GetCaptions vs Whisper

GetCaptions automatically transcribes speech and creates, edits and exports video captions. Its transcription supports more than 100 languages with word-level timing. Users can style captions with animated templates, including karaoke, highlight, neon and pop designs, then translate finished captions into dozens of languages. A brand kit can apply a logo, colors and fonts, importing them from a website or YouTube channel. Videos can be exported with captions burned in, or subtitles can be downloaded as SRT or VTT files. MP4, MOV and WebM are listed upload formats. The free Starter plan includes 60 minutes of transcription per month, videos up to five minutes and three projects. Pro costs $15 a month and includes 500 transcription minutes monthly, videos up to 60 minutes and 50 projects. Studio costs $35 a month and adds one-click publishing to TikTok, Instagram and YouTube, API access and unlimited usage subject to fair use. Uploaded audio is transcribed on GetCaptions' servers rather than sent to a third-party transcription service. Video files are retained for reopening and editing until the project or account is deleted.

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.