To convert a completed recording into text in Python, you can run OpenAI’s open-source Whisper model locally: install the package and its required ffmpeg command-line tool, load a model, and call transcribe() on an audio file. This is a file-transcription workflow, not live microphone capture. The code below prints the recognized text; it does not upload audio to a hosted transcription API.
Choose the workflow that matches your audio
A saved recording and speech arriving continuously from a microphone or stream are different problems. This tutorial implements transcription of a completed audio file with local Whisper. It does not record from a microphone or deliver live captions.
| What you want to transcribe | Suitable route | What to expect |
|---|---|---|
| A completed recording | Local Whisper, as shown below, or a hosted file-transcription API | Give the program an audio file; it returns recognized text. |
| Ongoing microphone, call, or media-stream audio | A streaming workflow such as OpenAI Realtime transcription | Audio is handled while it is being captured or streamed, rather than submitted as one finished file. |
OpenAI separates these workflows in its speech-to-text guide. A microphone is unnecessary for transcribing a recording you already have.
Run the local Whisper file-transcription solution
1. Install the prerequisites
The Whisper repository documents installation with pip install -U openai-whisper and requires the ffmpeg command-line tool. Install ffmpeg using the method appropriate for your operating system, then check that the command is available in your terminal. The repository also says Rust may be needed if a prebuilt tiktoken wheel is unavailable.
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pip install -U openai-whisperThe repository README states that it expects Python 3.8–3.11; that statement is not a guarantee that every operating system, Python build, or dependency combination will work. Consult the current Whisper README if installation fails or your environment differs.
2. Save and run the script
Save this as transcribe.py in an environment where Whisper and ffmpeg are installed. Replace audio.mp3 with the path to your recording.
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import whisper
model = whisper.load_model("turbo")
result = model.transcribe("audio.mp3")
print(result["text"])This follows the repository’s basic Python example. The first call loads the model; transcribe() processes the named recording; and the final line prints the recognized words. The example uses turbo, but you can choose another model supported by the package.
3. Check the output
The printed value is plain transcription text. Review it before relying on it: recognition quality varies with the language and recording, and the repository does not promise one accuracy rate for every audio file. This minimal example does not save a text file, create subtitles, add timestamps, or capture live speech.
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Local Whisper or a hosted transcription API?
Whisper’s Python package runs a model in your environment. A hosted API instead sends a completed recording to a transcription endpoint. The two approaches have different setup and data-flow implications; this local example does not use an API key.
| Consideration | Local Whisper package | Hosted transcription API |
|---|---|---|
| Execution | Load a model and call transcribe() in Python, as in the code above. |
Upload a completed recording to the API transcription endpoint. |
| Setup | Install openai-whisper and the required ffmpeg tool; model execution depends on the local environment. |
Use the API and its current supported model, formats, and request behavior; consult the transcription API reference. |
| Input mode | The example handles an audio file; it does not implement ongoing capture. | Use file transcription for a finished recording and a Realtime workflow for ongoing audio. |
| Choice factors | Consider language, speed and accuracy tradeoffs, environment constraints, and whether local execution matters. | Consider required output, accepted audio, and whether the input is a completed file or ongoing stream. |
Neither route is a universal best choice. Select based on the application’s environment, audio handling requirements, language needs, and whether input is a file or a live stream.
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Model, language, and translation considerations
The Whisper repository describes six model sizes, four with English-only variants, with speed and accuracy tradeoffs. It identifies turbo as an optimized version of large-v3 and notes that performance varies widely by language. Treat the model in the example as a starting point, not an accuracy guarantee.
Transcription returns speech in its original language. Translation is a separate task: the OpenAI API guide documents English translation of a completed recording through /v1/audio/translations with whisper-1. The Whisper repository recommends multilingual models for translating non-English speech into English and says turbo is not trained for translation; it returns the original language even when translation is requested.
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For API transcription, the guide says language hints can be provided for supported models; unsupported or incorrectly formatted language codes are rejected. The whisper-1 API also supports word- or segment-level timestamps through timestamp_granularities[]. These API features are not implemented by the local three-line example.
Audio formats and file-size limits for the hosted API
These limits concern OpenAI’s documented file-transcription API, not a stated limit for local Whisper. The guide lists MP3, MP4, MPEG, MPGA, M4A, WAV, and WebM, and sets a 25 MB maximum file size for that workflow. It recommends compressing larger recordings or splitting them into chunks no larger than 25 MB. Avoid cutting a chunk in the middle of a sentence because the missing context can affect transcription. The guide mentions PyDub as one way to split audio and makes no guarantee about third-party software’s usability or security.
The API reference also lists FLAC and OGG, while noting that supported formats vary by model. Check the current guide and reference for the exact model and request you plan to use rather than treating one format list as universal.
Quick Recap
Common setup problems
ffmpegis not found: Install the command-line tool and make sure its executable is available on your system path, then retry.- Package installation fails around
tiktoken: The Whisper README notes that Rust may be required when a prebuilt wheel is unavailable. Check the repository’s current installation guidance for your platform. - The recording path fails: Confirm the filename and path are correct and that the program can access the file. Use an explicit path if the script is running from a different working directory.
- The transcript is in the wrong language or quality is poor: Model performance varies by language. Review the recording and select a suitable model; do not assume changing one setting will guarantee accurate results.
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