Audio Analysis with the Gemini API: Turn Recordings into Text and Summaries

What you'll learn in this guide

You can use the Gemini API's audio input to describe a recording, summarize it, or answer questions about it. Along with transcription and translation, the API can analyze specific sections with timestamps; sentiment detection in speech and music is also among its documented use cases.

Architecture and interior design teams could try these features to draft text from meeting notes, project reviews, or site recordings. However, you should check the accuracy of the transcript. In particular, don't treat automated output as approved minutes when it comes to project decisions, measurements, or technical terms.

Requirements and file selection

The example uses the Python client and the gemini-3.8-flash model ID. The Python code imports the google.genai package to create a client, then uploads an audio file and adds it to a generate_content request alongside a text instruction. The source doesn't specify pricing, account types, or required setup steps, so we don't assume any conditions related to those here.

You can send audio in two ways: include it directly in the request, or upload it as a file before making the request. The documentation recommends using the Files API if the total request size—including the file, text prompt, and system instructions—exceeds 20 MB. The short Python example below shows the upload method and an instruction attached to a file:

from google import genai

client = genai.Client()
myfile = client.files.upload(file="path/to/sample.mp3")
response = client.models.generate_content(
    model="gemini-3.8-flash",
    contents=["Describe this audio clip", myfile]
)
print(response.text)

Replace path/to/sample.mp3 with the path to the audio file you want to use. The text in contents tells the model what to do. The code prints the response as text, so don't assume the output will always conform to a specific JSON schema.

Audio analysis, step by step

1. Define the task clearly. Start by requesting just one type of result: a short summary, transcription, translation, or description of the audio. If you want multiple outputs from the same recording, list each expectation separately in the instruction.

Summarize this audio recording in Turkish. Focus on the main topic and the project decisions mentioned by the speakers. Don't guess at information that is inaudible or unclear; identify it as uncertain.

This instruction is an example prompt for getting an initial summary from a meeting or project review recording. It's not a ready-made architecture prompt from the source; it's an example based on the documented audio summarization feature.

1. Upload the audio file and add it to the request. In the code above, the file is uploaded using client.files.upload, then the uploaded file and instruction are passed to the generate_content call. Check the total request size: the 20 MB limit applies to the total request described in the source, not just the file on its own. The documentation also says that for smaller requests, audio data can be sent directly in the request.

2. Specify what you expect from a transcript. When transcribing a recording, clearly state expectations such as language, translation, and segmentation. The example in the documentation requests an MM:SS timestamp for each segment, the primary language, an English translation for segments that aren't in English, and the speaker's sentiment. The example limits sentiment options to Happy, Sad, Angry, and Neutral.

Transcribe the audio recording in detail. For each segment, provide a timestamp in MM:SS format and specify the language spoken. If the speech isn't in English, include an English translation. For each segment, identify the dominant sentiment using only one of these options: Happy, Sad, Angry, Neutral. Start with a brief summary of the recording.

This prompt restates the workflow described in the documentation's transcription example for a general recording, such as an architecture meeting. If the audio is in Turkish, the request to translate into English from the source instruction is preserved; changing the target language doesn't guarantee the same result as in the example.

1. Request structured output. In the official example, the response format is set to application/json and a schema is defined. The schema contains a summary field called summary and an array of segments called segments; each segment has fields such as timestamp, content, language, language code, translation, and sentiment. This can make it possible to process specific fields in your application. However, some fields in the example schema are marked as required, while translation isn't included in the required list; check which fields your own application needs.

2. Check the output against the recording. Compare timestamps, proper names, numbers, and measurements with the original audio. For recordings with multiple languages, review the language and translation fields as well. A sentiment label is also an automated classification; it shouldn't be treated as objective fact in meeting minutes or customer feedback.

Common mistakes and limitations

The main limitation is that the Gemini API doesn't support real-time transcription. For live audio and video interactions, the documentation points to the Live API; for specialized speech-to-text models that support real-time transcription, it points to the Google Cloud Speech-to-Text API. Generating live captions during a meeting and processing an audio file afterward are therefore different workflows.

Another common mistake is assuming that a short instruction will produce detailed, well-structured output. If you need timestamps, a summary, language, translation, or sentiment, describe those requirements clearly in the prompt. If your application needs to parse the response into fields, consider the structured-output approach shown in the source. Also, a general request such as Describe this audio clip asks only for a description of the audio; it doesn't request a transcript or segment timestamps.

Consider the total request size when choosing an upload method. The official documentation explicitly recommends using the Files API for requests larger than 20 MB. The sources don't provide details on pricing, file retention periods, or comprehensive compatibility lists for specific audio formats, so don't make assumptions about these; check the current API documentation before using the service.

Implications for architecture, interiors, and archviz workflows

Architects and interior designers could try summarizing meeting recordings, transcribing site voice notes, or segmenting spoken feedback from visualization reviews. Timestamps can make it easier to return to a relevant point in a long recording. The translation field could also be useful for drafting work on multilingual discussions.

These aren't presented in the source as ready-made architecture applications; they're possible workflows based on the API's summarization, transcription, translation, and timestamp features. Technical terms, room names, measurements, and client decisions should always be verified by listening to the recording. Teams should also review the costs and data-processing terms for their own API use; the source page doesn't provide details on these topics.

Next steps

For your first test, choose a short recording with a single purpose. Start with a summary or transcript and check it against the audio; then add requirements such as timestamps, translation, or JSON fields one at a time. This makes it easier to identify whether an error comes from the audio input, the instruction, or the expected output format.

If you need live transcription, don't use the Gemini API file-processing example as though it were a real-time solution. Review the Live API and Google Cloud Speech-to-Text options referenced in the documentation separately. The example here is for file-based audio analysis and generating a text response.

Source and license

This guide has been adapted into Turkish based on examples in Google's Gemini API audio understanding documentation. The source is licensed under CC BY 4.0. The code example and usage information are based on descriptions in this openly licensed guide; the architecture-focused prompts were prepared editorially.