To create timestamped subtitles in Python, transcribe the audio with OpenAI Whisper and pass its result to Whisper’s subtitle writer. The example below writes an SRT file; you can choose VTT instead. FFmpeg can prepare media for this workflow, but its own documented Whisper filter is a separate route that uses whisper.cpp, not the Python package.
Generate an SRT file with Whisper in Python
Install and configure the openai-whisper package in your Python environment first. The project’s README documents loading a model and transcribing an audio file; its utility module provides a writer that saves timestamped subtitle formats.
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import whisper
from whisper.utils import get_writer
model = whisper.load_model("turbo")
result = model.transcribe("input.mp3")
writer = get_writer("srt", "output")
writer(result, "input.mp3", {})Change input.mp3 to the path of your audio file. The writer’s first argument selects the format, and its second is the output directory. It names the subtitle file from the source audio basename, so this example produces output/input.srt. Whisper’s result includes segments with timing as well as recognized text; writing only result["text"] would give you a transcript string, not timestamped subtitles.
This follows the documented Python APIs; it is not a claim that the snippet has been tested in every Python, operating-system, or package environment. Review the saved file and confirm its path before using it.
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Choose transcription or English translation
By default, transcription retains the spoken language. To request English output from non-English speech, pass task="translate" to transcribe, for example:
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Write VTT or adjust subtitle timing and layout
To write WebVTT rather than SRT, change the writer format to vtt:
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writer = get_writer("vtt", "output")
writer(result, "input.mp3", {})Whisper’s CLI implementation supports SRT and VTT, and documents options for line width, line count, and words per line. These control how text is arranged; they are distinct from choosing the timing granularity. The normal subtitle writer path uses segment timing. Word-level timing is available through a separate code path when word timestamps are present, while the CLI describes word timestamps as experimental. If precise word-by-word highlighting matters, verify the timing and formatting in the generated file rather than assuming segment subtitles provide it.
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Where FFmpeg fits—and when its Whisper filter is different
FFmpeg can be part of the surrounding media workflow—for example, when preparing, converting, or remuxing a media file. Its command-line documentation covers input and output handling, stream selection, and subtitles, but behavior depends on the input, output, codecs, and build. Subtitle filtering support also differs from audio and video filter processing, so there is no single generic FFmpeg command that works for every file and installation. See the FFmpeg command-line manual for the relevant options.
FFmpeg also documents a native whisper audio filter that can produce SRT, but this is a distinct integration: it requires the whisper.cpp library rather than calling Python’s openai-whisper package or get_writer. The FFmpeg filter manual describes options including a model path, language, queue, destination, and output format. Online documentation may cover features missing from an installed FFmpeg build; check the local build’s capabilities and documentation before relying on that filter.
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Review the generated captions
Listen while reading the subtitles and check names, technical terms, punctuation, timing, and line breaks. Automatic recognition can contain errors, and a correctly formatted SRT or VTT can still need editing to fit the audio and the viewing context.
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