#v2 - Inicia testes da v2

- Adiciona rastreamento de objetos
- Facial detection
- Legenda interativa
- Cortes mais precisos
- Refinamento do Prompt

.env.example

@@ -0,0 +1,47 @@
+RABBITMQ_HOST=rabbitmq
+RABBITMQ_PORT=5672
+RABBITMQ_USER=admin
+RABBITMQ_PASS=your_password_here
+RABBITMQ_QUEUE=to-render
+RABBITMQ_UPLOAD_QUEUE=to-upload
+RABBITMQ_PREFETCH=1
+RABBITMQ_HEARTBEAT=60
+RABBITMQ_BLOCKED_TIMEOUT=300
+OPENROUTER_API_URL=https://openrouter.ai/api/v1/chat/completions
+OPENROUTER_API_KEY=your_openrouter_api_key_here
+
+# Model selection - Recommended options:
+# - openai/gpt-oss-20b:free (Free tier, good quality)
+# - qwen/qwen-2.5-72b-instruct:free (Free, excellent reasoning)
+# - google/gemini-pro-1.5 (Best cost-benefit for podcasts)
+# - anthropic/claude-3.5-sonnet (Premium quality, best reasoning)
+OPENROUTER_MODEL=qwen/qwen-2.5-72b-instruct:free
+OPENROUTER_TEMPERATURE=0.6
+OPENROUTER_PROMPT_PATH=prompts/generate.txt
+
+FASTER_WHISPER_MODEL_SIZE=medium
+FASTER_WHISPER_DEVICE=auto
+
+RENDER_WIDTH=1080
+RENDER_HEIGHT=1920
+
+RENDER_FPS=30
+RENDER_CODEC=libx264
+RENDER_AUDIO_CODEC=aac
+RENDER_BITRATE=5000k
+RENDER_PRESET=faster
+
+SUBTITLE_HIGHLIGHT_COLOR=#00FF00
+SUBTITLE_BASE_COLOR=#FFFFFF
+
+RENDER_FONT_PATH=./Montserrat.ttf
+RENDER_TITLE_FONT_SIZE=110
+RENDER_SUBTITLE_FONT_SIZE=64
+
+CAPTION_MIN_WORDS=2
+CAPTION_MAX_WORDS=2
+
+ENABLE_SMART_FRAMING=true
+SMART_FRAMING_MIN_CONFIDENCE=0.5
+SMART_FRAMING_SMOOTHING_WINDOW=20
+SMART_FRAMING_FRAME_SKIP=2

.gitignore

@@ -14,7 +14,7 @@ outputs/
 # Ignore virtual envs
 venv/
 env/
-
+.claude
 # Ignore editor files
 .idea/
 *.swp
@@ -31,3 +31,4 @@ env/
 # Ignore mypy and pylint cache
 .mypy_cache/
 .pylint.d/
+CLAUDE.MD

docker-compose.yml

@@ -3,15 +3,18 @@ services:
     restart: unless-stopped
     build: .
     environment:
-      - FASTER_WHISPER_MODEL_SIZE=medium
-      - GEMINI_API_KEY=${GEMINI_API_KEY}
-      - GEMINI_MODEL=gemini-2.5-flash
-      - OPENROUTER_API_KEY=${OPENROUTER_API_KEY}
-      - OPENROUTER_MODEL=openai/gpt-oss-20b:free
       - RABBITMQ_PASS=${RABBITMQ_PASS}
+      - OPENROUTER_API_URL=${OPENROUTER_API_URL:-https://openrouter.ai/api/v1/chat/completions}
+      - OPENROUTER_API_KEY=${OPENROUTER_API_KEY}
+      - OPENROUTER_MODEL=${OPENROUTER_MODEL:-openai/gpt-oss-20b:free}
+      - OPENROUTER_PROMPT_PATH=${OPENROUTER_PROMPT_PATH:-prompts/generate.txt}
+      - FASTER_WHISPER_MODEL_SIZE=${FASTER_WHISPER_MODEL_SIZE:-medium}
     volumes:
       - "/root/videos:/app/videos"
       - "/root/outputs:/app/outputs"
+      - "/root/prompts:/app/prompts"
+      # - "./videos:/app/videos"
+      # - "./outputs:/app/outputs"
     command: "python -u main.py"
     networks:
       - dokploy-network

dockerfile

@@ -23,6 +23,9 @@ RUN apt-get update && \
         imagemagick \
         fonts-liberation \
         wget \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
         && rm -rf /var/lib/apt/lists/*
 
 COPY requirements.txt .

main.py

@@ -1,3 +1,17 @@
+import os
+import warnings
+
+# Suppress FFmpeg/AV1 warnings for cleaner logs
+os.environ['OPENCV_FFMPEG_CAPTURE_OPTIONS'] = 'loglevel;quiet'
+os.environ['OPENCV_LOG_LEVEL'] = 'ERROR'
+
+# Suppress MoviePy verbose logging
+os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
+
+# Filter deprecation warnings
+warnings.filterwarnings('ignore', category=DeprecationWarning)
+warnings.filterwarnings('ignore', category=UserWarning, module='moviepy')
+
 from video_render.config import load_settings
 from video_render.logging_utils import setup_logging
 from video_render.messaging import RabbitMQWorker

prompts/generate.txt

@@ -1,36 +1,85 @@
-Voce e um estrategista de conteudo especializado em identificar cortes curtos de videos longos que performam bem em redes sociais.
+Voce e especialista em viralidade de redes sociais (TikTok, Instagram Reels, YouTube Shorts). Analise a transcricao e selecione trechos com MAXIMO potencial viral, priorizando qualidade sobre quantidade.
 
-FUNCAO:
-- Analisar a transcricao completa de um video.
-- Escolher trechos curtos (entre 60s e 90s) com maior chance de engajamento.
-- O inicio do trecho deve ter um hook para engajar e prender a atenção do espectador.
-- Responder APENAS em JSON valido.
+PROCESSO DE ANALISE:
+1. Mapear potenciais trechos na transcricao
+2. Avaliar cada trecho usando sistema de pontuacao abaixo
+3. Rankear do maior para menor score viral
+4. Selecionar apenas os top-ranked baseado na duracao do video
 
-FORMATO DA RESPOSTA:
-{
-  "highlights": [
-    {
-      "start": <segundos_inicio_float>,
-      "end": <segundos_fim_float>,
-      "summary": "Resumo conciso do porque este trecho engaja"
-    }
-  ]
-}
+SISTEMA DE PONTUACAO VIRAL (0-100 pontos):
 
-REGRAS:
-- Liste no maximo 6 destaques.
-- Respeite a ordem cronologica.
-- Nunca deixe listas vazias; se nada for relevante, inclua uma entrada com start = 0, end = 0 e summary explicando a ausencia de cortes.
-- Utilize apenas valores numericos simples (ponto como separador decimal).
-- Nao repita um mesmo trecho.
+HOOK/ABERTURA (0-25 pontos):
+[25] Frase choqueante, pergunta polemica ou promessa ousada
+[20] Historia intrigante ou situacao inusitada
+[15] Afirmacao interessante mas previsivel
+[10] Introducao generica mas aceitavel
+[0] "Oi", "entao", silencio ou conteudo fraco
 
-PERSPECTIVA DE ANALISE:
-- Concentre-se em momentos com gatilhos emocionais, insights, storytelling ou chamadas para acao fortes.
-- Prefira trechos com comeco, meio e fim claros.
-- Evite partes redundantes, silenciosas ou extremamente tecnicas.
+GATILHO EMOCIONAL (0-25 pontos):
+[25] Emocao extrema: raiva, choque, riso intenso, inspiracao profunda
+[20] Emocao forte: surpresa, indignacao, humor, curiosidade intensa
+[15] Emocao moderada: interesse, leve humor, curiosidade
+[10] Emocao fraca: informativo sem impacto emocional
+[0] Monotono, tecnico, sem apelo emocional
+
+VALOR/UTILIDADE (0-20 pontos):
+[20] Segredo valioso, insight transformador ou informacao exclusiva
+[15] Ensina algo pratico e imediatamente aplicavel
+[10] Opiniao interessante ou perspectiva util
+[5] Informacao generica ou conhecimento comum
+[0] Nenhum valor pratico, puro enrolation
+
+ESTRUTURA NARRATIVA (0-15 pontos):
+[15] Historia completa com inicio, conflito/climax e resolucao
+[10] Segmento com comeco e fim coerentes
+[5] Trecho com sentido mas cortado abruptamente
+[0] Fragmento sem contexto ou conclusao
+
+RITMO E ENERGIA (0-15 pontos):
+[15] Dinamico, sem pausas, alta energia, palavras impactantes
+[10] Bom ritmo com pausas naturais curtas
+[5] Ritmo lento mas aceitavel
+[0] Muitas pausas, hesitacoes, monotonia, silencio
+
+REGRAS DE QUANTIDADE:
+5-10 min: 3 clipes (minimo 1 se score alto)
+10-20 min: 4 clipes
+20-30 min: 5 clipes
+30+ min: 6 clipes (maximo absoluto)
+
+IMPORTANTE: Priorize qualidade. Melhor 3 clipes score 80+ que 6 clipes score 50. Se poucos momentos virais, retorne apenas os melhores (minimo 1).
+
+CRITERIOS DE SELECAO:
+- Score viral maior ou igual 60 pontos (idealmente maior ou igual 70)
+- Duracao ideal: 60-90s
+- Duracao minima: 60s | Duracao maxima: 120s
+- Sem sobreposicao (end de um menor que start do proximo)
+- Inicio e fim coerentes
+
+EVITE:
+- Introducoes genericas
+- Trechos com silencio/pausas maiores que 3s
+- Explicacoes tecnicas sem gancho emocional
+- Segmentos sem conclusao
+- Momentos de transicao
+
+FORMATO JSON (retorne APENAS isto):
+{"highlights":[{"start":<float>,"end":<float>,"summary":"Score estimado e gatilhos principais"}]}
+
+REGRAS TECNICAS:
+- Float com ponto decimal (45.5 NAO 45,5)
+- Timestamps exatos dos segments fornecidos
+- Ordem cronologica (start crescente)
+- Minimo 1, maximo 6 highlights
+- Summary conciso (1-2 frases)
 
 TAREFA:
-- Leia a transcricao recebida no campo "transcript".
-- Use a lista de marcas de tempo detalhadas no campo "segments" para embasar suas escolhas.
-- Produza a saida JSON descrita acima.
+1. Leia transcricao e timestamps
+2. Avalie e pontue trechos mentalmente
+3. Rankear por score viral
+4. Selecione top-ranked baseado na duracao
+5. Retorne JSON
+6. Se video fraco, retorne pelo menos 1 highlight
+
+Objetivo: MAXIMIZAR chance de viralizar. Seja criterioso, apenas melhores trechos.
 

requirements.txt

@@ -4,4 +4,6 @@ numpy>=1.26.0
 requests
 pika
 faster-whisper==1.2.0
-google-genai
+mediapipe==0.10.18
+opencv-python==4.10.0.84
+scipy>=1.11.0

video_render/config.py

@@ -13,6 +13,8 @@ TEMP_ROOT = BASE_DIR / "temp"
 
 @dataclass(frozen=True)
 class RabbitMQSettings:
+    # host: str = os.environ.get("RABBITMQ_HOST", "154.12.229.181")
+    # port: int = int(os.environ.get("RABBITMQ_PORT", 32790))
     host: str = os.environ.get("RABBITMQ_HOST", "rabbitmq")
     port: int = int(os.environ.get("RABBITMQ_PORT", 5672))
     user: str = os.environ.get("RABBITMQ_USER", "admin")
@@ -24,33 +26,19 @@ class RabbitMQSettings:
     blocked_timeout: int = int(os.environ.get("RABBITMQ_BLOCKED_TIMEOUT", 300))
 
 
-@dataclass(frozen=True)
-class GeminiSettings:
-    api_key: str = os.environ.get("GEMINI_API_KEY", "")
-    model: str = os.environ.get("GEMINI_MODEL", "gemini-2.5-flash")
-    safety_settings: str | None = os.environ.get("GEMINI_SAFETY_SETTINGS")
-    temperature: float = float(os.environ.get("GEMINI_TEMPERATURE", 0.2))
-    top_k: int | None = (
-        int(os.environ["GEMINI_TOP_K"]) if os.environ.get("GEMINI_TOP_K") else None
-    )
-    top_p: float | None = (
-        float(os.environ["GEMINI_TOP_P"]) if os.environ.get("GEMINI_TOP_P") else None
-    )
-    prompt_path: str = os.environ.get("GEMINI_PROMPT_PATH", "prompts/generate.txt")
-
-
 @dataclass(frozen=True)
 class OpenRouterSettings:
-    api_key: str = os.environ.get("OPENROUTER_API_KEY", "")
+    api_key: str = os.environ.get("OPENROUTER_API_KEY", "https://openrouter.ai/api/v1/chat/completions")
     model: str = os.environ.get(
         "OPENROUTER_MODEL", "openai/gpt-oss-20b:free"
     )
     temperature: float = float(os.environ.get("OPENROUTER_TEMPERATURE", 0.6))
+    prompt_path: str = os.environ.get("OPENROUTER_PROMPT_PATH", "prompts/generate.txt")
 
 
 @dataclass(frozen=True)
 class WhisperSettings:
-    model_size: str = os.environ.get("FASTER_WHISPER_MODEL_SIZE", "small")
+    model_size: str = os.environ.get("FASTER_WHISPER_MODEL_SIZE", "medium")
     device: str | None = os.environ.get("FASTER_WHISPER_DEVICE")
     compute_type: str | None = os.environ.get("FASTER_WHISPER_COMPUTE_TYPE")
     download_root: Path = Path(
@@ -67,19 +55,23 @@ class RenderingSettings:
     audio_codec: str = os.environ.get("RENDER_AUDIO_CODEC", "aac")
     bitrate: str = os.environ.get("RENDER_BITRATE", "5000k")
     preset: str = os.environ.get("RENDER_PRESET", "faster")
-    highlight_color: str = os.environ.get("SUBTITLE_HIGHLIGHT_COLOR", "#FFD200")
+    highlight_color: str = os.environ.get("SUBTITLE_HIGHLIGHT_COLOR", "#00FF00")
     base_color: str = os.environ.get("SUBTITLE_BASE_COLOR", "#FFFFFF")
     font_path: Path = Path(os.environ.get("RENDER_FONT_PATH", "./Montserrat.ttf"))
     title_font_size: int = int(os.environ.get("RENDER_TITLE_FONT_SIZE", 110))
     subtitle_font_size: int = int(os.environ.get("RENDER_SUBTITLE_FONT_SIZE", 64))
-    caption_min_words: int = int(os.environ.get("CAPTION_MIN_WORDS", 3))
-    caption_max_words: int = int(os.environ.get("CAPTION_MAX_WORDS", 4))
+    caption_min_words: int = int(os.environ.get("CAPTION_MIN_WORDS", 2))
+    caption_max_words: int = int(os.environ.get("CAPTION_MAX_WORDS", 2))
+    # Smart framing settings
+    enable_smart_framing: bool = os.environ.get("ENABLE_SMART_FRAMING", "true").lower() in ("true", "1", "yes")
+    smart_framing_min_confidence: float = float(os.environ.get("SMART_FRAMING_MIN_CONFIDENCE", 0.5))
+    smart_framing_smoothing_window: int = int(os.environ.get("SMART_FRAMING_SMOOTHING_WINDOW", 20))
+    smart_framing_frame_skip: int = int(os.environ.get("SMART_FRAMING_FRAME_SKIP", 2))  # Process every Nth frame (CPU optimization)
 
 
 @dataclass(frozen=True)
 class Settings:
     rabbitmq: RabbitMQSettings = RabbitMQSettings()
-    gemini: GeminiSettings = GeminiSettings()
     openrouter: OpenRouterSettings = OpenRouterSettings()
     whisper: WhisperSettings = WhisperSettings()
     rendering: RenderingSettings = RenderingSettings()

video_render/context_detection.py

@@ -0,0 +1,398 @@
+"""
+Context detection module for video analysis.
+
+This module provides functionality to detect faces, track people,
+and identify who is speaking in video content using MediaPipe and audio analysis.
+"""
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+
+import cv2
+import mediapipe as mp
+import numpy as np
+from scipy import signal
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class FaceDetection:
+    """Represents a detected face in a frame."""
+    x: int
+    y: int
+    width: int
+    height: int
+    confidence: float
+    center_x: int
+    center_y: int
+    landmarks: Optional[List[Tuple[int, int]]] = None
+
+
+@dataclass
+class PersonTracking:
+    """Tracks a person across frames."""
+    person_id: int
+    face: FaceDetection
+    is_speaking: bool
+    speaking_confidence: float
+    frame_number: int
+
+
+@dataclass
+class FrameContext:
+    """Context information for a video frame."""
+    frame_number: int
+    timestamp: float
+    detected_faces: List[FaceDetection]
+    active_speakers: List[int]  # indices of speaking faces
+    primary_focus: Optional[Tuple[int, int]]  # (x, y) center point
+    layout_mode: str  # "single", "dual_split", "grid"
+
+
+class MediaPipeDetector:
+    """Face and pose detection using MediaPipe."""
+
+    def __init__(self, min_detection_confidence: float = 0.5, min_tracking_confidence: float = 0.5):
+        self.min_detection_confidence = min_detection_confidence
+        self.min_tracking_confidence = min_tracking_confidence
+        self.mp_face_detection = mp.solutions.face_detection
+        self.mp_face_mesh = mp.solutions.face_mesh
+
+        self.face_detection = self.mp_face_detection.FaceDetection(
+            min_detection_confidence=min_detection_confidence,
+            model_selection=1
+        )
+
+        self.face_mesh = self.mp_face_mesh.FaceMesh(
+            max_num_faces=5,
+            min_detection_confidence=min_detection_confidence,
+            min_tracking_confidence=min_tracking_confidence,
+            static_image_mode=False
+        )
+
+        logger.info("MediaPipe detector initialized")
+
+    def detect_faces(self, frame: np.ndarray) -> List[FaceDetection]:
+        """
+        Detect faces in a frame.
+
+        Args:
+            frame: RGB image array
+
+        Returns:
+            List of detected faces
+        """
+        height, width = frame.shape[:2]
+
+        if len(frame.shape) == 2:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+        elif frame.shape[2] == 4:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGRA2RGB)
+        else:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        results = self.face_detection.process(frame_rgb)
+
+        faces = []
+        if results.detections:
+            for detection in results.detections:
+                bbox = detection.location_data.relative_bounding_box
+
+                x = int(bbox.xmin * width)
+                y = int(bbox.ymin * height)
+                w = int(bbox.width * width)
+                h = int(bbox.height * height)
+
+                x = max(0, min(x, width - 1))
+                y = max(0, min(y, height - 1))
+                w = min(w, width - x)
+                h = min(h, height - y)
+
+                center_x = x + w // 2
+                center_y = y + h // 2
+
+                confidence = detection.score[0] if detection.score else 0.0
+
+                faces.append(FaceDetection(
+                    x=x,
+                    y=y,
+                    width=w,
+                    height=h,
+                    confidence=confidence,
+                    center_x=center_x,
+                    center_y=center_y
+                ))
+
+        return faces
+
+    def detect_face_landmarks(self, frame: np.ndarray) -> List[FaceDetection]:
+        """
+        Detect faces with landmarks for lip sync detection.
+
+        Args:
+            frame: RGB image array
+
+        Returns:
+            List of detected faces with landmark information
+        """
+        height, width = frame.shape[:2]
+
+        if len(frame.shape) == 2:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+        elif frame.shape[2] == 4:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGRA2RGB)
+        else:
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        results = self.face_mesh.process(frame_rgb)
+
+        faces = []
+        if results.multi_face_landmarks:
+            for face_landmarks in results.multi_face_landmarks:
+                xs = [lm.x for lm in face_landmarks.landmark]
+                ys = [lm.y for lm in face_landmarks.landmark]
+
+                x_min, x_max = min(xs), max(xs)
+                y_min, y_max = min(ys), max(ys)
+
+                x = int(x_min * width)
+                y = int(y_min * height)
+                w = int((x_max - x_min) * width)
+                h = int((y_max - y_min) * height)
+
+                center_x = x + w // 2
+                center_y = y + h // 2
+
+                lip_landmarks = []
+                for idx in [13, 14, 78, 308]:
+                    lm = face_landmarks.landmark[idx]
+                    lip_landmarks.append((int(lm.x * width), int(lm.y * height)))
+
+                faces.append(FaceDetection(
+                    x=x,
+                    y=y,
+                    width=w,
+                    height=h,
+                    confidence=1.0,
+                    center_x=center_x,
+                    center_y=center_y,
+                    landmarks=lip_landmarks
+                ))
+
+        return faces
+
+    def close(self):
+        """Release MediaPipe resources."""
+        self.face_detection.close()
+        self.face_mesh.close()
+
+
+class AudioActivityDetector:
+    """Detects speech activity in audio."""
+
+    def __init__(self, sample_rate: int = 44100, frame_duration_ms: int = 30):
+        self.sample_rate = sample_rate
+        self.frame_duration_ms = frame_duration_ms
+        self.frame_size = int(sample_rate * frame_duration_ms / 1000)
+
+        logger.info(f"Audio activity detector initialized (sr={sample_rate}, frame={frame_duration_ms}ms)")
+
+    def detect_speaking_periods(
+        self,
+        audio_samples: np.ndarray,
+        threshold: float = 0.02,
+        min_speech_duration: float = 0.1
+    ) -> List[Tuple[float, float]]:
+        """
+        Detect periods of speech in audio.
+
+        Args:
+            audio_samples: Audio samples array
+            threshold: Energy threshold for speech detection
+            min_speech_duration: Minimum duration of speech in seconds
+
+        Returns:
+            List of (start_time, end_time) tuples in seconds
+        """
+        if audio_samples.ndim > 1:
+            audio_samples = audio_samples.mean(axis=1)
+
+        energies = []
+        for i in range(0, len(audio_samples), self.frame_size):
+            frame = audio_samples[i:i + self.frame_size]
+            if len(frame) > 0:
+                energy = np.sqrt(np.mean(frame ** 2))
+                energies.append(energy)
+
+        speaking_frames = [e > threshold for e in energies]
+
+        periods = []
+        start_frame = None
+
+        for i, is_speaking in enumerate(speaking_frames):
+            if is_speaking and start_frame is None:
+                start_frame = i
+            elif not is_speaking and start_frame is not None:
+                start_time = start_frame * self.frame_duration_ms / 1000
+                end_time = i * self.frame_duration_ms / 1000
+
+                if end_time - start_time >= min_speech_duration:
+                    periods.append((start_time, end_time))
+
+                start_frame = None
+
+        if start_frame is not None:
+            start_time = start_frame * self.frame_duration_ms / 1000
+            end_time = len(speaking_frames) * self.frame_duration_ms / 1000
+            if end_time - start_time >= min_speech_duration:
+                periods.append((start_time, end_time))
+
+        return periods
+
+    def is_speaking_at_time(self, speaking_periods: List[Tuple[float, float]], time: float) -> bool:
+        """Check if there is speech activity at a given time."""
+        for start, end in speaking_periods:
+            if start <= time <= end:
+                return True
+        return False
+
+
+class ContextAnalyzer:
+    """Analyzes video context to determine focus and layout."""
+
+    def __init__(self):
+        self.detector = MediaPipeDetector()
+        self.audio_detector = AudioActivityDetector()
+        self.previous_faces: List[FaceDetection] = []
+
+        logger.info("Context analyzer initialized")
+
+    def analyze_frame(
+        self,
+        frame: np.ndarray,
+        timestamp: float,
+        frame_number: int,
+        speaking_periods: Optional[List[Tuple[float, float]]] = None
+    ) -> FrameContext:
+        """
+        Analyze a single frame to extract context information.
+
+        Args:
+            frame: Video frame (BGR format from OpenCV)
+            timestamp: Frame timestamp in seconds
+            frame_number: Frame index
+            speaking_periods: List of (start, end) times where speech is detected
+
+        Returns:
+            FrameContext with detection results
+        """
+        faces = self.detector.detect_face_landmarks(frame)
+
+        if not faces:
+            faces = self.detector.detect_faces(frame)
+
+        # Determine who is speaking
+        active_speakers = []
+        for i, face in enumerate(faces):
+            is_speaking = False
+
+            if speaking_periods and self.audio_detector.is_speaking_at_time(speaking_periods, timestamp):
+                is_speaking = True
+
+            if face.landmarks and len(self.previous_faces) > i:
+                is_speaking = is_speaking or self._detect_lip_movement(face, self.previous_faces[i])
+
+            if is_speaking:
+                active_speakers.append(i)
+
+        num_faces = len(faces)
+        num_speakers = len(active_speakers)
+
+        if num_faces == 0:
+            layout_mode = "single"
+        elif num_faces == 1:
+            layout_mode = "single"
+        elif num_faces == 2:
+            layout_mode = "dual_split"
+        elif num_faces >= 3:
+            layout_mode = "dual_split"
+        else:
+            layout_mode = "single"
+
+        primary_focus = self._calculate_focus_point(faces, active_speakers)
+
+        self.previous_faces = faces
+
+        return FrameContext(
+            frame_number=frame_number,
+            timestamp=timestamp,
+            detected_faces=faces,
+            active_speakers=active_speakers,
+            primary_focus=primary_focus,
+            layout_mode=layout_mode
+        )
+
+    def _detect_lip_movement(self, current_face: FaceDetection, previous_face: FaceDetection) -> bool:
+        """
+        Detect lip movement by comparing landmarks between frames.
+
+        Args:
+            current_face: Current frame face detection
+            previous_face: Previous frame face detection
+
+        Returns:
+            True if significant lip movement detected
+        """
+        if not current_face.landmarks or not previous_face.landmarks:
+            return False
+
+        def lip_distance(landmarks):
+            if len(landmarks) < 4:
+                return 0
+            
+            upper = np.array(landmarks[0:2])
+            lower = np.array(landmarks[2:4])
+            return np.linalg.norm(upper.mean(axis=0) - lower.mean(axis=0))
+
+        current_dist = lip_distance(current_face.landmarks)
+        previous_dist = lip_distance(previous_face.landmarks)
+
+        threshold = 2.0
+        return abs(current_dist - previous_dist) > threshold
+
+    def _calculate_focus_point(
+        self,
+        faces: List[FaceDetection],
+        active_speakers: List[int]
+    ) -> Optional[Tuple[int, int]]:
+        """
+        Calculate the primary focus point based on detected faces and speakers.
+
+        IMPORTANT: This focuses on ONE person to avoid focusing on empty space (table).
+        When multiple people are present, we pick the most relevant person, not average positions.
+
+        Args:
+            faces: List of detected faces
+            active_speakers: Indices of faces that are speaking
+
+        Returns:
+            (x, y) tuple of focus center, or None if no faces
+        """
+        if not faces:
+            return None
+
+        if active_speakers:
+            speaker_faces = [faces[i] for i in active_speakers if i < len(faces)]
+            if speaker_faces:
+                primary_speaker = max(speaker_faces, key=lambda f: f.confidence)
+                return (primary_speaker.center_x, primary_speaker.center_y)
+
+        most_confident = max(faces, key=lambda f: f.confidence)
+        return (most_confident.center_x, most_confident.center_y)
+
+    def close(self):
+        """Release resources."""
+        self.detector.close()

video_render/llm.py

@@ -2,11 +2,11 @@ from __future__ import annotations
 
 import json
 import logging
+import time
+import os
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Dict, List
 
-from google import genai
-from google.genai import types as genai_types
 import requests
 
 from video_render.config import BASE_DIR, Settings
@@ -14,27 +14,24 @@ from video_render.transcription import TranscriptionResult
 
 logger = logging.getLogger(__name__)
 
-OPENROUTER_ENDPOINT = "https://openrouter.ai/api/v1/chat/completions"
+OPENROUTER_ENDPOINT = os.environ.get("OPENROUTER_API_URL", "https://openrouter.ai/api/v1/chat/completions")
 
 
-class GeminiHighlighter:
+class OpenRouterCopywriter:
     def __init__(self, settings: Settings) -> None:
-        if not settings.gemini.api_key:
-            raise RuntimeError("GEMINI_API_KEY nao foi definido")
-
-        prompt_path = Path(settings.gemini.prompt_path)
+        if not settings.openrouter.api_key:
+            raise RuntimeError("OPENROUTER_API_KEY nao foi definido")
+        self.settings = settings
+        prompt_path = Path(settings.openrouter.prompt_path)
 
         if not prompt_path.is_absolute():
             prompt_path = BASE_DIR / prompt_path
-
         if not prompt_path.exists():
-            raise FileNotFoundError(f"Prompt do Gemini nao encontrado: {prompt_path}")
-
-        self.prompt_template = prompt_path.read_text(encoding="utf-8")
-        self.settings = settings
-        self.client = genai.Client()
+            raise FileNotFoundError(f"Prompt nao encontrado: {prompt_path}")
+        self.highlights_prompt_template = prompt_path.read_text(encoding="utf-8")
 
     def generate_highlights(self, transcription: TranscriptionResult) -> List[Dict]:
+        """Generate video highlights using OpenRouter GPT-OSS with retry logic."""
         payload = {
             "transcript": transcription.full_text,
             "segments": [
@@ -47,93 +44,139 @@ class GeminiHighlighter:
             ],
         }
 
-        try:
-            response = self._call_gemini(payload)
-        except Exception as exc:
-            logger.error("Gemini API request falhou: %s", exc)
-            raise RuntimeError("Gemini API request falhou") from exc
-
-        raw_text = self._extract_response_text(response)
-
-        parsed = self._extract_json(raw_text)
-        highlights = parsed.get("highlights")
-        if not isinstance(highlights, list):
-            raise ValueError("Resposta do Gemini invalida: campo 'highlights' ausente")
-        return highlights
-
-    def _call_gemini(self, payload: Dict[str, Any]) -> Any:
-        contents = [
-            {
-                "role": "user",
-                "parts": [
-                    {"text": self.prompt_template},
-                    {"text": json.dumps(payload, ensure_ascii=False)},
-                ],
-            }
-        ]
-
-        request_kwargs: Dict[str, Any] = {
-            "model": self.settings.gemini.model,
-            "contents": contents,
+        body = {
+            "model": self.settings.openrouter.model,
+            "temperature": self.settings.openrouter.temperature,
+            "messages": [
+                {"role": "system", "content": self.highlights_prompt_template},
+                {
+                    "role": "user",
+                    "content": json.dumps(payload, ensure_ascii=False),
+                },
+            ],
         }
 
-        config = self._build_generation_config()
-        if config is not None:
-            request_kwargs["config"] = config
+        headers = {
+            "Authorization": f"Bearer {self.settings.openrouter.api_key}",
+            "Content-Type": "application/json",
+            "X-Title": "Video Render - Highlights Detection"
+        }
 
-        return self.client.models.generate_content(**request_kwargs)
+        logger.info(f"Calling OpenRouter with model: {self.settings.openrouter.model}")
+        logger.debug(f"Request payload keys: transcript_length={len(payload['transcript'])}, segments_count={len(payload['segments'])}")
 
-    def _build_generation_config(self) -> Optional[genai_types.GenerateContentConfig]:
-        config_kwargs: Dict[str, Any] = {}
-        if self.settings.gemini.temperature is not None:
-            config_kwargs["temperature"] = self.settings.gemini.temperature
-        if self.settings.gemini.top_p is not None:
-            config_kwargs["top_p"] = self.settings.gemini.top_p
-        if self.settings.gemini.top_k is not None:
-            config_kwargs["top_k"] = self.settings.gemini.top_k
+        # Retry configuration for rate limits (especially free tier)
+        max_retries = 5
+        base_delay = 5  # Start with 5s delay
 
-        if not config_kwargs:
-            return None
+        for attempt in range(max_retries):
+            try:
+                response = requests.post(
+                    url=OPENROUTER_ENDPOINT,
+                    data=json.dumps(body),
+                    headers=headers,
+                    timeout=120,
+                )
+                response.raise_for_status()
+                data = response.json()
+                break
 
-        return genai_types.GenerateContentConfig(**config_kwargs)
+            except requests.exceptions.HTTPError as exc:
+                if exc.response.status_code == 429:
+                    if attempt < max_retries - 1:
+                        # Exponential backoff: 5s, 10s, 20s, 40s, 80s
+                        delay = base_delay * (2 ** attempt)
+                        logger.warning(f"Rate limit atingido (429). Aguardando {delay}s antes de tentar novamente (tentativa {attempt + 1}/{max_retries})")
+                        time.sleep(delay)
+                        continue
+                    else:
+                        logger.error("Rate limit atingido apos todas as tentativas")
+                        logger.error("Solucao: Use um modelo pago ou adicione creditos na OpenRouter")
+                        raise RuntimeError("OpenRouter rate limit excedido") from exc
+                else:
+                    logger.error(f"OpenRouter API request falhou com status {exc.response.status_code}: {exc}")
+                    raise RuntimeError("OpenRouter API request falhou") from exc
 
-    @staticmethod
-    def _extract_response_text(response: Any) -> str:
-        text = getattr(response, "text", None)
-        if text:
-            return str(text).strip()
+            except Exception as exc:
+                logger.error("OpenRouter API request falhou: %s", exc)
+                raise RuntimeError("OpenRouter API request falhou") from exc
 
-        candidates = getattr(response, "candidates", None) or []
-        for candidate in candidates:
-            content = getattr(candidate, "content", None)
-            if not content:
+        # Debug: log response structure
+        logger.info(f"OpenRouter response keys: {list(data.keys())}")
+        if "error" in data:
+            logger.error(f"OpenRouter API error: {data.get('error')}")
+            raise RuntimeError(f"OpenRouter API error: {data.get('error')}")
+
+        choices = data.get("choices") or []
+        if not choices:
+            logger.error(f"OpenRouter response completa: {json.dumps(data, indent=2)}")
+            raise RuntimeError("OpenRouter nao retornou escolhas")
+
+        message = choices[0].get("message", {}).get("content")
+        if not message:
+            raise RuntimeError("Resposta do OpenRouter sem conteudo")
+
+        parsed = self._extract_json(message)
+        highlights = parsed.get("highlights")
+        if not isinstance(highlights, list):
+            raise ValueError("Resposta do OpenRouter invalida: campo 'highlights' ausente")
+
+        valid_highlights = []
+        for highlight in highlights:
+            try:
+                start = float(highlight.get("start", 0))
+                end = float(highlight.get("end", 0))
+                summary = str(highlight.get("summary", "")).strip()
+
+                if start < 0 or end < 0:
+                    logger.warning(f"Highlight ignorado: timestamps negativos (start={start}, end={end})")
+                    continue
+
+                if end <= start:
+                    logger.warning(f"Highlight ignorado: end <= start (start={start}, end={end})")
+                    continue
+
+                duration = end - start
+                if duration < 45:
+                    logger.warning(f"Highlight ignorado: muito curto ({duration}s, minimo 45s)")
+                    continue
+
+                if duration > 120:
+                    logger.warning(f"Highlight ignorado: muito longo ({duration}s, maximo 120s)")
+                    continue
+
+                if not summary:
+                    logger.warning(f"Highlight ignorado: summary vazio")
+                    continue
+
+                valid_highlights.append({
+                    "start": start,
+                    "end": end,
+                    "summary": summary
+                })
+
+            except (TypeError, ValueError) as e:
+                logger.warning(f"Highlight invalido ignorado: {highlight} - {e}")
                 continue
-            parts = getattr(content, "parts", None) or []
-            for part in parts:
-                part_text = getattr(part, "text", None)
-                if part_text:
-                    return str(part_text).strip()
 
-        raise RuntimeError("Resposta do Gemini sem texto")
+        if not valid_highlights:
+            logger.warning("Nenhum highlight valido retornado pelo OpenRouter")
+            total_duration = 75.0
+            if transcription.segments:
+                total_duration = max(seg.end for seg in transcription.segments)
 
-    @staticmethod
-    def _extract_json(response_text: str) -> Dict:
-        try:
-            return json.loads(response_text)
-        except json.JSONDecodeError:
-            start = response_text.find("{")
-            end = response_text.rfind("}")
-            if start == -1 or end == -1:
-                raise
-            subset = response_text[start : end + 1]
-            return json.loads(subset)
+            fallback_end = min(75.0, total_duration)
+            if fallback_end < 60.0:
+                fallback_end = min(60.0, total_duration)
 
+            return [{
+                "start": 0.0,
+                "end": fallback_end,
+                "summary": "Trecho inicial do video (fallback automatico)"
+            }]
 
-class OpenRouterCopywriter:
-    def __init__(self, settings: Settings) -> None:
-        if not settings.openrouter.api_key:
-            raise RuntimeError("OPENROUTER_API_KEY nao foi definido")
-        self.settings = settings
+        logger.info(f"OpenRouter retornou {len(valid_highlights)} highlights validos")
+        return valid_highlights
 
     def generate_titles(self, highlights: List[Dict]) -> List[str]:
         if not highlights:

video_render/media.py

@@ -35,11 +35,29 @@ class MediaPreparer:
         sanitized_name = sanitize_filename(Path(filename).stem)
         workspace_dir = ensure_workspace(self.settings.videos_dir, sanitized_name)
 
+        transcription_json = workspace_dir / "transcription.json"
+        transcription_txt = workspace_dir / "transcription.txt"
+        temp_transcription_json = None
+        temp_transcription_txt = None
+
+        if transcription_json.exists():
+            temp_transcription_json = workspace_dir.parent / f".{sanitized_name}_transcription.json.tmp"
+            shutil.copy2(transcription_json, temp_transcription_json)
+        if transcription_txt.exists():
+            temp_transcription_txt = workspace_dir.parent / f".{sanitized_name}_transcription.txt.tmp"
+            shutil.copy2(transcription_txt, temp_transcription_txt)
+
         existing_children = list(workspace_dir.iterdir())
         if existing_children:
             logger.info("Limpando workspace existente para %s", sanitized_name)
             remove_paths(existing_children)
 
+        if temp_transcription_json and temp_transcription_json.exists():
+            shutil.move(str(temp_transcription_json), str(transcription_json))
+            logger.info("Transcrição preservada em %s", transcription_json)
+        if temp_transcription_txt and temp_transcription_txt.exists():
+            shutil.move(str(temp_transcription_txt), str(transcription_txt))
+
         destination_name = f"{sanitized_name}{source_path.suffix.lower()}"
         working_video_path = workspace_dir / destination_name
         shutil.copy2(source_path, working_video_path)

video_render/pipeline.py

@@ -6,7 +6,7 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 from video_render.config import Settings
-from video_render.llm import GeminiHighlighter, OpenRouterCopywriter
+from video_render.llm import OpenRouterCopywriter
 from video_render.media import MediaPreparer, VideoWorkspace
 from video_render.transcription import TranscriptionResult, TranscriptionService
 from video_render.utils import remove_paths, sanitize_filename
@@ -55,8 +55,7 @@ class VideoPipeline:
         self.settings = settings
         self.media_preparer = MediaPreparer(settings)
         self.transcriber = TranscriptionService(settings)
-        self.highlighter = GeminiHighlighter(settings)
-        self.copywriter = OpenRouterCopywriter(settings)
+        self.llm_service = OpenRouterCopywriter(settings)  # Using OpenRouter for both highlights and titles
         self.renderer = VideoRenderer(settings)
 
     def process_message(self, message: Dict[str, Any]) -> Dict[str, Any]:
@@ -65,12 +64,11 @@ class VideoPipeline:
             self._prepare_workspace(context)
             self._generate_transcription(context)
             self._determine_highlights(context)
-            self._generate_titles(context)
             self._render_clips(context)
+
             return self._build_success_payload(context)
         except Exception as exc:
             logger.exception("Falha ao processar vídeo %s", context.job.filename)
-            # return self._handle_failure(context, exc)
 
     def _parse_job(self, message: Dict[str, Any]) -> JobMessage:
         filename = message.get("filename")
@@ -102,7 +100,10 @@ class VideoPipeline:
             context.transcription = existing
             return
 
-        transcription = self.transcriber.transcribe(context.workspace.audio_path)
+        transcription = self.transcriber.transcribe(
+            context.workspace.audio_path,
+            output_dir=context.workspace.workspace_dir
+        )
         TranscriptionService.persist(transcription, context.workspace.workspace_dir)
         context.transcription = transcription
 
@@ -111,10 +112,10 @@ class VideoPipeline:
             raise RuntimeError("Transcricao nao disponivel")
 
         try:
-            highlights_raw = self.highlighter.generate_highlights(context.transcription)
+            highlights_raw = self.llm_service.generate_highlights(context.transcription)
         except Exception:
             logger.exception(
-                "Falha ao gerar destaques com Gemini; aplicando fallback padrao."
+                "Falha ao gerar destaques com OpenRouter; aplicando fallback padrao."
             )
             context.highlight_windows = [self._build_fallback_highlight(context)]
             return
@@ -130,11 +131,13 @@ class VideoPipeline:
                 continue
 
             summary = str(item.get("summary", "")).strip()
+            title = str(item.get("title", summary[:60])).strip()
+
             if end <= start:
                 logger.debug("Highlight com intervalo invalido ignorado: %s", item)
                 continue
 
-            windows.append(HighlightWindow(start=start, end=end, summary=summary))
+            windows.append(HighlightWindow(start=start, end=end, summary=summary, title=title))
 
         if not windows:
             windows.append(self._build_fallback_highlight(context))
@@ -142,17 +145,12 @@ class VideoPipeline:
         context.highlight_windows = windows
 
     def _generate_titles(self, context: PipelineContext) -> None:
-        if not context.highlight_windows:
-            return
+        """DEPRECATED: Titles are now generated together with highlights.
 
-        highlight_dicts = [
-            {"start": window.start, "end": window.end, "summary": window.summary}
-            for window in context.highlight_windows
-        ]
-        titles = self.copywriter.generate_titles(highlight_dicts)
-
-        for window, title in zip(context.highlight_windows, titles):
-            window.title = title.strip()
+        This method is kept for backwards compatibility but does nothing.
+        Titles are extracted from highlights in _determine_highlights().
+        """
+        pass
 
     def _build_fallback_highlight(self, context: PipelineContext) -> HighlightWindow:
         if not context.transcription:
@@ -167,6 +165,7 @@ class VideoPipeline:
             start=0.0,
             end=max(last_end, 10.0),
             summary="Sem destaque identificado; fallback automatico.",
+            title="Confira este momento",
         )
 
     def _render_clips(self, context: PipelineContext) -> None:

video_render/rendering.py

@@ -15,6 +15,7 @@ from PIL import Image, ImageColor, ImageDraw, ImageFont
 
 from video_render.config import Settings
 from video_render.transcription import TranscriptionResult, WordTiming
+from video_render.smart_framing import SmartFramer, extract_audio_samples
 
 logger = logging.getLogger(__name__)
 
@@ -54,7 +55,41 @@ class CaptionBuilder:
         self.space_width = self.font.getbbox(" ")[2] - self.font.getbbox(" ")[0]
 
     def build(self, words: Sequence[WordTiming], clip_start: float) -> List[CaptionClipSet]:
-        grouped = self._group_words(words)
+        # Filter out empty, whitespace-only, or very short words (likely noise)
+        valid_words = [
+            w for w in words
+            if w.word
+            and w.word.strip()
+            and len(w.word.strip()) >= 2  # At least 2 characters
+            and not w.word.strip() in ['...', '..', '.', ',', '-', 'hmm', 'hm', 'ah', 'eh', 'uh']  # Not just punctuation or filler
+        ]
+
+        # Note: We don't filter out words based on gaps here
+        # Gap detection is handled in _group_words_with_gaps
+        # This ensures captions disappear during silence naturally
+        filtered_words = valid_words
+
+        # Calculate speech density (words per second)
+        # If density is too low, it's likely just noise/silence being misinterpreted
+        if filtered_words:
+            first_word_time = filtered_words[0].start
+            last_word_time = filtered_words[-1].end
+            duration = last_word_time - first_word_time
+
+            if duration > 0:
+                words_per_second = len(filtered_words) / duration
+                # Typical speech is 2-3 words per second
+                # If less than 0.5 words/second, it's probably silence/noise
+                if words_per_second < 0.5:
+                    logger.debug(f"Captions suprimidas: densidade muito baixa ({words_per_second:.2f} palavras/seg)")
+                    return []
+
+        # Only show captions if we have at least 3 valid words (reduced from 5 for 2-word groups)
+        # This prevents showing captions for noise/mumbling
+        if len(filtered_words) < 3:
+            return []
+
+        grouped = self._group_words_with_gaps(filtered_words)
         clip_sets: List[CaptionClipSet] = []
 
         for group in grouped:
@@ -101,6 +136,92 @@ class CaptionBuilder:
         if len(widths) > 1:
             total_width += self.space_width * (len(widths) - 1)
 
+        # Check if text needs to wrap to multiple lines
+        # If total width exceeds canvas width, break into 2 lines
+        needs_wrap = total_width > self.canvas_width
+
+        if needs_wrap:
+            # Split into 2 lines - try to balance the lines
+            mid_point = len(texts) // 2
+            line1_texts = texts[:mid_point]
+            line2_texts = texts[mid_point:]
+            line1_widths = widths[:mid_point]
+            line2_widths = widths[mid_point:]
+
+            # Calculate widths for each line
+            line1_width = sum(line1_widths)
+            if len(line1_widths) > 1:
+                line1_width += self.space_width * (len(line1_widths) - 1)
+
+            line2_width = sum(line2_widths)
+            if len(line2_widths) > 1:
+                line2_width += self.space_width * (len(line2_widths) - 1)
+
+            # Double the canvas height for 2 lines
+            canvas_height = self.canvas_height * 2
+            base_image = Image.new("RGBA", (self.canvas_width, canvas_height), (0, 0, 0, 0))
+            base_draw = ImageDraw.Draw(base_image)
+            highlight_images: List[Image.Image] = []
+
+            # Stroke settings: 8px black stroke for better readability
+            stroke_width = 8
+            stroke_color = (0, 0, 0, 255)  # Black
+
+            # Draw line 1
+            x = max(0, (self.canvas_width - line1_width) // 2)
+            y = self.baseline
+            for i, (text, width) in enumerate(zip(line1_texts, line1_widths)):
+                base_draw.text(
+                    (x, y),
+                    text,
+                    font=self.font,
+                    fill=self.base_color,
+                    stroke_width=stroke_width,
+                    stroke_fill=stroke_color
+                )
+
+                highlight_image = Image.new("RGBA", base_image.size, (0, 0, 0, 0))
+                highlight_draw = ImageDraw.Draw(highlight_image)
+                highlight_draw.text(
+                    (x, y),
+                    text,
+                    font=self.font,
+                    fill=self.highlight_color,
+                    stroke_width=stroke_width,
+                    stroke_fill=stroke_color
+                )
+                highlight_images.append(highlight_image)
+                x += width + self.space_width
+
+            # Draw line 2
+            x = max(0, (self.canvas_width - line2_width) // 2)
+            y = self.baseline + self.text_height + 5  # 5px spacing between lines
+            for i, (text, width) in enumerate(zip(line2_texts, line2_widths)):
+                base_draw.text(
+                    (x, y),
+                    text,
+                    font=self.font,
+                    fill=self.base_color,
+                    stroke_width=stroke_width,
+                    stroke_fill=stroke_color
+                )
+
+                highlight_image = Image.new("RGBA", base_image.size, (0, 0, 0, 0))
+                highlight_draw = ImageDraw.Draw(highlight_image)
+                highlight_draw.text(
+                    (x, y),
+                    text,
+                    font=self.font,
+                    fill=self.highlight_color,
+                    stroke_width=stroke_width,
+                    stroke_fill=stroke_color
+                )
+                highlight_images.append(highlight_image)
+                x += width + self.space_width
+
+            return base_image, highlight_images
+
+        # Single line rendering (original code)
         start_x = max(0, (self.canvas_width - total_width) // 2)
 
         base_image = Image.new("RGBA", (self.canvas_width, self.canvas_height), (0, 0, 0, 0))
@@ -108,13 +229,31 @@ class CaptionBuilder:
         highlight_images: List[Image.Image] = []
         x = start_x
 
-        for text, width in zip(texts, widths):
-            base_draw.text((x, self.baseline), text, font=self.font, fill=self.base_color)
+        # Stroke settings: 8px black stroke for better readability
+        stroke_width = 8
+        stroke_color = (0, 0, 0, 255)  # Black
 
+        for text, width in zip(texts, widths):
+            # Draw base text with stroke
+            base_draw.text(
+                (x, self.baseline),
+                text,
+                font=self.font,
+                fill=self.base_color,
+                stroke_width=stroke_width,
+                stroke_fill=stroke_color
+            )
+
+            # Draw highlight text with stroke
             highlight_image = Image.new("RGBA", base_image.size, (0, 0, 0, 0))
             highlight_draw = ImageDraw.Draw(highlight_image)
             highlight_draw.text(
-                (x, self.baseline), text, font=self.font, fill=self.highlight_color
+                (x, self.baseline),
+                text,
+                font=self.font,
+                fill=self.highlight_color,
+                stroke_width=stroke_width,
+                stroke_fill=stroke_color
             )
             highlight_images.append(highlight_image)
 
@@ -153,6 +292,44 @@ class CaptionBuilder:
 
         return grouped
 
+    def _group_words_with_gaps(self, words: Sequence[WordTiming]) -> List[List[WordTiming]]:
+        """
+        Group words into 2-word chunks, respecting silence gaps.
+        Creates natural breaks where there are pauses > 1.5s
+        """
+        if not words:
+            return []
+
+        grouped: List[List[WordTiming]] = []
+        buffer: List[WordTiming] = []
+
+        for i, word in enumerate(words):
+            # Check if there's a long pause before this word
+            if i > 0:
+                gap = word.start - words[i-1].end
+                # If gap > 1.5s, finish current buffer and start new group
+                if gap > 1.5:
+                    if buffer:
+                        grouped.append(buffer)
+                        buffer = []
+
+            buffer.append(word)
+
+            # Group into 2 words maximum
+            if len(buffer) == 2:
+                grouped.append(buffer)
+                buffer = []
+
+        # Handle remaining words
+        if buffer:
+            if len(buffer) == 1 and grouped:
+                # Add single remaining word to last group
+                grouped[-1].append(buffer[0])
+            else:
+                grouped.append(buffer)
+
+        return [grp for grp in grouped if grp]
+
     @staticmethod
     def _clean_word(text: str) -> str:
         text = text.strip()
@@ -164,6 +341,12 @@ class VideoRenderer:
     def __init__(self, settings: Settings) -> None:
         self.settings = settings
         self.captions = CaptionBuilder(settings)
+        self.smart_framer = SmartFramer(
+            target_width=settings.rendering.frame_width,
+            target_height=settings.rendering.frame_height,
+            frame_skip=settings.rendering.smart_framing_frame_skip,
+            smoothing_window=settings.rendering.smart_framing_smoothing_window
+        )
 
     def render(
         self,
@@ -234,26 +417,100 @@ class VideoRenderer:
         duration = end - start
         frame_w = self.settings.rendering.frame_width
         frame_h = self.settings.rendering.frame_height
-        top_h = int(frame_h * 0.18)
+        # Removed top panel - no longer showing title
         bottom_h = int(frame_h * 0.20)
-        video_area_h = max(1, frame_h - top_h - bottom_h)
 
-        scale_factor = min(
-            frame_w / subclip.w,
-            video_area_h / subclip.h,
-        )
-        resized_clip = subclip.resized(scale_factor)
-        video_y = top_h + (video_area_h - resized_clip.h) // 2
-        video_clip = resized_clip.with_position(
-            ((frame_w - resized_clip.w) // 2, video_y)
-        )
+        # Use smart framing to create intelligent 9:16 video (if enabled)
+        if self.settings.rendering.enable_smart_framing:
+            logger.info(f"Creating smart framing plan for clip {index} ({start:.2f}s - {end:.2f}s)")
+
+            try:
+                # Extract audio for speech detection
+                audio_samples = extract_audio_samples(source_path, start, end)
+
+                # Create framing plan
+                framing_plan = self.smart_framer.create_framing_plan(
+                    video_path=source_path,
+                    start_time=start,
+                    end_time=end,
+                    audio_samples=audio_samples
+                )
+
+                # Apply smart framing based on detected layout
+                use_split_screen = framing_plan.layout_mode in ["dual_split", "grid"]
+                video_clip = self.smart_framer.apply_framing(
+                    video_clip=subclip,
+                    framing_plan=framing_plan,
+                    use_split_screen=use_split_screen
+                )
+
+                logger.info(f"Smart framing applied: layout={framing_plan.layout_mode}, "
+                           f"faces_detected={len(framing_plan.frame_contexts[0].detected_faces) if framing_plan.frame_contexts else 0}")
+
+            except Exception as exc:
+                logger.warning(f"Smart framing failed for clip {index}, falling back to center crop: {exc}", exc_info=True)
+
+                # Fallback to center crop (maintains aspect ratio, crops to fit)
+                video_area_h = max(1, frame_h - bottom_h)
+
+                # Use MAX to ensure video covers entire area (will crop excess)
+                scale_factor = max(
+                    frame_w / subclip.w,
+                    video_area_h / subclip.h,
+                )
+
+                # Resize to cover area
+                resized_clip = subclip.resized(scale_factor)
+
+                # Calculate crop region (center crop)
+                crop_x1 = max(0, (resized_clip.w - frame_w) // 2)
+                crop_y1 = max(0, (resized_clip.h - video_area_h) // 2)
+                crop_x2 = crop_x1 + frame_w
+                crop_y2 = crop_y1 + video_area_h
+
+                # Crop to fit target dimensions using MoviePy crop(x1, y1, x2, y2)
+                cropped_clip = resized_clip.cropped(
+                    x1=crop_x1,
+                    y1=crop_y1,
+                    x2=crop_x2,
+                    y2=crop_y2
+                )
+
+                video_clip = cropped_clip.with_position((0, 0))
+                resized_clip.close()
+        else:
+            # Use center crop (smart framing disabled)
+            logger.info(f"Using center crop for clip {index} (smart framing disabled)")
+            video_area_h = max(1, frame_h - bottom_h)
+
+            # Use MAX to ensure video covers entire area (will crop excess)
+            scale_factor = max(
+                frame_w / subclip.w,
+                video_area_h / subclip.h,
+            )
+
+            # Resize to cover area
+            resized_clip = subclip.resized(scale_factor)
+
+            # Calculate crop region (center crop)
+            crop_x1 = max(0, (resized_clip.w - frame_w) // 2)
+            crop_y1 = max(0, (resized_clip.h - video_area_h) // 2)
+            crop_x2 = crop_x1 + frame_w
+            crop_y2 = crop_y1 + video_area_h
+
+            # Crop to fit target dimensions using MoviePy crop(x1, y1, x2, y2)
+            cropped_clip = resized_clip.cropped(
+                x1=crop_x1,
+                y1=crop_y1,
+                x2=crop_x2,
+                y2=crop_y2
+            )
+
+            video_clip = cropped_clip.with_position((0, 0))
+            resized_clip.close()
 
         background = ColorClip(size=(frame_w, frame_h), color=(0, 0, 0)).with_duration(duration)
-        top_panel = (
-            ColorClip(size=(frame_w, top_h), color=(12, 12, 12))
-            .with_duration(duration)
-            .with_opacity(0.85)
-        )
+        # Removed top panel and title - no longer needed
         bottom_panel = (
             ColorClip(size=(frame_w, bottom_h), color=(12, 12, 12))
             .with_position((0, frame_h - bottom_h))
@@ -261,34 +518,42 @@ class VideoRenderer:
             .with_opacity(0.85)
         )
 
-        title_clip = self._build_title_clip(
-            title=title,
-            summary=summary,
-            duration=duration,
-            frame_width=frame_w,
-            top_panel_height=top_h,
-        )
-        title_clip = title_clip.with_position(
-            ((frame_w - title_clip.w) // 2, (top_h - title_clip.h) // 2)
-        )
-
         words = self._collect_words(transcription, start, end)
-        caption_sets = self.captions.build(words, clip_start=start)
+
+        # Calculate speech coverage: how much of the clip has actual speech?
+        # If less than 30% of the clip has speech, don't show captions
+        clip_duration = end - start
+        if words and clip_duration > 0:
+            # Calculate total time with speech
+            total_speech_time = sum(w.end - w.start for w in words)
+            speech_coverage = total_speech_time / clip_duration
+
+            if speech_coverage < 0.3:  # Less than 30% speech
+                logger.debug(f"Captions suprimidas: cobertura de fala baixa ({speech_coverage:.1%})")
+                words = []  # Clear words to prevent captions
+
+        # Only build captions if there are actual words to display
+        # This prevents empty/placeholder captions from appearing
+        caption_sets = self.captions.build(words, clip_start=start) if words else []
 
         caption_clips = []
         caption_resources: List[ImageClip] = []
-        caption_area_top = frame_h - bottom_h
-        caption_area_height = bottom_h
+
+        # Position captions 120px below center (for 1920px height, center is 960px, so 1080px)
+        # This ensures they're visible, well-positioned, and don't interfere with faces
+        # Range: 100-150px as requested, using 120px for optimal positioning
+        center_y = frame_h // 2
+        caption_y = center_y + 120
         caption_margin = 20
-        raw_caption_y = caption_area_top + (caption_area_height - self.captions.canvas_height) // 2
-        min_caption_y = caption_area_top + caption_margin
-        max_caption_y = (
-            caption_area_top + caption_area_height - self.captions.canvas_height - caption_margin
-        )
+
+        # Ensure captions stay within reasonable bounds (no top panel now)
+        min_caption_y = caption_margin
+        max_caption_y = frame_h - bottom_h - self.captions.canvas_height - caption_margin
+
         if max_caption_y < min_caption_y:
             caption_y = min_caption_y
         else:
-            caption_y = min(max(raw_caption_y, min_caption_y), max_caption_y)
+            caption_y = min(max(caption_y, min_caption_y), max_caption_y)
 
         for clip_set in caption_sets:
             base_positioned = clip_set.base.with_position(("center", caption_y))
@@ -299,30 +564,20 @@ class VideoRenderer:
                 caption_clips.append(positioned)
                 caption_resources.append(highlight)
 
-        if not caption_clips:
-            fallback_text = self._wrap_text(summary or title, max_width=frame_w - 160)
-            caption_clips.append(
-                self._make_textclip(
-                    text=fallback_text,
-                    font_path=self.settings.rendering.font_path,
-                    font_size=self.settings.rendering.subtitle_font_size,
-                    color=self.settings.rendering.base_color,
-                    size=(frame_w - 160, max(40, self.captions.canvas_height)),
-                )
-                .with_duration(duration)
-                .with_position(("center", caption_y))
-            )
+        # No fallback captions - if there are no dynamic captions, show nothing
+        # This matches Opus Clip behavior where captions only appear when there's actual speech
 
         audio_clip, audio_needs_close = self._materialize_audio(
             source_path=source_path,
             start=start,
             end=end,
             duration=duration,
-            fallback_audio=video_clip.audio or resized_clip.audio or subclip.audio,
+            fallback_audio=video_clip.audio or subclip.audio,
         )
 
+        # Composite with background, bottom panel, video, and captions only (no top panel or title)
         composite = CompositeVideoClip(
-            [background, top_panel, bottom_panel, video_clip, title_clip, *caption_clips],
+            [background, bottom_panel, video_clip, *caption_clips],
             size=(frame_w, frame_h),
         )
         if audio_clip is not None:
@@ -337,11 +592,8 @@ class VideoRenderer:
         )
 
         composite.close()
-        resized_clip.close()
         video_clip.close()
-        title_clip.close()
         background.close()
-        top_panel.close()
         bottom_panel.close()
         for clip in caption_clips:
             clip.close()
@@ -352,95 +604,6 @@ class VideoRenderer:
 
         return str(output_path)
 
-    def _build_title_clip(
-        self,
-        *,
-        title: str,
-        summary: str,
-        duration: float,
-        frame_width: int,
-        top_panel_height: int,
-    ) -> ImageClip:
-        text = (title or summary or "").strip()
-        if not text:
-            text = summary or ""
-
-        max_width = max(200, frame_width - 160)
-        font_size = self.settings.rendering.title_font_size
-        min_font_size = max(28, int(font_size * 0.6))
-        target_height = max(80, top_panel_height - 40)
-        title_color = ImageColor.getrgb(self.settings.rendering.base_color)
-        font_path = self.settings.rendering.font_path
-
-        while True:
-            font = ImageFont.truetype(str(font_path), font_size)
-            lines = self._split_title_lines(text, font, max_width)
-            line_height = font.getbbox("Ay")[3] - font.getbbox("Ay")[1]
-            spacing = max(4, int(line_height * 0.25))
-            text_height = self._measure_text_height(len(lines), line_height, spacing)
-
-            if text_height <= target_height or font_size <= min_font_size:
-                break
-
-            font_size = max(min_font_size, font_size - 6)
-
-        # Recompute dimensions with final font size to ensure consistency
-        font = ImageFont.truetype(str(font_path), font_size)
-        lines = self._split_title_lines(text, font, max_width)
-        line_height = font.getbbox("Ay")[3] - font.getbbox("Ay")[1]
-        spacing = max(4, int(line_height * 0.25))
-        text_height = self._measure_text_height(len(lines), line_height, spacing)
-        canvas_height = max(1, text_height)
-
-        image = Image.new("RGBA", (max_width, canvas_height), (0, 0, 0, 0))
-        draw = ImageDraw.Draw(image)
-        y = 0
-        for idx, line in enumerate(lines):
-            bbox = font.getbbox(line)
-            line_width = bbox[2] - bbox[0]
-            x = max(0, (max_width - line_width) // 2)
-            draw.text((x, y - bbox[1]), line, font=font, fill=title_color)
-            y += line_height
-            if idx < len(lines) - 1:
-                y += spacing
-
-        return ImageClip(np.array(image)).with_duration(duration)
-
-    @staticmethod
-    def _measure_text_height(line_count: int, line_height: int, spacing: int) -> int:
-        if line_count <= 0:
-            return line_height
-        return line_count * line_height + max(0, line_count - 1) * spacing
-
-    @staticmethod
-    def _split_title_lines(
-        text: str, font: ImageFont.FreeTypeFont, max_width: int
-    ) -> List[str]:
-        words = text.split()
-        if not words:
-            return [""]
-
-        lines: List[str] = []
-        current: List[str] = []
-        for word in words:
-            test_line = " ".join(current + [word]) if current else word
-            bbox = font.getbbox(test_line)
-            line_width = bbox[2] - bbox[0]
-            if line_width <= max_width or not current:
-                current.append(word)
-                if line_width > max_width and not current[:-1]:
-                    lines.append(" ".join(current))
-                    current = []
-                continue
-
-            lines.append(" ".join(current))
-            current = [word]
-
-        if current:
-            lines.append(" ".join(current))
-
-        return lines
-
     def _materialize_audio(
         self,
         *,

video_render/smart_framing.py

@@ -0,0 +1,687 @@
+"""
+Smart framing module for intelligent video cropping and composition.
+
+This module provides functionality to create 9:16 vertical videos with
+intelligent framing that follows the action and speakers.
+"""
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+
+import cv2
+import numpy as np
+from moviepy.video.VideoClip import VideoClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+from scipy import signal
+
+from video_render.context_detection import ContextAnalyzer, FrameContext, FaceDetection
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class CropRegion:
+    """Defines a crop region for a frame."""
+    x: int
+    y: int
+    width: int
+    height: int
+
+
+@dataclass
+class FramingPlan:
+    """Complete framing plan for a video segment."""
+    frame_contexts: List[FrameContext]
+    crop_regions: List[CropRegion]
+    layout_mode: str
+    fps: float
+
+
+class SmartFramer:
+    """Creates intelligent 9:16 framing for horizontal videos."""
+
+    def __init__(
+        self,
+        target_width: int = 1080,
+        target_height: int = 1920,
+        frame_skip: int = 2,
+        smoothing_window: int = 15
+    ):
+        self.target_width = target_width
+        self.target_height = target_height
+        self.target_aspect = target_height / target_width
+
+        # Performance parameters
+        self.frame_skip = frame_skip  # Process every Nth frame (CPU optimization)
+
+        # Smoothing parameters
+        self.smoothing_window = smoothing_window
+        self.max_velocity = 30  # pixels per frame (reduced for smoother transitions)
+
+        logger.info(f"Smart framer initialized (target: {target_width}x{target_height}, frame_skip={frame_skip})")
+
+    def create_framing_plan(
+        self,
+        video_path: str,
+        start_time: float,
+        end_time: float,
+        audio_samples: Optional[np.ndarray] = None
+    ) -> FramingPlan:
+        """
+        Analyze video and create a complete framing plan.
+
+        Args:
+            video_path: Path to video file
+            start_time: Start time in seconds
+            end_time: End time in seconds
+            audio_samples: Optional audio samples for speech detection
+
+        Returns:
+            FramingPlan with all frame contexts and crop regions
+        """
+        analyzer = ContextAnalyzer()
+
+        # Detect speaking periods from audio if available
+        speaking_periods = None
+        if audio_samples is not None:
+            speaking_periods = analyzer.audio_detector.detect_speaking_periods(audio_samples)
+
+        # Open video with error suppression for AV1 codec warnings
+        import os
+        os.environ['OPENCV_FFMPEG_CAPTURE_OPTIONS'] = 'loglevel;quiet'
+
+        cap = cv2.VideoCapture(video_path)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+
+        # Calculate frame range
+        start_frame = int(start_time * fps)
+        end_frame = int(end_time * fps)
+
+        # Set to start frame
+        cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
+
+        frame_contexts = []
+        frame_number = start_frame
+        processed_count = 0
+
+        logger.info(f"Analyzing frames {start_frame} to {end_frame} (fps={fps}, skip={self.frame_skip})")
+
+        while frame_number < end_frame:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            # Only process every Nth frame for performance (CPU optimization)
+            if processed_count % self.frame_skip == 0:
+                timestamp = frame_number / fps
+                context = analyzer.analyze_frame(frame, timestamp, frame_number, speaking_periods)
+                frame_contexts.append(context)
+
+            frame_number += 1
+            processed_count += 1
+
+        # Get video dimensions before releasing capture
+        source_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        source_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        cap.release()
+        analyzer.close()
+
+        # Determine overall layout mode (most common)
+        layout_modes = [ctx.layout_mode for ctx in frame_contexts]
+        if layout_modes:
+            overall_layout = max(set(layout_modes), key=layout_modes.count)
+        else:
+            overall_layout = "single"
+
+        # Calculate crop regions based on contexts
+
+        crop_regions = self._calculate_crop_regions(
+            frame_contexts,
+            source_width,
+            source_height
+        )
+
+        return FramingPlan(
+            frame_contexts=frame_contexts,
+            crop_regions=crop_regions,
+            layout_mode=overall_layout,
+            fps=fps
+        )
+
+    def _calculate_crop_regions(
+        self,
+        contexts: List[FrameContext],
+        source_width: int,
+        source_height: int
+    ) -> List[CropRegion]:
+        """
+        Calculate smooth crop regions for each frame.
+
+        Args:
+            contexts: List of frame contexts
+            source_width: Source video width
+            source_height: Source video height
+
+        Returns:
+            List of crop regions
+        """
+        if not contexts:
+            return []
+
+        # Calculate ideal crop dimensions maintaining EXACT 9:16 aspect ratio
+        source_aspect = source_width / source_height
+
+        if source_aspect > self.target_aspect:
+            # Source is wider - crop horizontally (use full height)
+            crop_height = source_height
+            crop_width = int(crop_height / self.target_aspect)
+
+            # Ensure crop width fits within source
+            if crop_width > source_width:
+                crop_width = source_width
+                crop_height = int(crop_width * self.target_aspect)
+        else:
+            # Source is taller - crop vertically (use full width)
+            crop_width = source_width
+            crop_height = int(crop_width * self.target_aspect)
+
+            # Ensure crop height fits within source
+            if crop_height > source_height:
+                crop_height = source_height
+                crop_width = int(crop_height / self.target_aspect)
+
+        # Calculate center points for each frame
+        # Since we now always focus on ONE person directly (not averaging),
+        # we can use the focus point directly without complex validation
+        center_xs = []
+        center_ys = []
+
+        for ctx in contexts:
+            if ctx.primary_focus:
+                # Primary focus is now always a single person's center, never averaged
+                # This means it will never be on the table/empty space
+                center_xs.append(ctx.primary_focus[0])
+                center_ys.append(ctx.primary_focus[1])
+            else:
+                # Default to center only if no faces detected at all
+                center_xs.append(source_width // 2)
+                center_ys.append(source_height // 2)
+
+        # Smooth the center points
+        if len(center_xs) > self.smoothing_window:
+            kernel_size = min(self.smoothing_window, len(center_xs))
+            if kernel_size % 2 == 0:
+                kernel_size -= 1
+
+            center_xs = signal.medfilt(center_xs, kernel_size=kernel_size).tolist()
+            center_ys = signal.medfilt(center_ys, kernel_size=kernel_size).tolist()
+
+        # Limit velocity (prevent jarring movements)
+        center_xs = self._limit_velocity(center_xs, self.max_velocity)
+        center_ys = self._limit_velocity(center_ys, self.max_velocity)
+
+        # Convert to crop regions
+        crop_regions = []
+        for center_x, center_y in zip(center_xs, center_ys):
+            # Calculate top-left corner
+            x = int(center_x - crop_width // 2)
+            y = int(center_y - crop_height // 2)
+
+            # Clamp to valid bounds
+            x = max(0, min(x, source_width - crop_width))
+            y = max(0, min(y, source_height - crop_height))
+
+            crop_regions.append(CropRegion(
+                x=x,
+                y=y,
+                width=crop_width,
+                height=crop_height
+            ))
+
+        return crop_regions
+
+    def _limit_velocity(self, positions: List[float], max_velocity: float) -> List[float]:
+        """
+        Limit the velocity of position changes.
+
+        Args:
+            positions: List of positions
+            max_velocity: Maximum allowed change per frame
+
+        Returns:
+            Smoothed positions
+        """
+        if len(positions) <= 1:
+            return positions
+
+        limited = [positions[0]]
+
+        for i in range(1, len(positions)):
+            delta = positions[i] - limited[i - 1]
+            if abs(delta) > max_velocity:
+                delta = max_velocity if delta > 0 else -max_velocity
+
+            limited.append(limited[i - 1] + delta)
+
+        return limited
+
+    def apply_framing(
+        self,
+        video_clip: VideoFileClip,
+        framing_plan: FramingPlan,
+        use_split_screen: bool = False
+    ) -> VideoClip:
+        """
+        Apply smart framing to a video clip.
+
+        Args:
+            video_clip: Source video clip
+            framing_plan: Framing plan to apply
+            use_split_screen: Whether to use split screen for multiple people
+
+        Returns:
+            Reframed video clip
+        """
+        # Handle different layout modes
+        if framing_plan.layout_mode in ["single", "single_speaker"]:
+            # Single person or single speaker - use focused single framing
+            return self._apply_single_framing(video_clip, framing_plan)
+        elif framing_plan.layout_mode == "dual_split" and use_split_screen:
+            # Two people in conversation - use split screen
+            return self._apply_split_screen(video_clip, framing_plan)
+        elif framing_plan.layout_mode == "grid" and use_split_screen:
+            # 3+ people - use grid layout
+            return self._apply_grid_layout(video_clip, framing_plan)
+        else:
+            # Fallback to single framing
+            return self._apply_single_framing(video_clip, framing_plan)
+
+    def _apply_single_framing(
+        self,
+        video_clip: VideoFileClip,
+        framing_plan: FramingPlan
+    ) -> VideoClip:
+        """
+        Apply single-focus framing (following one person or action).
+
+        Args:
+            video_clip: Source video clip
+            framing_plan: Framing plan
+
+        Returns:
+            Reframed video clip
+        """
+        def make_frame(t):
+            # Get the original frame
+            frame = video_clip.get_frame(t)
+
+            # Ensure we have valid crop regions
+            if not framing_plan.crop_regions:
+                # Fallback: return center crop
+                h, w = frame.shape[:2]
+                crop_h = int(w * self.target_aspect)
+                crop_w = w
+                if crop_h > h:
+                    crop_h = h
+                    crop_w = int(h / self.target_aspect)
+                y = (h - crop_h) // 2
+                x = (w - crop_w) // 2
+                cropped = frame[y:y + crop_h, x:x + crop_w]
+            else:
+                # Calculate exact frame index with decimal precision for interpolation
+                exact_frame_idx = (t * framing_plan.fps) / self.frame_skip
+
+                # Get the two adjacent analyzed frames
+                idx_floor = int(exact_frame_idx)
+                idx_ceil = idx_floor + 1
+
+                # Interpolation factor (0.0 to 1.0)
+                alpha = exact_frame_idx - idx_floor
+
+                # Clamp indices to valid range
+                idx_floor = max(0, min(idx_floor, len(framing_plan.crop_regions) - 1))
+                idx_ceil = max(0, min(idx_ceil, len(framing_plan.crop_regions) - 1))
+
+                # Get crop regions
+                crop1 = framing_plan.crop_regions[idx_floor]
+                crop2 = framing_plan.crop_regions[idx_ceil]
+
+                # Linear interpolation between crop regions
+                x = int(crop1.x * (1 - alpha) + crop2.x * alpha)
+                y = int(crop1.y * (1 - alpha) + crop2.y * alpha)
+                width = int(crop1.width * (1 - alpha) + crop2.width * alpha)
+                height = int(crop1.height * (1 - alpha) + crop2.height * alpha)
+
+                # Ensure crop stays within frame bounds
+                h, w = frame.shape[:2]
+                x = max(0, min(x, w - width))
+                y = max(0, min(y, h - height))
+                width = min(width, w - x)
+                height = min(height, h - y)
+
+                # Crop the frame
+                cropped = frame[y:y + height, x:x + width]
+
+            # Resize to target dimensions
+            resized = cv2.resize(
+                cropped,
+                (self.target_width, self.target_height),
+                interpolation=cv2.INTER_LINEAR
+            )
+
+            return resized
+
+        # MoviePy 2.x compatible way to create VideoClip
+        new_clip = VideoClip(duration=video_clip.duration)
+        new_clip.size = (self.target_width, self.target_height)
+        new_clip.frame_function = make_frame
+        return new_clip
+
+    def _apply_split_screen(
+        self,
+        video_clip: VideoFileClip,
+        framing_plan: FramingPlan
+    ) -> VideoClip:
+        """
+        Apply split screen for two people.
+
+        Args:
+            video_clip: Source video clip
+            framing_plan: Framing plan
+
+        Returns:
+            Split screen video clip
+        """
+        def make_frame(t):
+            frame = video_clip.get_frame(t)
+            # Calculate exact frame index with decimal precision for smooth interpolation
+            exact_frame_idx = (t * framing_plan.fps) / self.frame_skip
+            frame_idx = int(exact_frame_idx)
+
+            # Ensure we have valid contexts
+            if not framing_plan.frame_contexts:
+                # Fallback to simple center crop
+                h, w = frame.shape[:2]
+                crop_h = int(w * self.target_aspect)
+                crop_w = w
+                if crop_h > h:
+                    crop_h = h
+                    crop_w = int(h / self.target_aspect)
+                y = (h - crop_h) // 2
+                x = (w - crop_w) // 2
+                cropped = frame[y:y + crop_h, x:x + crop_w]
+                return cv2.resize(cropped, (self.target_width, self.target_height), interpolation=cv2.INTER_LINEAR)
+
+            # Clamp index to valid range
+            frame_idx = max(0, min(frame_idx, len(framing_plan.frame_contexts) - 1))
+            context = framing_plan.frame_contexts[frame_idx]
+
+            # Create output frame
+            output = np.zeros((self.target_height, self.target_width, 3), dtype=np.uint8)
+
+            if len(context.detected_faces) >= 2:
+                # Split vertically 50/50 (two columns)
+                half_width = self.target_width // 2
+
+                # Select the 2 most relevant faces
+                # Priority: ALWAYS show active speaker first + most confident other person
+                if context.active_speakers and len(context.active_speakers) >= 1:
+                    # Get the PRIMARY speaker (most confident among active speakers)
+                    speaker_faces = [context.detected_faces[i] for i in context.active_speakers
+                                   if i < len(context.detected_faces)]
+
+                    primary_speaker = max(speaker_faces, key=lambda f: f.confidence)
+
+                    # Get OTHER faces (not the primary speaker)
+                    other_faces = [f for f in context.detected_faces if f != primary_speaker]
+
+                    if len(speaker_faces) >= 2:
+                        # Multiple speakers: show primary + second most confident speaker
+                        other_speakers = [f for f in speaker_faces if f != primary_speaker]
+                        secondary_person = max(other_speakers, key=lambda f: f.confidence)
+                    elif other_faces:
+                        # One speaker: show speaker + most confident other person
+                        secondary_person = max(other_faces, key=lambda f: f.confidence)
+                    else:
+                        # Fallback: only one person detected
+                        secondary_person = primary_speaker
+
+                    selected_faces = [primary_speaker, secondary_person]
+                else:
+                    # No speakers: take 2 most confident faces
+                    selected_faces = sorted(context.detected_faces, key=lambda f: f.confidence, reverse=True)[:2]
+
+                # Sort selected faces by horizontal position for consistent left/right placement
+                faces = sorted(selected_faces, key=lambda f: f.center_x)
+                left_face = faces[0]
+                right_face = faces[1]
+
+                # Process each person's frame
+                for idx, face in enumerate([left_face, right_face]):
+                    # Calculate crop region focused on this person
+                    # Each person gets half the width, full target aspect ratio (9:16)
+                    # This ensures NO distortion when resizing
+
+                    # For split screen: each side is half_width x full_height
+                    # We need to maintain 9:16 aspect for each half
+                    half_width = self.target_width // 2
+                    half_aspect = self.target_height / half_width  # Aspect ratio for half
+
+                    # Determine crop size based on face with padding
+                    face_width = max(face.width, frame.shape[1] // 4)  # At least 1/4 of frame width
+                    crop_width = int(face_width * 2.5)  # Add padding around face
+                    crop_height = int(crop_width * half_aspect)  # Maintain correct aspect
+
+                    # Ensure crop fits in frame, maintaining aspect ratio
+                    max_crop_width = frame.shape[1] // 2  # Half the source width
+                    max_crop_height = frame.shape[0]  # Full source height
+
+                    # If crop is too wide, scale down proportionally
+                    if crop_width > max_crop_width:
+                        crop_width = max_crop_width
+                        crop_height = int(crop_width * half_aspect)
+
+                    # If crop is too tall, scale down proportionally
+                    if crop_height > max_crop_height:
+                        crop_height = max_crop_height
+                        crop_width = int(crop_height / half_aspect)
+
+                    # Center crop on face
+                    x = max(0, face.center_x - crop_width // 2)
+                    y = max(0, face.center_y - crop_height // 2)
+
+                    # Clamp to frame boundaries
+                    x = min(x, frame.shape[1] - crop_width)
+                    y = min(y, frame.shape[0] - crop_height)
+
+                    # Extract and resize crop
+                    cropped = frame[y:y + crop_height, x:x + crop_width]
+                    resized = cv2.resize(
+                        cropped,
+                        (half_width, self.target_height),
+                        interpolation=cv2.INTER_LINEAR
+                    )
+
+                    # Place in output at appropriate horizontal position
+                    x_offset = idx * half_width
+                    output[:, x_offset:x_offset + half_width] = resized
+            else:
+                # Fall back to single framing
+                if framing_plan.crop_regions:
+                    crop_idx = max(0, min(frame_idx, len(framing_plan.crop_regions) - 1))
+                    crop = framing_plan.crop_regions[crop_idx]
+                    cropped = frame[crop.y:crop.y + crop.height, crop.x:crop.x + crop.width]
+                else:
+                    # Fallback to center crop if no crop regions available
+                    h, w = frame.shape[:2]
+                    crop_h = int(w * self.target_aspect)
+                    crop_w = w
+                    if crop_h > h:
+                        crop_h = h
+                        crop_w = int(h / self.target_aspect)
+                    y = (h - crop_h) // 2
+                    x = (w - crop_w) // 2
+                    cropped = frame[y:y + crop_h, x:x + crop_w]
+                output = cv2.resize(
+                    cropped,
+                    (self.target_width, self.target_height),
+                    interpolation=cv2.INTER_LINEAR
+                )
+
+            return output
+
+        # MoviePy 2.x compatible way to create VideoClip
+        new_clip = VideoClip(duration=video_clip.duration)
+        new_clip.size = (self.target_width, self.target_height)
+        new_clip.frame_function = make_frame
+        return new_clip
+
+    def _apply_grid_layout(
+        self,
+        video_clip: VideoFileClip,
+        framing_plan: FramingPlan
+    ) -> VideoClip:
+        """
+        Apply grid layout for 3+ people.
+
+        Args:
+            video_clip: Source video clip
+            framing_plan: Framing plan
+
+        Returns:
+            Grid layout video clip
+        """
+        def make_frame(t):
+            frame = video_clip.get_frame(t)
+            # Calculate exact frame index with decimal precision for smooth interpolation
+            exact_frame_idx = (t * framing_plan.fps) / self.frame_skip
+            frame_idx = int(exact_frame_idx)
+
+            # Ensure we have valid contexts
+            if not framing_plan.frame_contexts:
+                # Fallback to simple center crop
+                h, w = frame.shape[:2]
+                crop_h = int(w * self.target_aspect)
+                crop_w = w
+                if crop_h > h:
+                    crop_h = h
+                    crop_w = int(h / self.target_aspect)
+                y = (h - crop_h) // 2
+                x = (w - crop_w) // 2
+                cropped = frame[y:y + crop_h, x:x + crop_w]
+                return cv2.resize(cropped, (self.target_width, self.target_height), interpolation=cv2.INTER_LINEAR)
+
+            # Clamp index to valid range
+            frame_idx = max(0, min(frame_idx, len(framing_plan.frame_contexts) - 1))
+            context = framing_plan.frame_contexts[frame_idx]
+
+            output = np.zeros((self.target_height, self.target_width, 3), dtype=np.uint8)
+
+            num_faces = len(context.detected_faces)
+
+            if num_faces >= 3:
+                # Create 2x2 grid
+                cell_width = self.target_width // 2
+                cell_height = self.target_height // 2
+
+                for idx, face in enumerate(context.detected_faces[:4]):
+                    # Calculate grid position
+                    row = idx // 2
+                    col = idx % 2
+
+                    # Each grid cell maintains aspect ratio (square in this case: cell_width = cell_height)
+                    cell_aspect = cell_height / cell_width
+
+                    # Crop around face with correct aspect ratio
+                    crop_width = frame.shape[1] // 2
+                    crop_height = int(crop_width * cell_aspect)
+
+                    # Ensure crop fits in frame, maintaining aspect
+                    max_crop_width = frame.shape[1] // 2
+                    max_crop_height = frame.shape[0] // 2
+
+                    if crop_width > max_crop_width:
+                        crop_width = max_crop_width
+                        crop_height = int(crop_width * cell_aspect)
+
+                    if crop_height > max_crop_height:
+                        crop_height = max_crop_height
+                        crop_width = int(crop_height / cell_aspect)
+
+                    # Center crop on face
+                    x = max(0, face.center_x - crop_width // 2)
+                    y = max(0, face.center_y - crop_height // 2)
+
+                    # Clamp to frame boundaries
+                    x = min(x, frame.shape[1] - crop_width)
+                    y = min(y, frame.shape[0] - crop_height)
+
+                    cropped = frame[y:y + crop_height, x:x + crop_width]
+                    resized = cv2.resize(
+                        cropped,
+                        (cell_width, cell_height),
+                        interpolation=cv2.INTER_LINEAR
+                    )
+
+                    # Place in grid
+                    y_offset = row * cell_height
+                    x_offset = col * cell_width
+                    output[y_offset:y_offset + cell_height, x_offset:x_offset + cell_width] = resized
+            else:
+                # Fall back to single framing
+                if framing_plan.crop_regions:
+                    crop_idx = max(0, min(frame_idx, len(framing_plan.crop_regions) - 1))
+                    crop = framing_plan.crop_regions[crop_idx]
+                    cropped = frame[crop.y:crop.y + crop.height, crop.x:crop.x + crop.width]
+                else:
+                    # Fallback to center crop if no crop regions available
+                    h, w = frame.shape[:2]
+                    crop_h = int(w * self.target_aspect)
+                    crop_w = w
+                    if crop_h > h:
+                        crop_h = h
+                        crop_w = int(h / self.target_aspect)
+                    y = (h - crop_h) // 2
+                    x = (w - crop_w) // 2
+                    cropped = frame[y:y + crop_h, x:x + crop_w]
+                output = cv2.resize(
+                    cropped,
+                    (self.target_width, self.target_height),
+                    interpolation=cv2.INTER_LINEAR
+                )
+
+            return output
+
+        # MoviePy 2.x compatible way to create VideoClip
+        new_clip = VideoClip(duration=video_clip.duration)
+        new_clip.size = (self.target_width, self.target_height)
+        new_clip.frame_function = make_frame
+        return new_clip
+
+
+def extract_audio_samples(video_path: str, start_time: float, end_time: float) -> Optional[np.ndarray]:
+    """
+    Extract audio samples from video for speech detection.
+
+    Args:
+        video_path: Path to video file
+        start_time: Start time in seconds
+        end_time: End time in seconds
+
+    Returns:
+        Audio samples array or None if no audio
+    """
+    try:
+        from moviepy.audio.io.AudioFileClip import AudioFileClip
+
+        with AudioFileClip(video_path) as audio:
+            segment = audio.subclipped(start_time, end_time)
+            fps = getattr(segment, 'fps', 44100)
+            samples = segment.to_soundarray(fps=fps)
+            return samples
+    except Exception as exc:
+        logger.warning(f"Failed to extract audio: {exc}")
+        return None

video_render/transcription.py

@@ -56,7 +56,14 @@ class TranscriptionService:
             )
         return self._model
 
-    def transcribe(self, audio_path: Path) -> TranscriptionResult:
+    def transcribe(self, audio_path: Path, output_dir: Optional[Path] = None) -> TranscriptionResult:
+        if output_dir is not None:
+            existing_transcription = self.load(output_dir)
+            if existing_transcription is not None:
+                logger.info("Transcrição já existe em %s, reutilizando...", output_dir)
+                return existing_transcription
+
+        logger.info("Iniciando transcrição do áudio com FasterWhisper...")
         model = self._load_model()
         segments, _ = model.transcribe(
             str(audio_path),