SmartSub vs Whisper

SmartSub is an open-source desktop app for transcription, subtitle translation, proofreading, AI dubbing and video subtitle rendering. It runs on Windows, macOS and Linux, and is free with no local usage limits. For local processing, its maker says models process files on the user's computer, so those files do not leave the device. Transcription options include whisper.cpp, faster-whisper, FunASR, Qwen3-ASR and FireRedASR. Translation includes 20 services, including built-in free translation and integrations such as DeepSeek, Gemini and Ollama. The proofreading workspace offers video-synced editing, undo and redo, search and replace, and AI proofreading. SmartSub can generate speech from subtitle lines, align audio to the timeline and clone voices with ZipVoice, Volcengine or ElevenLabs. It can burn subtitles into video or package soft subtitles, preview styles and include dubbed audio. It also downloads videos from links using yt-dlp and lux, supporting more than 1,800 sites. Cloud transcription uploads audio to the configured provider endpoint; the README advises against using it for sensitive content.

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.