How to Analyze Construction Site and Interior Audio with the Gemini API

What you’ll learn in this guide

Audio recordings made during construction site visits, interior reviews, or project meetings can be turned into searchable notes later. The Gemini API can be used to describe and summarize audio content and answer questions about it. Its documented use cases also include transcription, translation, sentiment detection, and adding timestamps to specific audio segments.

This guide covers how to send an audio file to the API and write prompts suited to architectural workflows. The examples are designed for organizing a site observation, material assessment, or project meeting recording. Rather than treating the output as a site report or technical decision, consider it a draft to check against the recording.

Requirements

You’ll need access to the Gemini API, an API key, and an audio file. Google’s examples use the google-genai client for Python, imported with from google import genai and initialized using genai.Client(). Since the source does not specify a particular client library version, no version number is given here.

The documentation shows how to upload an audio file and use it in a generate_content request, or include the audio data directly in the request. If the total request size—including the file, text prompt, and system instructions—exceeds 20 MB, use the Files API. The text examples in this guide are instructions to send to the API; uploading the file and receiving a response are handled by the application’s API call.

Keep one limitation in mind from the outset: the Gemini API does not support real-time transcription. The steps below are therefore for processing an existing audio file, not transcribing a live meeting as it happens.

Audio analysis, step by step

1. Choose the recording and define your goal

First, clarify what you want to find out. For example, you might want to list noises heard in a space, extract decisions made during a meeting, or locate a particular passage in the recording. Asking the model to rely only on information audible in the recording makes the output easier to review.

Upload the recording through the Files API, or include it in the request when appropriate. Google’s example uses the model name gemini-3.8-flash. Then pass the audio file and an instruction tailored to your goal—such as the one below—to a generate_content call.

Review this audio recording as an architectural site observation. Based only on information audible in the recording, summarize the space, sounds heard, and issues raised by the speakers in short bullet points. Do not guess at information that is inaudible or unclear; explicitly note when something is uncertain.

This instruction asks for an initial set of notes rather than presenting the recording as a technical report. Any points marked as uncertain in the output should be checked by listening to the recording.

2. Request a transcript or short summary

You can request a transcript or summary to extract decisions and follow-up tasks from a meeting recording. The Gemini API can summarize audio content and answer questions about it. Narrow the prompt to the type of information the team will need in the next step.

Summarize this project meeting recording. Under separate headings, list the decisions made, follow-up tasks explicitly mentioned by the speakers, and questions that remain unresolved. Do not add people, dates, measurements, or decisions that are not in the recording.

This kind of output can be useful as a first draft of meeting notes. However, check project terminology and the people responsible against the original audio; a misheard phrase could change the meaning of a decision.

3. Request timestamps and structured results

When you need to locate specific parts of a recording, you can request a timestamp for each segment. Google’s example application divides a transcript into segments and returns structured output containing fields such as timestamp, content, language, and sentiment. The example sets the response format to JSON and defines the fields with a schema.

Divide the audio recording into meaningful segments. For each segment, provide a timestamp in MM:SS format, a transcript of the audible content, and the language spoken. Also include a short summary of the segment. If you are unsure of a timestamp, say so explicitly; do not fill in words that cannot be heard.

If you need structured results in your application, you can define the response format and fields with a schema, as in the API documentation’s example. In the example schema, the summary field contains the overall summary, while the segments array holds the individual segment records; each segment object has fields such as timestamp and content. This approach can make the results easier to review or process in an application, but a schema does not guarantee transcription accuracy.

4. Ask questions about the audio

Instead of asking for another summary of the entire file, you can ask a question focused on a particular topic in the recording. For example, in an interior review, you might want to extract what the speakers said about lighting, acoustics, or finishes. Don’t assume these topics are mentioned in the recording; instruct the model to report only what it can hear.

List separately what is said about lighting, acoustics, and surface materials in this interior review recording. If a topic is not mentioned in the recording, say so. Give the timestamp for the relevant segment for each item and do not add technical recommendations that are not in the recording.

The Gemini API supports use cases that analyze specific parts of audio and provide timestamps. When reviewing the results, listen to each item at the relevant timestamp; a timestamp alone does not prove that the recording has been interpreted correctly.

Common mistakes

• Writing a vague prompt: A general instruction such as “analyze the recording” doesn’t clearly specify what information you need. Choose a goal: transcript, summary, follow-up task, or a particular topic.

• Treating guesses as facts: If a technical measurement, material name, or decision is unclear in the recording, ask the model not to fill in the gaps and verify the output by listening.

• Overlooking request size: If the total request exceeds 20 MB, use the Files API. Include not just the audio file, but also other content in the request when calculating the size.

• Expecting live transcription: The Gemini API audio documentation states that real-time transcription is not supported. Use this workflow for existing files.

• Treating structured output as a verified record: A JSON schema defines the response format. You still need to check the accuracy of the words heard, language identification, and timestamps.

Next steps

Start by testing with a short recording whose contents you know. Try separate prompts for a summary, transcript, and timestamped segments using the same audio to determine which output is most useful for your team. If the results will be added to project notes, include a step in the workflow to compare them with the recording.

Teams building an application can review the Python example in the documentation, which uploads audio through the Files API and calls generate_content. The REST example using an API key also shows the GEMINI_API_KEY variable. Consider the total request size when choosing how to provide the audio data; if you need live transcription, keep in mind that the Gemini API audio guide does not support that use case.

Sources and licensing

This guide is based on Google’s Gemini API audio understanding documentation and the Gemini Omni Flash information on the video generation page. The content has been adapted from CC BY 4.0-licensed sources and rewritten for architecture, interior design, and archviz workflows.