Captions vs Whisper

Captions is an AI video platform for creating and editing videos from footage or prompts. AI Edit can apply cuts, captions, music, and formatting to uploaded material. The service offers automatic transcription, more than 100 caption styles, and translation into over 100 languages. Users can dub videos into more than 30 languages with a cloned version of their voice. Prompt to Video creates talking-head videos from scripts using built-in actors or an AI Twin. The platform is aimed at creators, marketers, and developers making materials such as ads, listing videos, and podcast clips. Its Mirage API generates content from scripts, image references, or actor IDs and uses credit-based billing. The free plan costs nothing and includes no AI usage credits, one caption template, and basic editing tools. Max costs $24.99 a month and includes 500 monthly credits; Frontier plans provide higher credit allowances. Unused credits can roll over for up to two extra months, capped at three times the monthly allowance. Captions is available on Android, iOS, and web, with an API offering.

Whisper is an open-source speech recognition model for transcription, speech translation, language identification, and voice activity detection. Its documented command-line and Python interfaces process audio files, and it can translate speech from multiple languages into English. The repository lists six model sizes—tiny, base, small, medium, large, and turbo—with different speed and memory tradeoffs; stated VRAM needs range from about 1 GB to 10 GB, depending on size. Installation is documented through pip and requires the ffmpeg command-line tool. Rust may be needed if a prebuilt tiktoken wheel is unavailable. The code and model weights are released under the MIT License, with local installation using Python and ffmpeg. Whisper is listed for Linux, macOS, Windows, and self-hosted deployment. OpenAI says it was trained on 680,000 hours of multilingual and multitask supervised data collected from the web. Performance varies widely by language, and it does not outperform models specialized for LibriSpeech. Turbo is not trained for translation; multilingual models are recommended for translating non-English speech into English.