\d{2}:\d{2}:\d{2}[,\.]\d{3})')
+
+    for block in blocks:
+        block = block.strip()
+        if not block:
+            continue
+        
+        lines = block.splitlines()
+        if len(lines) < 2:
+            continue
+
+        # Find time line (line 0 or 1 - index may be missing)
+        time_match = None
+        text_start_idx = 0
+        for idx, line in enumerate(lines[:3]): # check first 3 lines for time
+            m = time_pattern.search(line)
+            if m:
+                time_match = m
+                text_start_idx = idx + 1
+                break
+        
+        if not time_match:
+            continue
+
+        start_str, end_str = time_match.groups()
+        text = ' '.join(lines[text_start_idx:]).strip()
+        
+        # Clean Whisper / Twitch artifacts
+        text = re.sub(r'<[^>]+>', '', text) # remove <i>, <font> etc
+        text = text.replace('\n', ' ').strip()
+        
+        if text:
+            segments.append({
+                "start": srt_time_to_seconds(start_str),
+                "end": srt_time_to_seconds(end_str),
+                "text": text
+            })
+    
+    print(f"Loaded {len(segments)} segments from SRT")
+    if len(segments) == 0:
+        print(f"WARNING: Parsed 0 segments. First 500 chars of file:\n{content[:500]}")
+    return segments
+
+def score_keywords(segments, duration, keywords):
+    """Returns per-second keyword boost score + keyword hits"""
+    keyword_score = np.zeros(int(duration))
+    hits = []
+
+    # Build regex with word boundaries for each keyword
+    # \bace\b won't match "place", \bgg\b won't match "ggs"
+    patterns = []
+    for kw in keywords:
+        kw = kw.strip()
+        if not kw:
+            continue
+        # \b at start/end, but handle multi-word phrases
+        # re.escape handles "let's go", "1v4", etc
+        pat = re.compile(r'\b' + re.escape(kw.lower()) + r'\b', re.IGNORECASE)
+        patterns.append((kw.lower(), pat))
+
+    for seg in segments:
+        text = seg['text']
+        text_lower = text.lower()
+        for kw_lower, pat in patterns:
+            if pat.search(text_lower):
+                start = int(max(0, seg['start'] - 1))
+                end = int(min(duration-1, seg['end'] + 2))
+                keyword_score[start:end+1] += 1.0
+                hits.append({
+                    "timestamp": round(seg['start'], 2),
+                    "end": round(seg['end'], 2),
+                    "keyword": kw_lower,
+                    "text": text.strip()
+                })
+                break
+
+    if keyword_score.max() > 0:
+        keyword_score = keyword_score / keyword_score.max()
+
+    return keyword_score, hits
+
+def find_highlights(final_scores, duration, top_n=5, clip_len=8, min_gap=12):
+    candidates = []
+    for i in range(len(final_scores) - clip_len):
+        candidates.append((np.mean(final_scores[i:i+clip_len]), i))
+
+    candidates.sort(reverse=True, key=lambda x: x[0])
+    picked = []
+    for score, start in candidates:
+        if all(abs(start - p[1]) >= min_gap for p in picked):
+            picked.append((score, start))
+        if len(picked) >= top_n:
+            break
+
+    picked.sort(key=lambda x: x[1])
+    return [(s, e, sc) for sc, s in [(sc, st) for sc, st in picked] for e in [s+clip_len]]
+
+def format_timestamp(seconds):
+    m, s = divmod(int(seconds), 60)
+    h, m = divmod(m, 60)
+    return f"{h:02d}:{m:02d}:{s:02d}" if h > 0 else f"{m:02d}:{s:02d}"
+
+def highlights(video_path, keywords=None, top_n=5, clip_len=8, min_gap=12):
+    if keywords is None:
+        keywords = DEFAULT_KEYWORDS
+
+    av_score, duration = get_av_scores(video_path, sample_fps=0.5)
+    segments = load_text_from_srt(video_path.replace('.mp4', '.srt')) 
+    kw_score, kw_hits = score_keywords(segments, duration, keywords)
+
+    final_score = av_score + (kw_score * 1.5)
+    final_score = (final_score - final_score.min()) / (final_score.max() - final_score.min() + 1e-6)
+    final_score = np.convolve(final_score, np.ones(3)/3, mode='same')
+
+    highlights = find_highlights(final_score, duration, top_n=top_n, clip_len=clip_len)
+
+    return highlights, kw_hits
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Find highlights with Whisper keywords + timestamps")
+    parser.add_argument("video", help="Input video file")
+    parser.add_argument("--keywords", type=str, default=",".join(DEFAULT_KEYWORDS), help="Comma-separated keywords to hunt for")
+    parser.add_argument("--model", type=str, default="base", help="Whisper model: tiny, base, small, medium")
+    parser.add_argument("--top", type=int, default=5)
+    parser.add_argument("--len", type=int, default=30, help="Highlight length in seconds")
+    parser.add_argument("--no-cut", action="store_true", help="Only print timestamps, don't cut video files")
+    args = parser.parse_args()
+
+    keywords = [k.strip() for k in args.keywords.split(",") if k.strip()]
+
+    av_score, duration = get_av_scores(args.video, sample_fps=0.5)
+    
+    segments = load_text_from_srt(args.video.replace('.mp4', '.srt')) 
+    kw_score, kw_hits = score_keywords(segments, duration, keywords)
+
+    # FINAL SCORE: AV + keyword boost (keyword is huge if found)
+    final_score = av_score + (kw_score * 1.5)
+    final_score = (final_score - final_score.min()) / (final_score.max() - final_score.min() + 1e-6)
+    final_score = np.convolve(final_score, np.ones(3)/3, mode='same') # smooth
+
+    highlights = find_highlights(final_score, duration, top_n=args.top, clip_len=args.len)
+
+    # --- OUTPUT ---
+    print("\n" + "="*60)
+    print("KEYWORD HITS:")
+    for h in kw_hits:
+        print(f" [{format_timestamp(h['timestamp'])}] ({h['keyword']}) -> {h['text']}")
+
+    print("\n" + "="*60)
+    print("FINAL HIGHLIGHTS:")
+    output_data = []
+    for i, (start, end, score) in enumerate(highlights, 1):
+        # find transcript in that window
+        texts_in_window = [s['text'].strip() for s in segments if not (s['end'] < start or s['start'] > end)]
+        transcript = " ".join(texts_in_window)[:120]
+
+        print(f"{i}. {format_timestamp(start)} - {format_timestamp(end)} (score {score:.2f}) | {transcript}")
+        output_data.append({
+            "id": i,
+            "start_sec": float(start),
+            "end_sec": float(end),
+            "start_timestamp": format_timestamp(start),
+            "end_timestamp": format_timestamp(end),
+            "score": float(score),
+            "transcript": transcript
+        })
+
+    # Save timestamps to JSON
+    with open("highlights.json", "w") as f:
+        json.dump({"video": args.video, "keywords": keywords, "highlights": output_data, "keyword_hits": kw_hits}, f, indent=2)
+    print(f"\nTimestamps saved to highlights.json")
+
+    # Cut videos if requested
+    #if not args.no_cut:
+    if args.no_cut:
+        print("\nCutting clips...")
+        Path("highlights").mkdir(exist_ok=True)
+        clip = VideoFileClip(args.video)
+        for h in output_data:
+            sub = clip.subclip(h['start_sec'], h['end_sec'])
+            out = f"highlights/highlight_{h['id']}_{h['start_timestamp'].replace(':','-')}.mp4"
+            sub.write_videofile(out, codec="libx264", audio_codec="aac", logger=None)
+        clip.close()
+        print("Done ->./highlights/")