SRTGen vs Whisper

SRTGen is a web and API platform for creating and localizing subtitles for video and audio. It transcribes uploads into editable, time-coded text, then lets users review captions, translate them, style them or burn them into video. Subtitle exports include SRT, VTT, ASS and TXT; JSON is available through agent localization. The service supports 100 caption languages and a workflow combining transcription, translation, voice dubbing, review and export. Its stated content range includes social videos, podcasts, courses, marketing videos and news. The free plan includes 20 signup credits, 20 transcription minutes and 40 translation minutes, with recording capped at five minutes and 720p. Paid plans start at $4 per month for the first two months, then $8 per month. Monthly or yearly billing and non-expiring top-ups are offered. SRTGen also supports MCP-compatible agents, collaborative workspaces and a REST developer API on higher plans.

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.