Some context the video can't fit. Pipeline: WhisperX for transcription, an LLM writes the script with timestamps, CLIP plus a vision model picks the shots that actually match each line, Qwen3-TTS narrates, ffmpeg renders 9:16 with burned-in subtitles. Runs on a rented GPU, about $0.20 and 7 minutes per video. The parts nobody warns you about: The narrator literally laughed mid-sentence during a funeral scene. Whisper writes [laughs] into the transcript and the TTS performs it. Had to strip those annotations before the script step. Scene matching. V1 picked random pretty frames. Now it anchors on the timestamp, verifies with CLIP, then re-ranks with a vision model. Loudness. The hook was always quieter than the body. Fixed with two-pass ffmpeg loudnorm. 60 seconds is a brutal budget. 11 lines max, and long lines get split. 279 people tried it, 0 paying so far. Free tier is 1 video, no card. What would you fix first: the voice, the scene matching, or the pacing? Video is compressed hard, so sorry for the quality. https://reddit.com/link/1xh5je6/video/klfmn6bhw2yh1/player   submitted by   /u/aivideorecap [link]   [comments]