subtutu

subtutu is a command-line tool that automatically generates SRT subtitle files from any video or audio file. It uses faster-whisper — a high-performance reimplementation of OpenAI Whisper — to transcribe spoken audio into accurate, timestamped subtitles up to 4x faster than the original Whisper on CPU.

No API key required. Everything runs locally on your machine.

subtutu lecture.mp4
# → lecture.srt

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Who is this for?

• Content creators who want subtitles for YouTube videos, reels, or podcasts
• Developers building subtitle pipelines
• Researchers transcribing interviews or recordings
• Anyone who needs fast, offline, accurate subtitles from a video file

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Features

• Generates standard .srt subtitle files ready for use in any video editor or player
• Powered by faster-whisper (CTranslate2) — 4x faster than openai-whisper on CPU, up to 12x on GPU
• Shows real-time transcription progress as segments are decoded
• Auto-selects the best model for your hardware (RAM + VRAM aware)
• Shows estimated processing time and accuracy for each model before starting
• Supports 99+ languages with automatic language detection
• Handles MP4, MOV, MKV, AVI, MP3, WAV, M4A, and any format ffmpeg can read
• Clear error messages for common problems (missing ffmpeg, no audio track, silent video, etc.)

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Requirements

• Python 3.9+
• ffmpeg — required for audio decoding

Install ffmpeg on macOS:

brew install ffmpeg

Install ffmpeg on Ubuntu/Debian:

sudo apt install ffmpeg

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Installation

pip install subtutu

No separate PyTorch install needed — subtutu uses CTranslate2 for inference.

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Usage

subtutu <video_or_audio_file> [options]

The subtitle file is written to the same directory as the input file by default. If a .srt already exists, a new file is created automatically (video_1.srt, video_2.srt, etc.).

subtutu video.mp4
# Output: video.srt

Options

Flag	Default	Description
--model	auto	Model: tiny, base, small, medium, large-v3, turbo, or auto to pick based on hardware
--language	en	Language code (e.g. en, fr, de, ja, zh). Use auto to detect automatically
--output	alongside input	Output .srt path or directory
--device	auto	Force compute device: cpu or cuda

Examples

# Subtitle an English video (default)
subtutu interview.mp4

# Use a more accurate model
subtutu documentary.mp4 --model medium

# Auto-detect the spoken language
subtutu foreign_film.mp4 --language auto

# Subtitle a French video
subtutu podcast.mp3 --language fr

# Save the subtitle file to a specific location
subtutu recording.mov --output ~/Desktop/recording.srt

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Choosing a model

When --model auto is used (the default), subtutu checks your available RAM and GPU memory, then shows a table like this before loading anything:

  Model        Accuracy    Est. time
  ────────────   ────────   ──────────
   tiny            60%         1m 2s
   base            75%         2m 5s
▶  small           85%         5m 33s
   medium          93%        16m 40s
   turbo           90%         4m 10s
   large-v3        97%        33m 20s

  Recommended: small
  Press Enter to use 'small', or type a model name:

Press Enter to accept, or type a different model name to switch.

Model	Size (int8)	CPU Speed	Accuracy
tiny	~75 MB	~120x real-time	60%
base	~145 MB	~60x real-time	75%
small	~490 MB	~24x real-time	85%
medium	~1.5 GB	~8x real-time	93%
turbo	~810 MB	~30x real-time	90%
large-v3	~3 GB	~4x real-time	97%

Models are downloaded on first use and cached in ~/.cache/huggingface/hub/.

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Supported file formats

Any format that ffmpeg can decode, including:

mp4 mov mkv avi webm flv m4v mp3 wav m4a aac ogg flac wma

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Troubleshooting

ffmpeg not found Install ffmpeg — see Requirements above.

No speech detected Try --language auto if the video is not in English. Check that the video actually has an audio track.

Not enough memory to load the model Switch to a smaller model: --model small or --model tiny.

Permission denied reading a file on macOS Terminal may need Full Disk Access: System Settings > Privacy & Security > Full Disk Access.

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License

MIT

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Acknowledgements

Built on faster-whisper by SYSTRAN. Whisper models by OpenAI. Audio decoding by ffmpeg.