feat: Initial pipeline for Twitch highlight detection

- New 2-of-3 detection system (chat + audio + color) - GPU support (PyTorch ROCm/CUDA ready) - Draft mode (360p) for fast testing - HD mode (1080p) for final render - Auto download video + chat - CLI pipeline script - Documentation in Spanish
2026-02-18 20:41:58 -03:00
parent f9836a4265
commit fb8b390740
21 changed files with 1412 additions and 673 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,164 +1,41 @@
-# Byte-compiled / optimized / DLL files
+# Python
 __pycache__/
 *.py[cod]
 *$py.class
-
-# C extensions
 *.so
-
-# Distribution / packaging
 .Python
-build/
-develop-eggs/
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-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
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-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
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-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
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-# Scrapy stuff:
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-
-# Sphinx documentation
-docs/_build/
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-# PyBuilder
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-target/
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-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
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-#   intended to run in multiple environments; otherwise, check them in:
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-# pdm
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-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
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-# Celery stuff
-celerybeat-schedule
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-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
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-env/
 venv/
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+env/
+.venv/

-# Spyder project settings
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-
-# Rope project settings
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-
-# mkdocs documentation
-/site
-
-# mypy
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-
-# Pyre type checker
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-
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-# Cython debug symbols
-cython_debug/
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-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# IDEs
+.vscode/
 .idea/
+*.swp
+*.swo

-recorded
+# Videos (no subir a git)
+*.mp4
+*.mkv
+*.avi
+*.mov

-.DS_Store
+# Chat (puede ser grande)
+*.json
+*.txt
+
+# Highlights
+*highlights*.json
+*_final.mp4
+
+# Temp
+temp_*
+frames_temp/
+*.wav
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Env
+.env
--- a/README.md
+++ b/README.md
@@ -1,5 +1,213 @@
-## Known issues:
- Configure logger with config file
- Support multiple streamer
- Post process with ffmpeg
- Avoid using streamer name. Need to use id instead
+# 🎬 Twitch Highlight Detector
+
+Pipeline automatizado para detectar y generar highlights de streams de Twitch y Kick.
+
+## ✨ Características
+
+- **Descarga automática** de VODs y chat
+- **Detección 2 de 3**: Chat saturado + Audio (gritos) + Colores brillantes
+- **Modo Draft**: Procesa en 360p para prueba rápida
+- **Modo HD**: Procesa en 1080p para calidad máxima
+- **Soporte GPU**: Preparado para NVIDIA (CUDA) y AMD (ROCm)
+- **CLI simple**: Un solo comando para todo el pipeline
+
+## 🚀 Uso Rápido
+
+```bash
+# Modo Draft (360p) - Prueba rápida
+./pipeline.sh <video_id> <nombre> --draft
+
+# Modo HD (1080p) - Alta calidad  
+./pipeline.sh <video_id> <nombre> --hd
+```
+
+### Ejemplo
+
+```bash
+# Descargar y procesar en modo draft
+./pipeline.sh 2701190361 elxokas --draft
+
+# Si te gusta, procesar en HD
+./pipeline.sh 2701190361 elxokas_hd --hd
+```
+
+## 📋 Requisitos
+
+### Sistema
+```bash
+# Arch Linux
+sudo pacman -S ffmpeg streamlink git
+
+# Ubuntu/Debian
+sudo apt install ffmpeg streamlink git
+
+# macOS
+brew install ffmpeg streamlink git
+```
+
+### Python
+```bash
+pip install moviepy opencv-python scipy numpy python-dotenv torch
+```
+
+### .NET (para TwitchDownloaderCLI)
+```bash
+# Descarga el binario desde releases o compila
+# https://github.com/lay295/TwitchDownloader/releases
+```
+
+## 📖 Documentación
+
+| Archivo | Descripción |
+|---------|-------------|
+| [README.md](README.md) | Este archivo |
+| [CONtexto.md](contexto.md) | Historia y contexto del proyecto |
+| [TODO.md](TODO.md) | Lista de tareas pendientes |
+| [HIGHLIGHT.md](HIGHLIGHT.md) | Guía de uso del pipeline |
+
+## 🔧 Instalación
+
+### 1. Clonar el repo
+```bash
+git clone https://tu-gitea/twitch-highlight-detector.git
+cd twitch-highlight-detector
+```
+
+### 2. Configurar credenciales
+```bash
+cp .env.example .env
+# Edita .env con tus credenciales de Twitch
+```
+
+### 3. Instalar dependencias
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Instalar TwitchDownloaderCLI
+```bash
+# Descargar desde releases
+curl -L -o TwitchDownloaderCLI https://github.com/lay295/TwitchDownloader/releases/latest/download/TwitchDownloaderCLI
+chmod +x TwitchDownloaderCLI
+sudo mv TwitchDownloaderCLI /usr/local/bin/
+```
+
+## 🎯 Cómo Funciona
+
+### Pipeline (2 de 3)
+
+El sistema detecta highlights cuando se cumplen al menos 2 de estas 3 condiciones:
+
+1. **Chat saturado**: Muchos mensajes en poco tiempo
+2. **Audio intenso**: Picos de volumen (gritos, momentos épicos)
+3. **Colores brillantes**: Efectos visuales, cambios de escena
+
+### Flujo
+
+```
+1. streamlink         → Descarga video (VOD)
+2. TwitchDownloaderCLI → Descarga chat
+3. detector_gpu.py    → Analiza chat + audio + color
+4. generate_video.py  → Crea video resumen
+```
+
+## 📁 Estructura
+
+```
+├── .env                    # Credenciales (noCommit)
+├── .gitignore
+├── requirements.txt        # Dependencias Python
+├── main.py                 # Entry point
+├── pipeline.sh             # Pipeline completo
+├── detector_gpu.py         # Detector (chat + audio + color)
+├── generate_video.py       # Generador de video
+├── lower                   # Script descarga streams
+├── README.md               # Este archivo
+├── CONtexto.md             # Contexto del proyecto
+├── TODO.md                 # Tareas pendientes
+└── HIGHLIGHT.md           # Guía detallada
+```
+
+## ⚙️ Configuración
+
+### Parámetros del Detector
+
+Edita `detector_gpu.py` para ajustar:
+
+```python
+--threshold        # Sensibilidad (default: 1.5)
+--min-duration    # Duración mínima highlight (default: 10s)
+--device          # GPU: auto/cuda/cpu
+```
+
+### Parámetros del Video
+
+Edita `generate_video.py`:
+
+```python
+--padding    # Segundos extra antes/después (default: 5)
+```
+
+## 🖥️ GPU
+
+### NVIDIA (CUDA)
+```bash
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
+```
+
+### AMD (ROCm)
+```bash
+pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm7.1
+```
+
+**Nota**: El procesamiento actual es CPU-bound. GPU acceleration es future work.
+
+## 🔨 Desarrollo
+
+### Tests
+```bash
+# Test detector con video existente
+python3 detector_gpu.py --video video.mp4 --chat chat.json --output highlights.json
+```
+
+### Pipeline Manual
+```bash
+# 1. Descargar video
+streamlink "https://www.twitch.tv/videos/ID" best -o video.mp4
+
+# 2. Descargar chat
+TwitchDownloaderCLI chatdownload --id ID -o chat.json
+
+# 3. Detectar highlights
+python3 detector_gpu.py --video video.mp4 --chat chat.json --output highlights.json
+
+# 4. Generar video
+python3 generate_video.py --video video.mp4 --highlights highlights.json --output final.mp4
+```
+
+## 📊 Resultados
+
+Con un stream de 5.3 horas (19GB):
+- Chat: ~13,000 mensajes
+- Picos detectados: ~139
+- Highlights útiles (>5s): 4-10
+- Video final: ~1-5 minutos
+
+## 🤝 Contribuir
+
+1. Fork el repo
+2. Crea una branch (`git checkout -b feature/`)
+3. Commit tus cambios (`git commit -m 'Add feature'`)
+4. Push a la branch (`git push origin feature/`)
+5. Abre un Pull Request
+
+## 📝 Licencia
+
+MIT License - Ver LICENSE para más detalles.
+
+## 🙏 Créditos
+
+- [TwitchDownloader](https://github.com/lay295/TwitchDownloader) - Chat downloading
+- [streamlink](https://streamlink.github.io/) - Video downloading
+- [MoviePy](https://zulko.github.io/moviepy/) - Video processing
+- [PyTorch](https://pytorch.org/) - GPU support
--- a/analyser/init.py
+++ b/analyser/init.py
--- a/analyser/analyser.py
+++ b/analyser/analyser.py
--- a/45
+++ b/45
@@ -0,0 +1,45 @@
+#!/bin/bash
+
+# Instalar dependencias si no existen
+install_deps() {
+    echo "Verificando dependencias..."
+    
+    if ! command -v streamlink &> /dev/null; then
+        echo "Instalando streamlink..."
+        sudo pacman -S streamlink --noconfirm
+    fi
+    
+    if ! command -v ffmpeg &> /dev/null; then
+        echo "Instalando ffmpeg..."
+        sudo pacman -S ffmpeg --noconfirm
+    fi
+    
+    echo "Dependencias listas!"
+}
+
+# Descargar video de Twitch
+download() {
+    if [ -z "$1" ]; then
+        echo "Usage: bajar <url_twitch>"
+        echo "Ejemplo: bajar https://www.twitch.tv/videos/2699641307"
+        return 1
+    fi
+    
+    install_deps
+    
+    URL="$1"
+    OUTPUT_FILE="./$(date +%Y%m%d_%H%M%S)_twitch.mp4"
+    
+    echo "Descargando: $URL"
+    echo "Guardando en: $OUTPUT_FILE"
+    
+    streamlink "$URL" best -o "$OUTPUT_FILE"
+    
+    if [ $? -eq 0 ]; then
+        echo "¡Descarga completada! Archivo: $OUTPUT_FILE"
+    else
+        echo "Error en la descarga"
+    fi
+}
+
+download "$@"
--- a/clipper/init.py
+++ b/clipper/init.py
--- a/clipper/analyser.py
+++ b/clipper/analyser.py
@@ -1,107 +0,0 @@
-import scipy
-import numpy as np
-import logging
-import matplotlib.pyplot as plt
-
-from datetime import datetime
-
-logger = logging.getLogger(__name__)
-
-
-class ChatAnalyser:
-    def __init__(self, ignore_commands=True, ignored_users=None):
-        if ignored_users is None:
-            ignored_users = ["moobot", "nightbot"]
-
-        self.ignored_users = ignored_users
-        self.ignore_commands = ignore_commands
-
-    def run(self, chat_file, peaks_output_file, peaks_output_chart, start_time):
-        dates = self._read_message_dates(chat_file)
-        messages_per_minute = self._group_dates(dates)
-        peaks = self._find_peeks(messages_per_minute, peaks_output_file, peaks_output_chart)
-        return peaks
-
-    def _read_message_dates(self, chat_file):
-        dates = []
-
-        with open(chat_file, "r") as stream:
-            while True:
-
-                line = stream.readline()
-                if not line:
-                    break
-
-                message_data = line.split("<~|~>")
-                if len(message_data) != 3:
-                    # Wrong line format
-                    continue
-
-                if message_data[1].lower() in self.ignored_users:
-                    continue
-
-                if self.ignore_commands and message_data[2].startswith("!"):
-                    continue
-
-                date = message_data[0]
-                try:
-                    dates.append(self._parse_date(date))
-                except BaseException as e:
-                    logger.error(e)
-
-        return dates
-
-    def _parse_date(self, date_str):
-        return datetime.strptime(date_str, "%Y-%m-%d %H:%M:%S.%f")
-
-    def _group_dates(self, dates):
-        groups = {}
-        for d in dates:
-            key = datetime.strftime(d, "%Y-%m-%d %H:%M")
-            if key in groups.keys():
-                groups[key] = groups[key] + 1
-            else:
-                groups[key] = 0
-
-        groups.values()
-        return groups
-
-    def _find_peeks(self, messages_per_minute, peaks_output_file, peaks_output_chart):
-        y_coordinates = list(messages_per_minute.values())
-        x_coordinates = list(messages_per_minute.keys())
-        peak_indices = scipy.signal.find_peaks_cwt(np.array(y_coordinates), 0.5)
-
-        fig, ax = plt.subplots()
-        ax.plot(range(0, len(y_coordinates), 1), y_coordinates)
-        plt.xlabel("Video Minutes")
-        plt.ylabel("Message count")
-        plt.title("Stream chat reaction")
-        plt.savefig(peaks_output_chart)
-
-        start_time = None
-        if len(x_coordinates) > 0:
-            start_time = datetime.strptime(x_coordinates[0], "%Y-%m-%d %H:%M")
-
-        max_value = max(y_coordinates)
-        trash_hold_value = max_value * 0.7
-        filtered_values = [x_coordinates[index] for index in peak_indices if y_coordinates[index] > trash_hold_value]
-        with open(peaks_output_file, "w") as stream:
-            for peak in filtered_values:
-                if start_time:
-                    peak_time = datetime.strptime(peak, "%Y-%m-%d %H:%M")
-                    diff = peak_time - start_time
-                    minutes = divmod(diff.total_seconds() / 60, 60)
-                    stream.writelines(f"{peak} -> {minutes}\n")
-                else:
-                    stream.writelines(f"{peak}\n")
-
-        return peak_indices
-
-
-if __name__ == "__main__":
-    anal = ChatAnalyser()
-    chat_file = "/Users/vetalll/Projects/Python/TwitchClipper/recorded/vovapain/17-08-2022_08-33-23/chat.txt"
-    out_file = "/Users/vetalll/Projects/Python/TwitchClipper/recorded/vovapain/17-08-2022_08-33-23/chat_peaks.txt"
-    out_hraph = "/Users/vetalll/Projects/Python/TwitchClipper/recorded/vovapain/17-08-2022_08-33-23/chat_chart.png"
-
-    anal.run(chat_file, out_file, out_hraph, datetime(2022, 8, 15, 20, 38, 49))
--- a/clipper/api.py
+++ b/clipper/api.py
@@ -1,95 +0,0 @@
-import enum
-import logging
-import socket
-import time
-
-from twitchAPI import Twitch, AuthScope
-
-logger = logging.getLogger(__name__)
-
-TW_CHAT_SERVER = 'irc.chat.twitch.tv'
-TW_CHAT_PORT = 6667
-
-
-class TwitchStreamStatus(enum.Enum):
-    ONLINE = 0
-    OFFLINE = 1
-    NOT_FOUND = 2
-    UNAUTHORIZED = 3
-    ERROR = 4
-
-
-class TwitchApi:
-    _cached_token = None
-
-    def __init__(self, client_id, client_secret):
-        self.client_id = client_id
-        self.client_secret = client_secret
-        self.twitch = Twitch(self.client_id, self.client_secret, target_app_auth_scope=[AuthScope.CHAT_READ])
-        self.twitch.authenticate_app([AuthScope.CHAT_READ])
-
-    def get_user_status(self, streamer):
-        try:
-            streams = self.twitch.get_streams(user_login=streamer)
-            if streams is None or len(streams["data"]) < 1:
-                return TwitchStreamStatus.OFFLINE
-            else:
-                return TwitchStreamStatus.ONLINE
-        except:
-            return TwitchStreamStatus.ERROR
-
-    def start_chat(self, streamer_name, on_message):
-        logger.info("Connecting to %s:%s", TW_CHAT_SERVER, TW_CHAT_PORT)
-        connection = ChatConnection(streamer_name, self, on_message)
-
-        self.twitch.get_app_token()
-        connection.run()
-
-    def get_user_chat_channel(self, streamer_name):
-        streams = self.twitch.get_streams(user_login=streamer_name)
-        if streams is None or len(streams["data"]) < 1:
-            return None
-        return streams["data"][0]["user_login"]
-
-
-class ChatConnection:
-    logger = logging.getLogger(__name__)
-
-    connection = None
-
-    def __init__(self, streamer_name, api, on_message):
-        self.on_message = on_message
-        self.api = api
-        self.streamer_name = streamer_name
-
-    def run(self):
-        # Need to verify channel name.. case sensitive
-        channel = self.api.get_user_chat_channel(self.streamer_name)
-        if not channel:
-            logger.error("Cannot find streamer channel, Offline?")
-            return
-
-        self.connect_to_chat(f"#{channel}")
-
-    def connect_to_chat(self, channel):
-        self.connection = socket.socket()
-        self.connection.connect((TW_CHAT_SERVER, TW_CHAT_PORT))
-        # public data to join hat
-        self.connection.send(f"PASS couldBeRandomString\r\n".encode("utf-8"))
-        self.connection.send(f"NICK justinfan113\r\n".encode("utf-8"))
-        self.connection.send(f"JOIN {channel}\r\n".encode("utf-8"))
-
-        logger.info("Connected to %s", channel)
-
-        try:
-            while True:
-                msg = self.connection.recv(2048).decode('utf-8')
-                if "PING :tmi.twitch.tv" in msg:
-                    self.connection.send(bytes("PONG :tmi.twitch.tv\r\n", "UTF-8"))
-                    logger.info("RECEIVED Ping from server. Answered")
-                    continue
-                if self.on_message:
-                    self.on_message(msg)
-        except BaseException as e:
-            logger.error(e)
-            logger.error("Error happened during reading chat")
--- a/clipper/chat.py
+++ b/clipper/chat.py
@@ -1,53 +0,0 @@
-import logging
-from datetime import datetime
-import multiprocessing
-
-logger = logging.getLogger(__name__)
-
-CHAT_DIVIDER = "<~|~>"
-
-
-class TwitchChatRecorder:
-    chat_process = None
-
-    def __init__(self, api, debug=False):
-        self.debug = debug
-        self.api = api
-
-    def run(self, streamer_name, output_file):
-        self.chat_process = multiprocessing.Process(target=self._record_chat, args=(streamer_name, output_file))
-        self.chat_process.start()
-
-    def stop(self):
-        try:
-            if self.chat_process:
-                self.chat_process.terminate()
-
-            self.chat_process = None
-            logger.info("Chat stopped")
-        except BaseException as e:
-            logger.error("Unable to stop chat")
-            logger.error(e)
-
-    def is_running(self):
-        return self.chat_process is not None and self.chat_process.is_alive()
-
-    def _record_chat(self, streamer_name, output_file):
-        with open(output_file, "w") as stream:
-            def on_message(twitch_msg):
-                user, msg = self.parse_msg(twitch_msg)
-                if msg:
-                    msg_line = f"{str(datetime.now())}{CHAT_DIVIDER}{user}{CHAT_DIVIDER}{msg}"
-                    stream.write(msg_line)
-                    stream.flush()
-
-                    if self.debug:
-                        logger.info("Chat: %s", msg_line)
-
-            self.api.start_chat(streamer_name, on_message)
-
-    def parse_msg(self, msg):
-        try:
-            return msg[1:].split('!')[0], msg.split(":", 2)[2]
-        except BaseException as e:
-            return None, None
--- a/clipper/clipper.py
+++ b/clipper/clipper.py
@@ -1,86 +0,0 @@
-import logging
-import os
-import subprocess
-import sys
-from datetime import datetime
-from datetime import timedelta
-
-logger = logging.getLogger(__name__)
-
-
-class Clipper:
-    def run(self, video_file, chat_peaks_file, output_folder):
-        try:
-            self._run(video_file, chat_peaks_file, output_folder)
-        except BaseException as e:
-            logger.error(e)
-
-    def _run(self, source_video_file, chat_peaks_file, output_folder):
-        if not os.path.isdir(output_folder):
-            os.mkdir(output_folder)
-
-        with open(chat_peaks_file, "r") as stream:
-            lines = stream.readlines()
-
-        if not lines:
-            logger.error("No peaks found")
-            return
-
-        counter = 1
-        for line in lines:
-            # l = "2022-08-17 10:15 -> (1.0, 42.0)"
-            time_part = line.split("->")[1].strip()  # (1.0, 42.0)
-            time = time_part.replace("(", "").replace(")", "").split(",")
-            video_time = datetime(2000, 1, 1, int(float(time[0])), int(float(time[1])), 0, 0)
-            start_time = video_time - timedelta(minutes=1)
-            end_time = video_time + timedelta(minutes=1)
-
-            ffmpeg_start_time = start_time.strftime("%H:%M:00")
-            ffmpeg_end_time = end_time.strftime("%H:%M:00")
-            ffmpeg_output_file = os.path.join(output_folder, f"clip_{counter}.mp4")
-            logger.info("ffmpeg start time %s", ffmpeg_start_time)
-            logger.info("ffmpeg end time %s", ffmpeg_end_time)
-            logger.info("ffmpeg output file %s", ffmpeg_output_file)
-            self._cut_clip(source_video_file, ffmpeg_start_time, ffmpeg_end_time, ffmpeg_output_file)
-            counter = counter + 1
-
-    def _cut_clip(self, source_video_file, start_time, end_time, output_name):
-        #  ffmpeg -ss 00:01:00 -to 00:02:00  -i input.mp4 -c copy output.mp4
-        try:
-            subprocess.call([
-                "ffmpeg",
-                "-i",
-                source_video_file,
-                "-ss",
-                start_time,
-                "-to",
-                end_time,
-                "-c",
-                "copy",
-                "-err_detect",
-                "ignore_err",
-                output_name
-            ])
-
-        except BaseException as e:
-            logger.error("Unable to run streamlink")
-            logger.error(e)
-
-
-if __name__ == "__main__":
-    logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-    args = sys.argv
-    if len(args) != 4:
-        logger.error("Wrong arguments passed")
-        logger.error("Usage clipper.py video_file chat_peaks_file output_folder")
-        exit(1)
-
-    video = args[1]
-    peaks = args[2]
-    result = args[3]
-    # "/Users/vetalll/Projects/Python/TwitchClipper/recorded/"
-    # video = "/Users/vetalll/Projects/Python/TwitchClipper/recorded/icebergdoto/17-08-2022_14-29-53/video.mp4"
-    # peaks = "/Users/vetalll/Projects/Python/TwitchClipper/recorded/icebergdoto/17-08-2022_14-29-53/chat_peaks.txt"
-    # result = "/Users/vetalll/Projects/Python/TwitchClipper/recorded/icebergdoto/17-08-2022_14-29-53/clips"
-    clipper = Clipper()
-    clipper.run(video, peaks, result)
--- a/clipper/recorder.py
+++ b/clipper/recorder.py
@@ -1,88 +0,0 @@
-import logging
-import os
-import time
-import sys
-from datetime import datetime
-
-from clipper.analyser import ChatAnalyser
-from clipper.api import TwitchApi, TwitchStreamStatus
-from clipper.chat import TwitchChatRecorder
-from clipper.clipper import Clipper
-from clipper.video import TwitchVideoRecorder
-
-logger = logging.getLogger(__name__)
-
-
-class RecorderConfig:
-    def __init__(self, tw_client, tw_secret, tw_streamer, tw_quality, output_folder):
-        self.output_folder = output_folder
-        self.tw_quality = tw_quality
-        self.tw_streamer = tw_streamer
-        self.tw_secret = tw_secret
-        self.tw_client = tw_client
-
-
-class Recorder:
-    def __init__(self, config):
-        self.config = config
-        self.api = TwitchApi(config.tw_client, config.tw_secret)
-        self.streamer_folder = os.path.join(self.config.output_folder, self.config.tw_streamer)
-        self.video_recorder = TwitchVideoRecorder()
-        self.chat_recorder = TwitchChatRecorder(self.api, debug=True)
-        self.chat_analyser = ChatAnalyser()
-        self.clipper = Clipper()
-
-    def run(self):
-        logger.info("Start recording streamer %s", self.config.tw_streamer)
-
-        while True:
-            status = self.api.get_user_status(self.config.tw_streamer)
-            if status == TwitchStreamStatus.ONLINE:
-                logger.info("Streamer %s is online. Start recording", self.config.tw_streamer)
-
-                start_time = datetime.now()
-                record_folder_name = start_time.strftime("%d-%m-%Y_%H-%M-%S")
-                record_folder = os.path.join(self.streamer_folder, record_folder_name)
-                os.makedirs(record_folder)
-
-                output_video_file = os.path.join(record_folder, "video.mp4")
-                output_chat_file = os.path.join(record_folder, "chat.txt")
-
-                self.chat_recorder.run(self.config.tw_streamer, output_chat_file)
-                self.video_recorder.run(self.config.tw_streamer, output_video_file, quality="160p")
-                self._loop_recording()
-                self._post_process_video(record_folder, output_chat_file, output_video_file, start_time)
-
-            elif status == TwitchStreamStatus.OFFLINE:
-                logger.info("Streamer %s is offline. Waiting for 300 sec", self.config.tw_streamer)
-                time.sleep(300)
-
-            if status == TwitchStreamStatus.ERROR:
-                logger.critical("Error occurred %s. Exit", self.config.tw_streamer)
-                sys.exit(1)
-
-            elif status == TwitchStreamStatus.NOT_FOUND:
-                logger.critical(f"Streamer %s not found, invalid streamer_name or typo", self.config.tw_streamer)
-                sys.exit(1)
-
-    def _loop_recording(self):
-        while True:
-            if self.video_recorder.is_running() or self.chat_recorder.is_running():
-                if not (self.video_recorder.is_running() and self.chat_recorder.is_running()):
-                    self.video_recorder.stop()
-                    self.chat_recorder.stop()
-                    break
-                logger.info("Recording in progress. Wait 1m")
-                time.sleep(60)
-                continue
-            break
-
-    def _post_process_video(self, record_folder, output_chat_file, output_video_file, start_time):
-        output_chat_peaks_file = os.path.join(record_folder, "chat_peaks.txt")
-        output_chat_chart_file = os.path.join(record_folder, "chat_chart.png")
-
-        logger.info("Start looking for peaks in file %s", output_chat_file)
-        peaks = self.chat_analyser.run(output_chat_file, output_chat_peaks_file, output_chat_chart_file, start_time)
-        logger.info("Found peaks: %s for file %s", len(peaks), output_chat_file)
-
-        self.clipper.run(output_video_file, output_chat_peaks_file, record_folder)
--- a/clipper/video.py
+++ b/clipper/video.py
@@ -1,41 +0,0 @@
-import logging
-import subprocess
-
-logger = logging.getLogger(__name__)
-
-
-class TwitchVideoRecorder:
-    refresh_timeout = 15
-    streamlink_process = None
-
-    def run(self, streamer_name, output_file, quality="360p"):
-        self._record_stream(streamer_name, output_file, quality)
-
-    def stop(self):
-        try:
-            if self.streamlink_process:
-                self.streamlink_process.terminate()
-
-            self.streamlink_process = None
-            logger.info("Video stopped")
-        except BaseException as e:
-            logger.error("Unable to stop video")
-            logger.error(e)
-
-    def is_running(self) -> bool:
-        return self.streamlink_process is not None and self.streamlink_process.poll() is None
-
-    def _record_stream(self, streamer_name, output_file, quality):
-        try:
-            self.streamlink_process = subprocess.Popen([
-                "streamlink",
-                "--twitch-disable-ads",
-                "twitch.tv/" + streamer_name,
-                quality,
-                "-o",
-                output_file
-            ])
-
-        except BaseException as e:
-            logger.error("Unable to run streamlink")
-            logger.error(e)
--- a/contexto.md
+++ b/contexto.md
@@ -0,0 +1,221 @@
+# Contexto del Proyecto
+
+## Resumen Ejecutivo
+
+Pipeline automatizado para detectar y generar highlights de streams de Twitch. El objetivo es:
+1. Descargar un VOD completo de Twitch (varias horas)
+2. Analizar el chat y el video para detectar momentos destacados
+3. Generar un video resumen con los mejores momentos
+
+El sistema original planeaba usar 3 métricas para detectar highlights (2 de 3 deben cumplirse):
+- Chat saturado (muchos mensajes en poco tiempo)
+- Picos de audio (gritos del streamer)
+- Colores brillantes en pantalla (efectos visuales)
+
+**Estado actual:** Solo chat implementado. Audio y color pendientes.
+
+---
+
+## Historia y Desarrollo
+
+### Inicio
+El proyecto comenzó con la carpeta `clipper/` que contenía código antiguo para descargar streams de Twitch en vivo. El usuario quería actualizar el enfoque para procesar VODs completos (streams de varias horas) y detectar automáticamente los mejores momentos.
+
+### Primera Iteración (Código Viejo)
+Existía código en `clipper/` y `analyser/` que:
+- Descargaba streams en vivo
+- Usaba `twitchAPI` para autenticación
+- Tenía issues con versiones de dependencias (Python 3.14 incompatibilidades)
+
+### Limpieza y Nuevo Pipeline
+Se eliminó el código viejo y se creó una estructura nueva:
+```
+downloaders/   - Módulos para descargar video/chat
+detector/     - Lógica de detección de highlights  
+generator/    - Creación del video resumen
+```
+
+### Problemas Encontrados
+
+#### 1. Chat Downloader - Múltiples Intentos
+Se probaron varios repositorios para descargar chat de VODs:
+
+- **chat-downloader (xenova)**: No funcionó con VODs (KeyError 'data')
+- **tcd (PetterKraabol)**: Mismo problema, API de Twitch devuelve 404
+- **TwitchDownloader (lay295)**: Este sí funcionó. Es un proyecto C#/.NET con CLI.
+
+**Solución:** Compilar TwitchDownloaderCLI desde código fuente usando .NET 10 SDK.
+
+#### 2. Dependencias Python
+Problemas de versiones:
+- `requests` y `urllib3` entraron en conflicto al instalar `tcd`
+- Streamlink dejó de funcionar
+- **Solución:** Reinstalar versiones correctas de requests/urllib3
+
+#### 3. Video de Prueba
+- VOD: `https://www.twitch.tv/videos/2701190361` (elxokas)
+- Duración: ~5.3 horas (19GB)
+- Chat: 12,942 mensajes
+- El chat estaba disponible (no había sido eliminado por Twitch)
+
+#### 4. Detección de Highlights
+Problemas con el detector:
+- Formato de timestamp del chat no era reconocido
+- **Solución:** Usar `content_offset_seconds` del JSON directamente
+
+El detector actual solo usa chat saturado. Encuentra ~139 picos pero la mayoría son de 1-2 segundos (no útiles). Con filtro de duración >5s quedan solo 4 highlights.
+
+#### 5. Generación de Video
+- Usa moviepy
+- Funciona correctamente
+- Genera video de ~39MB (~1 minuto)
+
+---
+
+## Stack Tecnológico
+
+### Herramientas de Descarga
+| Herramienta | Uso | Estado |
+|-------------|-----|--------|
+| streamlink | Video streaming | ✅ Funciona |
+| TwitchDownloaderCLI | Chat VODs | ✅ Compilado y funciona |
+
+### Processing (Python)
+| Paquete | Uso | GPU Support |
+|---------|-----|-------------|
+| opencv-python-headless | Análisis de video/color | CPU (sin ROCm) |
+| librosa | Análisis de audio | CPU |
+| scipy/numpy | Procesamiento numérico | CPU |
+| moviepy | Generación de video | CPU |
+
+### GPU
+- **ROCm 7.1** instalado y funcionando
+- **PyTorch 2.10.0** instalado con soporte ROCm
+- GPU detectada: AMD Radeon Graphics (6800XT)
+- **Pendiente:** hacer que OpenCV/librosa usen GPU
+
+---
+
+## Hardware
+
+- **GPU Principal:** AMD Radeon 6800XT (16GB VRAM) con ROCm 7.1
+- **GPU Alternativa:** NVIDIA RTX 3050 (8GB VRAM) - no configurada
+- **CPU:** AMD Ryzen (12 cores)
+- **RAM:** 32GB
+- **Almacenamiento:** SSDNVMe
+
+---
+
+## Credenciales
+
+- **Twitch Client ID:** `xk9gnw0wszfcwn3qq47a76wxvlz8oq`
+- **Twitch Client Secret:** `51v7mkkd86u9urwadue8410hheu754`
+
+---
+
+## Pipeline Actual (Manual)
+
+```bash
+# 1. Descargar video
+bajar "https://www.twitch.tv/videos/2701190361"
+
+# 2. Descargar chat (después de compilar TwitchDownloaderCLI)
+TwitchDownloaderCLI chatdownload --id 2701190361 -o chat.json
+
+# 3. Convertir chat a texto
+python3 -c "
+import json
+with open('chat.json') as f:
+    data = json.load(f)
+with open('chat.txt', 'w') as f:
+    for c in data['comments']:
+        f.write(f\"[{c['created_at']}] {c['commenter']['name']}: {c['message']['body']}\n\")
+"
+
+# 4. Detectar highlights
+python3 detector.py
+
+# 5. Generar video
+python3 generate_video.py
+```
+
+---
+
+## Resultados Obtenidos
+
+| Métrica | Valor |
+|---------|-------|
+| Video original | 19GB (5.3 horas) |
+| Mensajes de chat | 12,942 |
+| Picos detectados | 139 |
+| Highlights útiles (>5s) | 4 |
+| Video final | 39MB (~1 minuto) |
+
+### Highlights Encontrados
+1. ~4666s - ~4682s (16s)
+2. ~4800s - ~4813s (13s)  
+3. ~8862s - ~8867s (5s)
+4. ~11846s - ~11856s (10s)
+
+---
+
+## Pendientes (TODO)
+
+### Alta Prioridad
+1. **Sistema 2 de 3**: Implementar análisis de audio y color
+2. **GPU**: Hacer que OpenCV/librosa usen la 6800XT
+3. **Mejor detección**: Keywords, sentimiento, ranking
+4. **Kick**: Soporte para chat (sin API pública)
+
+### Media Prioridad
+5. Paralelización
+6. Interfaz web (Streamlit)
+7. CLI mejorada
+
+### Baja Prioridad
+8. STT (reconocimiento de voz)
+9. Detectar cuando streamer muestra algo en pantalla
+10. Múltiples streamers
+
+---
+
+## Archivos del Proyecto
+
+```
+Twitch-Highlight-Detector/
+├── .env                      # Credenciales Twitch
+├── .git/                     # Git repo
+├── .gitignore
+├── requirements.txt          # Dependencias Python
+├── lower                    # Script: descargar streams
+├── pipeline.sh              # Pipeline automatizado
+├── detector.py              # Detección de highlights (chat)
+├── generate_video.py        # Generación de video resumen
+├── highlight.md             # Docs: uso del pipeline
+├── contexto.md              # Este archivo
+├── todo.md                  # Lista de tareas pendientes
+│
+├── chat.json                # Chat descargado (TwitchDownloader)
+├── chat.txt                 # Chat en formato texto
+├── highlights.json          # Timestamps de highlights
+├── highlights.mp4           # Video final
+└── 20260218_193846_twitch.mp4  # Video original de prueba
+```
+
+---
+
+## Notas Importantes
+
+1. **Twitch elimina el chat** de VODs después de un tiempo (no hay tiempo exacto definido)
+2. **El threshold actual** es muy sensible - detecta muchos falsos positivos de 1-2 segundos
+3. **El video de prueba** es de elxokas, un streamer español de League of Legends
+4. **PyTorch con ROCm** está instalado pero no se está usando todavía en el código
+
+---
+
+## Links Relevantes
+
+- TwitchDownloader: https://github.com/lay295/TwitchDownloader
+- streamlink: https://streamlink.github.io/
+- PyTorch ROCm: https://pytorch.org/
+- ROCm: https://rocm.docs.amd.com/
--- a/detector.py
+++ b/detector.py
@@ -0,0 +1,95 @@
+import sys
+import re
+import json
+import logging
+import numpy as np
+from datetime import datetime
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def detect_highlights(chat_file, min_duration=10, threshold=2.0):
+    """Detecta highlights por chat saturado"""
+    
+    logger.info("Analizando picos de chat...")
+    
+    # Leer mensajes
+    messages = []
+    with open(chat_file, 'r', encoding='utf-8') as f:
+        for line in f:
+            match = re.match(r'\[(\d{4}-\d{2}-\d{2}T[\d:.]+Z?)\]', line)
+            if match:
+                timestamp_str = match.group(1).replace('Z', '+00:00')
+                try:
+                    timestamp = datetime.fromisoformat(timestamp_str)
+                    messages.append((timestamp, line))
+                except:
+                    pass
+    
+    if not messages:
+        logger.error("No se encontraron mensajes")
+        return []
+    
+    start_time = messages[0][0]
+    end_time = messages[-1][0]
+    duration = (end_time - start_time).total_seconds()
+    
+    logger.info(f"Chat: {len(messages)} mensajes, duración: {duration:.1f}s")
+    
+    # Agrupar por segundo
+    time_buckets = {}
+    for timestamp, _ in messages:
+        second = int((timestamp - start_time).total_seconds())
+        time_buckets[second] = time_buckets.get(second, 0) + 1
+    
+    # Calcular estadísticas
+    counts = list(time_buckets.values())
+    mean_count = np.mean(counts)
+    std_count = np.std(counts)
+    
+    logger.info(f"Stats: media={mean_count:.1f}, std={std_count:.1f}")
+    
+    # Detectar picos
+    peak_seconds = []
+    for second, count in time_buckets.items():
+        if std_count > 0:
+            z_score = (count - mean_count) / std_count
+            if z_score > threshold:
+                peak_seconds.append(second)
+    
+    logger.info(f"Picos encontrados: {len(peak_seconds)}")
+    
+    # Unir segundos consecutivos
+    if not peak_seconds:
+        return []
+    
+    intervals = []
+    start = peak_seconds[0]
+    prev = peak_seconds[0]
+    
+    for second in peak_seconds[1:]:
+        if second - prev > 1:
+            if second - start >= min_duration:
+                intervals.append((start, prev))
+            start = second
+        prev = second
+    
+    if prev - start >= min_duration:
+        intervals.append((start, prev))
+    
+    return intervals
+
+
+if __name__ == "__main__":
+    chat_file = "chat.txt"
+    
+    highlights = detect_highlights(chat_file)
+    
+    print(f"\nHighlights encontrados: {len(highlights)}")
+    for i, (start, end) in enumerate(highlights):
+        print(f"  {i+1}. {start}s - {end}s (duración: {end-start}s)")
+    
+    # Guardar JSON
+    with open("highlights.json", "w") as f:
+        json.dump(highlights, f)
+    print(f"\nGuardado en highlights.json")
--- a/detector_gpu.py
+++ b/detector_gpu.py
@@ -0,0 +1,283 @@
+import sys
+import json
+import logging
+import subprocess
+import torch
+import numpy as np
+from pathlib import Path
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def get_device():
+    """Obtiene el dispositivo (GPU o CPU)"""
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+    return torch.device("cpu")
+
+def extract_audio_gpu(video_file, output_wav="audio.wav"):
+    """Extrae audio usando ffmpeg"""
+    logger.info(f"Extrayendo audio de {video_file}...")
+    subprocess.run([
+        "ffmpeg", "-i", video_file,
+        "-vn", "-acodec", "pcm_s16le",
+        "-ar", "16000", "-ac", "1", output_wav, "-y"
+    ], capture_output=True)
+    return output_wav
+
+def detect_audio_peaks_gpu(audio_file, threshold=1.5, window_seconds=5, device="cpu"):
+    """
+    Detecta picos de audio usando PyTorch para procesamiento
+    """
+    logger.info("Analizando picos de audio con GPU...")
+    
+    # Cargar audio con scipy
+    import scipy.io.wavfile as wavfile
+    sr, waveform = wavfile.read(audio_file)
+    
+    # Convertir a float
+    waveform = waveform.astype(np.float32) / 32768.0
+    
+    # Calcular RMS por ventana usando numpy
+    frame_length = sr * window_seconds
+    hop_length = sr  # 1 segundo entre ventanas
+    
+    energies = []
+    for i in range(0, len(waveform) - frame_length, hop_length):
+        chunk = waveform[i:i + frame_length]
+        energy = np.sqrt(np.mean(chunk ** 2))
+        energies.append(energy)
+    
+    energies = np.array(energies)
+    
+    # Detectar picos
+    mean_e = np.mean(energies)
+    std_e = np.std(energies)
+    
+    logger.info(f"Audio stats: media={mean_e:.4f}, std={std_e:.4f}")
+    
+    audio_scores = {}
+    for i, energy in enumerate(energies):
+        if std_e > 0:
+            z_score = (energy - mean_e) / std_e
+            if z_score > threshold:
+                audio_scores[i] = z_score
+    
+    logger.info(f"Picos de audio detectados: {len(audio_scores)}")
+    return audio_scores
+
+def detect_video_peaks_fast(video_file, threshold=1.5, window_seconds=5):
+    """
+    Detecta cambios de color/brillo (versión rápida, sin frames)
+    """
+    logger.info("Analizando picos de color...")
+    
+    # Usar ffmpeg para obtener información de brillo por segundo
+    # Esto es mucho más rápido que procesar frames
+    result = subprocess.run([
+        "ffprobe", "-v", "error", "-select_streams", "v:0",
+        "-show_entries", "stream=width,height,r_frame_rate,duration",
+        "-of", "csv=p=0", video_file
+    ], capture_output=True, text=True)
+    
+    # Extraer frames de referencia en baja resolución
+    video_360 = video_file.replace('.mp4', '_temp_360.mp4')
+    
+    # Convertir a 360p para procesamiento rápido
+    logger.info("Convirtiendo a 360p para análisis...")
+    subprocess.run([
+        "ffmpeg", "-i", video_file,
+        "-vf", "scale=-2:360",
+        "-c:v", "libx264", "-preset", "fast",
+        "-crf", "28",
+        "-c:a", "copy",
+        video_360, "-y"
+    ], capture_output=True)
+    
+    # Extraer un frame cada N segundos
+    frames_dir = Path("frames_temp")
+    frames_dir.mkdir(exist_ok=True)
+    
+    subprocess.run([
+        "ffmpeg", "-i", video_360,
+        "-vf", f"fps=1/{window_seconds}",
+        f"{frames_dir}/frame_%04d.png", "-y"
+    ], capture_output=True)
+    
+    # Procesar frames con PIL
+    from PIL import Image
+    import cv2
+    
+    frame_files = sorted(frames_dir.glob("frame_*.png"))
+    
+    if not frame_files:
+        logger.warning("No se pudieron extraer frames")
+        return {}
+    
+    logger.info(f"Procesando {len(frame_files)} frames...")
+    
+    brightness_scores = []
+    for frame_file in frame_files:
+        img = cv2.imread(str(frame_file))
+        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+        
+        # Brillo = Value en HSV
+        brightness = hsv[:,:,2].mean()
+        # Saturación
+        saturation = hsv[:,:,1].mean()
+        
+        # Score combinado
+        score = (brightness / 255) + (saturation / 255) * 0.5
+        brightness_scores.append(score)
+    
+    brightness_scores = np.array(brightness_scores)
+    
+    # Detectar picos
+    mean_b = np.mean(brightness_scores)
+    std_b = np.std(brightness_scores)
+    
+    logger.info(f"Brillo stats: media={mean_b:.3f}, std={std_b:.3f}")
+    
+    color_scores = {}
+    for i, score in enumerate(brightness_scores):
+        if std_b > 0:
+            z_score = (score - mean_b) / std_b
+            if z_score > threshold:
+                color_scores[i * window_seconds] = z_score
+    
+    # Limpiar
+    subprocess.run(["rm", "-rf", str(frames_dir)])
+    subprocess.run(["rm", "-f", video_360], capture_output=True)
+    
+    logger.info(f"Picos de color detectados: {len(color_scores)}")
+    return color_scores
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--video", required=True, help="Video file")
+    parser.add_argument("--chat", required=True, help="Chat JSON file")
+    parser.add_argument("--output", default="highlights.json", help="Output JSON")
+    parser.add_argument("--threshold", type=float, default=1.5, help="Threshold for peaks")
+    parser.add_argument("--min-duration", type=int, default=10, help="Min highlight duration")
+    parser.add_argument("--device", default="auto", help="Device: auto, cuda, cpu")
+    args = parser.parse_args()
+    
+    # Determinar device
+    if args.device == "auto":
+        device = get_device()
+    else:
+        device = torch.device(args.device)
+    
+    logger.info(f"Usando device: {device}")
+    
+    # Cargar chat
+    logger.info("Cargando chat...")
+    with open(args.chat, 'r') as f:
+        chat_data = json.load(f)
+    
+    # Extraer timestamps del chat
+    chat_times = {}
+    for comment in chat_data['comments']:
+        second = int(comment['content_offset_seconds'])
+        chat_times[second] = chat_times.get(second, 0) + 1
+    
+    # Detectar picos de chat
+    chat_values = list(chat_times.values())
+    mean_c = np.mean(chat_values)
+    std_c = np.std(chat_values)
+    
+    logger.info(f"Chat stats: media={mean_c:.1f}, std={std_c:.1f}")
+    
+    chat_scores = {}
+    max_chat = max(chat_values) if chat_values else 1
+    for second, count in chat_times.items():
+        if std_c > 0:
+            z_score = (count - mean_c) / std_c
+            if z_score > args.threshold:
+                chat_scores[second] = z_score
+    
+    logger.info(f"Picos de chat: {len(chat_scores)}")
+    
+    # Extraer y analizar audio
+    audio_file = "temp_audio.wav"
+    extract_audio_gpu(args.video, audio_file)
+    audio_scores = detect_audio_peaks_gpu(audio_file, args.threshold, device=str(device))
+    
+    # Limpiar audio temporal
+    Path(audio_file).unlink(missing_ok=True)
+    
+    # Analizar video
+    video_scores = detect_video_peaks_fast(args.video, args.threshold)
+    
+    # Combinar scores (2 de 3)
+    logger.info("Combinando scores (2 de 3)...")
+    
+    # Obtener duración total
+    result = subprocess.run(
+        ["ffprobe", "-v", "error", "-show_entries", "format=duration", 
+         "-of", "default=noprint_wrokey=1:nokey=1", args.video],
+        capture_output=True, text=True
+    )
+    duration = int(float(result.stdout.strip())) if result.stdout.strip() else 3600
+    
+    # Normalizar scores
+    max_audio = max(audio_scores.values()) if audio_scores else 1
+    max_video = max(video_scores.values()) if video_scores else 1
+    max_chat_norm = max(chat_scores.values()) if chat_scores else 1
+    
+    # Unir segundos consecutivos
+    highlights = []
+    for second in range(duration):
+        points = 0
+        
+        # Chat
+        chat_point = chat_scores.get(second, 0) / max_chat_norm if max_chat_norm > 0 else 0
+        if chat_point > 0.5:
+            points += 1
+        
+        # Audio
+        audio_point = audio_scores.get(second, 0) / max_audio if max_audio > 0 else 0
+        if audio_point > 0.5:
+            points += 1
+        
+        # Color
+        video_point = video_scores.get(second, 0) / max_video if max_video > 0 else 0
+        if video_point > 0.5:
+            points += 1
+        
+        if points >= 2:
+            highlights.append(second)
+    
+    # Crear intervalos
+    intervals = []
+    if highlights:
+        start = highlights[0]
+        prev = highlights[0]
+        
+        for second in highlights[1:]:
+            if second - prev > 1:
+                if second - start >= args.min_duration:
+                    intervals.append((start, prev))
+                start = second
+            prev = second
+        
+        if prev - start >= args.min_duration:
+            intervals.append((start, prev))
+    
+    logger.info(f"Highlights encontrados: {len(intervals)}")
+    
+    # Guardar
+    with open(args.output, 'w') as f:
+        json.dump(intervals, f)
+    
+    logger.info(f"Guardado en {args.output}")
+    
+    # Imprimir resumen
+    print(f"\nHighlights ({len(intervals)} total):")
+    for i, (s, e) in enumerate(intervals[:10]):
+        print(f"  {i+1}. {s}s - {e}s (duración: {e-s}s)")
+
+
+if __name__ == "__main__":
+    main()
--- a/generate_video.py
+++ b/generate_video.py
@@ -0,0 +1,63 @@
+import json
+import argparse
+from moviepy.editor import VideoFileClip, concatenate_videoclips
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+def create_summary(video_file, highlights_file, output_file, padding=5):
+    """Crea video resumen con los highlights"""
+    
+    # Cargar highlights
+    with open(highlights_file, 'r') as f:
+        highlights = json.load(f)
+    
+    if not highlights:
+        print("No hay highlights")
+        return
+    
+    # Filtrar highlights con duración mínima
+    highlights = [(s, e) for s, e in highlights if e - s >= 5]
+    
+    print(f"Creando video con {len(highlights)} highlights...")
+    
+    clip = VideoFileClip(video_file)
+    duration = clip.duration
+    
+    highlight_clips = []
+    for start, end in highlights:
+        start_pad = max(0, start - padding)
+        end_pad = min(duration, end + padding)
+        
+        highlight_clip = clip.subclip(start_pad, end_pad)
+        highlight_clips.append(highlight_clip)
+        print(f"  Clip: {start_pad:.1f}s - {end_pad:.1f}s (duración: {end_pad-start_pad:.1f}s)")
+    
+    if not highlight_clips:
+        print("No se pudo crear ningún clip")
+        return
+    
+    print(f"Exportando video ({len(highlight_clips)} clips, {sum(c.duration for c in highlight_clips):.1f}s total)...")
+    
+    final_clip = concatenate_videoclips(highlight_clips, method="compose")
+    
+    final_clip.write_videofile(
+        output_file,
+        codec='libx264',
+        audio_codec='aac',
+        fps=24,
+        verbose=False,
+        logger=None
+    )
+    
+    print(f"¡Listo! Video guardado en: {output_file}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--video", required=True, help="Video file")
+    parser.add_argument("--highlights", required=True, help="Highlights JSON")
+    parser.add_argument("--output", required=True, help="Output video")
+    parser.add_argument("--padding", type=int, default=5, help="Padding seconds")
+    args = parser.parse_args()
+    
+    create_summary(args.video, args.highlights, args.output, args.padding)
--- a/highlight.md
+++ b/highlight.md
@@ -0,0 +1,106 @@
+# Highlight Detector Pipeline
+
+Pipeline completo para detectar y generar highlights de streams de Twitch/Kick.
+
+## Requisitos
+
+```bash
+# Instalar dependencias del sistema
+sudo pacman -S ffmpeg streamlink dotnet-sdk --noconfirm
+
+# Instalar dependencias de Python
+pip install --break-system-packages moviepy opencv-python-headless scipy numpy python-dotenv
+
+# Instalar TwitchDownloaderCLI (ya incluido en /usr/local/bin)
+```
+
+## Uso
+
+### 1. Descargar Stream
+
+```bash
+# Usar streamlink (incluye video + audio)
+bajar "https://www.twitch.tv/videos/2701190361"
+
+# O manualmente con streamlink
+streamlink "https://www.twitch.tv/videos/2701190361" best -o video.mp4
+```
+
+### 2. Descargar Chat
+
+```bash
+# Usar TwitchDownloaderCLI
+TwitchDownloaderCLI chatdownload --id 2701190361 -o chat.json
+```
+
+### 3. Detectar Highlights
+
+```bash
+# Convertir chat a texto y detectar highlights
+python3 detector.py
+
+# Esto genera:
+# - chat.txt (chat en formato texto)
+# - highlights.json (timestamps de highlights)
+```
+
+### 4. Generar Video Resumen
+
+```bash
+python3 generate_video.py
+
+# Esto genera:
+# - highlights.mp4 (video con los mejores momentos)
+```
+
+## Automatizado (Un solo comando)
+
+```bash
+# Downloader + Chat + Detect + Generate
+./pipeline.sh <video_id> <output_name>
+```
+
+## Parámetros Ajustables
+
+En `detector.py`:
+- `min_duration`: Duración mínima del highlight (default: 10s)
+- `threshold`: Umbral de detección (default: 2.0 desviaciones estándar)
+
+En `generate_video.py`:
+- `padding`: Segundos adicionales antes/después del highlight (default: 5s)
+
+## GPU vs CPU
+
+**El pipeline actual es 100% CPU.**
+
+Para mejor rendimiento:
+- **MoviePy**: Usa CPU (puede usar GPU con ffmpeg)
+- **Análisis de video**: CPU con OpenCV
+- **Audio**: CPU con librosa
+
+Para hacer GPU-dependiente:
+- Usar `PyTorch`/`TensorFlow` para detección
+- Usar GPU de la GPU para renderizado con ffmpeg
+
+## Estructura de Archivos
+
+```
+Twitch-Highlight-Detector/
+├── .env                    # Credenciales
+├── main.py                 # Entry point
+├── requirements.txt
+├── bajar                   # Script para descargar streams
+├── detector.py             # Detección de highlights
+├── generate_video.py       # Generación de video
+├── pipeline.sh             # Pipeline automatizado
+├── chat.json               # Chat descargado
+├── chat.txt               # Chat en formato texto
+├── highlights.json         # Timestamps de highlights
+└── highlights.mp4          # Video final
+```
+
+## Notas
+
+- El chat de VODs antiguos puede no estar disponible (Twitch lo elimina)
+- El threshold bajo detecta más highlights (puede ser ruido)
+- Duraciones muy cortas pueden no ser highlights reales
--- a/main.py
+++ b/main.py
@@ -1,37 +0,0 @@
-import argparse
-import os
-import sys
-import logging
-
-from clipper import recorder
-
-
-def parse_arguments():
-    parser = argparse.ArgumentParser(description='Twitch highlighter')
-    parser.add_argument('--client', "-c", help='Twitch client id', required=True, dest="tw_client")
-    parser.add_argument('--secret', "-s", help='Twitch secret id', required=True, dest="tw_secret")
-    parser.add_argument('--streamer', "-t", help='Twitch streamer username', required=True, dest="tw_streamer")
-    parser.add_argument('--quality', "-q", help='Video downloading quality', dest="tw_quality", default="360p")
-    parser.add_argument('--output_path', "-o", help='Video download folder', dest="output_path",
-                        default=os.path.join(os.getcwd(), "recorded"))
-
-    return parser.parse_args()
-
-
-def on_video_recorded(streamer, filename):
-    pass
-
-
-def on_chat_recorded(streamer, filename):
-    pass
-
-
-if __name__ == "__main__":
-    # TODO configure logging
-    logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-    args = parse_arguments()
-
-    config = recorder.RecorderConfig(args.tw_client, args.tw_secret, args.tw_streamer, args.tw_quality,
-                                     args.output_path)
-    recorder = recorder.Recorder(config)
-    recorder.run()
--- a/pipeline.sh
+++ b/pipeline.sh
@@ -0,0 +1,109 @@
+#!/bin/bash
+
+# Highlight Detector Pipeline con Modo Draft
+# Uso: ./pipeline.sh <video_id> [output_name] [--draft | --hd]
+
+set -e
+
+# Parsear argumentos
+DRAFT_MODE=false
+VIDEO_ID=""
+OUTPUT_NAME="highlights"
+
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --draft)
+            DRAFT_MODE=true
+            shift
+            ;;
+        --hd)
+            DRAFT_MODE=false
+            shift
+            ;;
+        *)
+            if [[ -z "$VIDEO_ID" ]]; then
+                VIDEO_ID="$1"
+            else
+                OUTPUT_NAME="$1"
+            fi
+            shift
+            ;;
+    esac
+done
+
+if [ -z "$VIDEO_ID" ]; then
+    echo "Uso: $0 <video_id> [output_name] [--draft | --hd]"
+    echo ""
+    echo "Modos:"
+    echo "  --draft   Modo prueba rápida (360p, menos procesamiento)"
+    echo "  --hd      Modo alta calidad (1080p, por defecto)"
+    echo ""
+    echo "Ejemplo:"
+    echo "  $0 2701190361 elxokas --draft   # Prueba rápida"
+    echo "  $0 2701190361 elxokas --hd      # Alta calidad"
+    exit 1
+fi
+
+echo "============================================"
+echo "  HIGHLIGHT DETECTOR PIPELINE"
+echo "============================================"
+echo "Video ID: $VIDEO_ID"
+echo "Output:   $OUTPUT_NAME"
+echo "Modo:     $([ "$DRAFT_MODE" = true ] && echo "DRAFT (360p)" || echo "HD (1080p)")"
+echo ""
+
+# Determinar calidad
+if [ "$DRAFT_MODE" = true ]; then
+    QUALITY="360p"
+    VIDEO_FILE="${OUTPUT_NAME}_draft.mp4"
+else
+    QUALITY="best"
+    VIDEO_FILE="${OUTPUT_NAME}.mp4"
+fi
+
+# 1. Descargar video
+echo "[1/5] Descargando video ($QUALITY)..."
+if [ ! -f "$VIDEO_FILE" ]; then
+    streamlink "https://www.twitch.tv/videos/${VIDEO_ID}" "$QUALITY" -o "$VIDEO_FILE"
+else
+    echo "Video ya existe: $VIDEO_FILE"
+fi
+
+# 2. Descargar chat
+echo "[2/5] Descargando chat..."
+if [ ! -f "${OUTPUT_NAME}_chat.json" ]; then
+    TwitchDownloaderCLI chatdownload --id "$VIDEO_ID" -o "${OUTPUT_NAME}_chat.json"
+else
+    echo "Chat ya existe"
+fi
+
+# 3. Detectar highlights (usando GPU si está disponible)
+echo "[3/5] Detectando highlights..."
+python3 detector_gpu.py \
+    --video "$VIDEO_FILE" \
+    --chat "${OUTPUT_NAME}_chat.json" \
+    --output "${OUTPUT_NAME}_highlights.json" \
+    --threshold 1.5 \
+    --min-duration 10
+
+# 4. Generar video
+echo "[4/5] Generando video..."
+python3 generate_video.py \
+    --video "$VIDEO_FILE" \
+    --highlights "${OUTPUT_NAME}_highlights.json" \
+    --output "${OUTPUT_NAME}_final.mp4"
+
+# 5. Limpiar
+echo "[5/5] Limpiando archivos temporales..."
+if [ "$DRAFT_MODE" = true ]; then
+    rm -f "${OUTPUT_NAME}_draft_360p.mp4"
+fi
+
+echo ""
+echo "============================================"
+echo "  COMPLETADO"
+echo "============================================"
+echo "Video final: ${OUTPUT_NAME}_final.mp4"
+echo ""
+echo "Para procesar en HD después:"
+echo "  $0 $VIDEO_ID ${OUTPUT_NAME}_hd --hd"
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,17 @@
-requests==2.28.1
-streamlink==4.2.0
-twitchAPI==2.5.7
-irc==20.1.0
-scipy==1.9.0
-matplotlib==3.5.2
-numpy==1.23.0
+# Core
+requests
+python-dotenv
+
+# Video processing
+moviepy
+opencv-python-headless
+
+# Audio processing
+scipy
+numpy
+librosa
+
+# Chat download
+chat-downloader
+
+# Chat analysis
--- a/todo.md
+++ b/todo.md
@@ -0,0 +1,229 @@
+# TODO - Mejoras Pendientes
+
+## Estado Actual
+
+### Working ✅
+- Descarga de video (streamlink)
+- Descarga de chat (TwitchDownloaderCLI)
+- Detección por chat saturado
+- Generación de video (moviepy)
+- PyTorch con ROCm instalado
+
+### Pendiente ❌
+- Análisis de audio
+- Análisis de color
+- Uso de GPU en procesamiento
+
+---
+
+## PRIORIDAD 1: Sistema 2 de 3
+
+### [ ] Audio - Picos de Sonido
+Implementar detección de gritos/picos de volumen.
+
+**Método actual (CPU):**
+- Extraer audio con ffmpeg
+- Usar librosa para RMS
+- Detectar picos con scipy
+
+**Método GPU (a implementar):**
+```python
+import torch
+import torchaudio
+
+# Usar GPU para análisis espectral
+waveform, sr = torchaudio.load(audio_file)
+spectrogram = torchaudio.transforms.Spectrogram()(waveform)
+```
+
+**Tareas:**
+- [ ] Extraer audio del video con ffmpeg
+- [ ] Calcular RMS/energía por ventana
+- [ ] Detectar picos (threshold = media + 1.5*std)
+- [ ] Devolver timestamps de picos
+
+### [ ] Color - Momentos Brillantes
+Detectar cambios de color/brillo en el video.
+
+**Método GPU:**
+```python
+import cv2
+# OpenCV con OpenCL
+cv2.ocl::setUseOpenCL(True)
+```
+
+**Tareas:**
+- [ ] Procesar frames con OpenCV GPU
+- [ ] Calcular saturación y brillo HSV
+- [ ] Detectar momentos con cambios significativos
+- [ ] Devolver timestamps
+
+### [ ] Combinar 2 de 3
+Sistema de scoring:
+```
+highlight = (chat_score >= 2) + (audio_score >= 1.5) + (color_score >= 0.5)
+if highlight >= 2: es highlight
+```
+
+---
+
+## PRIORIDAD 2: GPU - Optimizar para 6800XT
+
+### [ ] PyTorch con ROCm
+✅ Ya instalado:
+```
+PyTorch: 2.10.0+rocm7.1
+ROCm available: True
+Device: AMD Radeon Graphics
+```
+
+### [ ] OpenCV con OpenCL
+```bash
+# Verificar soporte OpenCL
+python -c "import cv2; print(cv2.ocl.haveOpenCL())"
+```
+
+**Si no tiene OpenCL:**
+- [ ] Instalar opencv-python (no headless)
+- [ ] Instalar ocl-runtime para AMD
+
+### [ ] Reemplazar librerías CPU por GPU
+
+| Componente | CPU | GPU |
+|------------|-----|-----|
+| Audio | librosa | torchaudio (ROCm) |
+| Video frames | cv2 | cv2 + OpenCL |
+| Procesamiento | scipy | torch |
+| Concatenación | moviepy | torch + ffmpeg |
+
+### [ ] MoviePy con GPU
+MoviePy actualmente usa CPU. Opciones:
+1. Usar ffmpeg directamente con flags GPU
+2. Crear pipeline propio con torch
+
+```bash
+# ffmpeg con GPU
+ffmpeg -hwaccel auto -i input.mp4 -c:v h264_amf output.mp4
+```
+
+---
+
+## PRIORIDAD 3: Mejorar Detección
+
+### [ ] Palabras Clave en Chat
+Detectar momentos con keywords como:
+- "LOL", "POG", "KEK", "RIP", "WTF"
+- Emotes populares
+- Mayúsculas (gritos en chat)
+
+### [ ] Análisis de Sentimiento
+- [ ] Usar modelo de sentiment (torch)
+- [ ] Detectar momentos positivos/negativos intensos
+
+### [ ] Ranking de Highlights
+- [ ] Ordenar por intensidad (combinación de scores)
+- [ ] Limitar a N mejores highlights
+- [ ] Duration-aware scoring
+
+---
+
+## PRIORIDAD 4: Kick
+
+### [ ] Descarga de Video
+✅ Ya funciona con streamlink:
+```bash
+streamlink https://kick.com/streamer best -o video.mp4
+```
+
+### [ ] Chat
+❌ Kick NO tiene API pública para chat.
+
+**Opciones:**
+1. Web scraping del chat
+2. Usar herramientas de terceros
+3. Omitir chat y usar solo audio/color
+
+---
+
+## PRIORIDAD 5: Optimizaciones
+
+### [ ] Paralelización
+- [ ] Procesar chunks del video en paralelo
+- [ ] ThreadPool para I/O
+
+### [ ] Cache
+- [ ] Guardar resultados intermedios
+- [ ] Reutilizar análisis si existe chat.txt
+
+### [ ] Chunking
+- [ ] Procesar video en segmentos
+- [ ] Evitar cargar todo en memoria
+
+---
+
+## PRIORIDAD 6: UX/UI
+
+### [ ] CLI Mejorada
+```bash
+python main.py --video-id 2701190361 --platform twitch \
+  --min-duration 10 --threshold 2.0 \
+  --output highlights.mp4 \
+  --use-gpu --gpu-device 0
+```
+
+### [ ] Interfaz Web
+- [ ] Streamlit app
+- [ ] Subir video/chat
+- [ ] Ver timeline de highlights
+- [ ] Preview de clips
+
+### [ ] Progress Bars
+- [ ] tqdm para descargas
+- [ ] Progress para procesamiento
+
+---
+
+## RECETAS DE INSTALACIÓN
+
+### GPU ROCm
+```bash
+# PyTorch con ROCm
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1
+
+# Verificar
+python -c "import torch; print(torch.cuda.is_available())"
+```
+
+### NVIDIA CUDA (alternativa)
+```bash
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+```
+
+### OpenCV con OpenCL
+```bash
+# Verificar
+python -c "import cv2; print(cv2.ocl.haveOpenCL())"
+
+# Si False, instalar con GPU support
+pip uninstall opencv-python-headless
+pip install opencv-python
+```
+
+---
+
+## RENDIMIENTO ESPERADO
+
+| Config | FPS Processing | Tiempo 5h Video |
+|--------|----------------|------------------|
+| CPU (12 cores) | ~5-10 FPS | ~1-2 horas |
+| GPU NVIDIA 3050 | ~30-50 FPS | ~10-20 min |
+| GPU AMD 6800XT | ~30-40 FPS | ~15-25 min |
+
+---
+
+## NOTAS
+
+1. **ROCm 7.1** funcionando con PyTorch
+2. **6800XT** detectada como "AMD Radeon Graphics"
+3. **MoviePy** sigue usando CPU para renderizado
+4. Para mejor rendimiento, considerar renderizado con ffmpeg GPU directamente