To create timestamped subtitles in Python, transcribe the audio with OpenAI Whisper and pass the result to Whisper’s subtitle writer. The example below creates an SRT file; you can use the same workflow for WebVTT (VTT). FFmpeg is optional for preparing or converting media, while its own Whisper filter is a separate route that uses whisper.cpp rather than the Python package.
Generate an SRT file with Whisper in Python
Install the OpenAI Whisper Python package and make sure FFmpeg is available for Whisper’s media handling. The sources cited here do not specify a pinned installation command or a platform compatibility matrix, so check the project’s current installation instructions for your operating system and environment.
Save this as a Python script or run it in a Python console after installing the package:
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", {})- Replace
input.mp3with the path to your audio or supported media file. - Load a Whisper model and call
transcribe. The result includes transcript text and timed segments; the method processes input with a sliding 30-second window, as described in the Whisper README. - Create a writer with
get_writer("srt", "output"), whereoutputis the destination directory. - Call the writer with the transcription result, source path, and options. It names the subtitle file from the input’s basename and writes it in the selected directory. For this example, check for
output/input.srt.
The Python API pattern is documented by the Whisper README and the project’s subtitle writer implementation. The example illustrates that API; it is not a claim that the code was tested in a particular environment.
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Choose SRT, VTT, or plain transcript text
SRT and VTT are subtitle formats with timestamps. Whisper’s CLI and writer implementation support both. Change the writer format to "vtt" and use an output directory of your choice to produce VTT instead:
writer = get_writer("vtt", "output")
writer(result, "input.mp3", {})A plain transcript string is useful for reading, but it is not itself a timestamped subtitle file. Use the transcription result’s segments with the writer when you need timed captions. The CLI implementation lists the supported output formats.
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Transcribe speech or translate it into English
By default, transcription retains the spoken language. Translation is a different task: it requests English text from non-English speech. The Whisper README says the turbo model is not trained for translation tasks, so do not assume that selecting translation makes it suitable.
For non-English-to-English translation, the README recommends multilingual models such as tiny, base, small, medium, or large, and says to use medium or large for the best translation results. Model and CLI details can change as the repository evolves; see the current Whisper README and CLI implementation before relying on particular options.
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Adjust subtitle timing and line layout
The basic writer workflow creates captions from Whisper’s segment timing. Word-level timestamps and subtitle layout are separate choices: word timing changes how timing is represented, while line width, line count, and words-per-line options affect formatting. The CLI marks word timestamps as experimental, and the writer has code paths for word timing when it is present. Consult the CLI implementation and writer implementation for the current option names and behavior.
Review the resulting subtitles against the audio. Correct recognition errors, punctuation, awkward breaks, and any timing that does not fit the speech before publishing or sharing the file.
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Where FFmpeg fits—and its separate Whisper filter
FFmpeg can be used to prepare media before transcription or handle media afterward, but the Python example above uses OpenAI Whisper’s Python package and writer. It does not call FFmpeg’s Whisper filter.
The FFmpeg project documents a separate audio filter named whisper that depends on the whisper.cpp library. Its options include a model path, language, queue, destination, and output format, and its manual gives an SRT example. This is an alternative integration, not a setting to add to get_writer. See the FFmpeg filter manual.
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If you choose that filter, confirm it is present in your installed FFmpeg build; the online manual may describe features your build does not include. For media preparation, remuxing, stream selection, and subtitle handling, consult the FFmpeg command-line manual. Subtitle-filter support can differ from audio and video filter processing, so a command that works for one input or build may not work for another.
Choose the route that matches your workflow
| Need | Use | What to know |
|---|---|---|
| Generate subtitles within a Python workflow | OpenAI Whisper package with get_writer |
Supports SRT and VTT; the basic path writes segment-timed captions. |
| Use FFmpeg’s documented Whisper audio filter | FFmpeg filter with whisper.cpp | Requires a build with the filter and its documented whisper.cpp dependency. |
| Prepare, select, or remux media | FFmpeg command-line tools as needed | Input formats, streams, and subtitle support depend on the media and build. |
Neither route is established as a universal winner across environments. Choose based on whether you want a Python API, the FFmpeg filter’s integration, or FFmpeg’s media-handling functions.
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