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What you need
- A Mac with Python and pip available in a Python environment. Whisper’s README says the codebase is expected to be compatible with Python 3.8–3.11; check the current README if you use a newer Python version or installation fails.
- FFmpeg, which Whisper requires to read audio from media files.
- The video file you want to transcribe, and enough disk space for the model files Whisper downloads when needed.
OpenAI releases Whisper’s code and model weights under the MIT License, according to its README. That license does not determine whether you have rights to use the source video or its transcript.
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Install FFmpeg and Whisper
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Open Terminal and install FFmpeg with Homebrew:
brew install ffmpegThis is the macOS installation command documented in the Whisper README. If Homebrew is not installed, set it up separately before running this command.
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Activate the Python environment where you want Whisper installed, then run:
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pip install -U openai-whisperThe README also documents installing from GitHub. Use a Python version supported by the project’s current installation guidance.
Generate an SRT subtitle file
In Terminal, change to the folder containing your video, or provide its full path. Run this example to create an SRT:
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whisper "input-video.mp4" --model turbo --output_format srt
Replace input-video.mp4 with your file’s name or path. The command combines documented Whisper CLI options; it is an example, not a performance test. Whisper writes the subtitle output to its output directory, which defaults to the current working directory. To choose a different folder, add --output_dir followed by the folder path:
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whisper "input-video.mp4" --model turbo --output_format srt --output_dir "subtitles"
Whisper supports both SRT and VTT subtitle output. Choose the format accepted by the editor or platform where you plan to use the file:
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whisper "input-video.mp4" --model turbo --output_format vtt
The CLI also supports text, TSV, JSON, and JSONL output, but those are not subtitle formats. See the transcription implementation for CLI options.
Choose the model and language behavior
Transcribe speech in its original language
Whisper can detect the spoken language automatically. You can also specify it with --language if you know what is spoken. For example, to request English transcription explicitly:
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whisper "input-video.mp4" --model turbo --language English --output_format srt
The CLI defaults to the turbo model. OpenAI describes turbo as an optimized version of large-v3 intended to run faster with minimal accuracy degradation. No Mac-specific speed or accuracy guarantee is established, so processing time and recognition quality can vary.
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For translation into English, use a multilingual model such as medium or large and set --task translate:
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whisper "input-video.mp4" --model medium --task translate --output_format srt
Do not use turbo for this task: the README says it is not trained for translation and returns the original language even when translation is requested. For ordinary transcription, Whisper writes speech in the source language; translation is a separate task. Model and task guidance is in the project README.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review the subtitles before using them
Automatic recognition can mishear speech or produce awkward timing and line breaks. Watch the video while checking the subtitle file, and correct:
- Names, technical terms, and numbers.
- Missed or incorrectly recognized words and punctuation.
- Subtitle start and end times against the speech.
- Line breaks and readability on screen.
Whisper’s CLI includes experimental word-level timestamps and options to limit subtitle line width and line count. These controls can help shape the output, but they do not guarantee publication-ready captions. The available options are documented in the transcription implementation.
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The generated SRT or VTT is a sidecar subtitle file, not a finished video with captions embedded. You can import it into a video editor or upload it alongside the video on a platform that accepts subtitle files. To make captions permanently visible in the image, use an editor or encoding workflow that can burn subtitles into the video; that is a separate step from Whisper’s subtitle generation.
Quick Recap
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