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fal-ai/audio-understanding
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fal-ai/audio-understanding
Schema
LLMs
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Table of contents

A audio understanding model to analyze audio content and answer questions about what's happening in the audio based on user prompts.
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fal-ai/audio-understanding

A audio understanding model to analyze audio content and answer questions about what's happening in the audio based on user prompts.
Inference
Commercial use
Schema
LLMs
PlaygroundAPI
Loading API documentation

Table of contents

Table of contents

1. Calling the API

  • Install the client
  • Setup your API Key
  • Submit a request

2. Authentication

  • API Key

3. Queue

  • Submit a request
  • Fetch request status
  • Get the result

4. Files

  • Data URI (base64)
  • Hosted files (URL)
  • Uploading files

5. Schema

  • Input
  • Output
  • Other

About

Analyze Audio

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client
Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/audio-understanding", {
  input: {
    audio_url: "https://storage.googleapis.com/falserverless/model_tests/audio-understanding/Title_%20Running%20on%20Fal.mp3",
    prompt: "What is being discussed in this audio?"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In an environment where you cannot set environment variables, you can configure the API key manually on the client.
import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});
Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/audio-understanding", {
  input: {
    audio_url: "https://storage.googleapis.com/falserverless/model_tests/audio-understanding/Title_%20Running%20on%20Fal.mp3",
    prompt: "What is being discussed in this audio?"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/audio-understanding", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/audio-understanding", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);
Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

audio_url string* required

URL of the audio file to analyze

prompt string* required

The question or prompt about the audio content.

detailed_analysis boolean

Whether to request a more detailed analysis of the audio

{
  "audio_url": "https://storage.googleapis.com/falserverless/model_tests/audio-understanding/Title_%20Running%20on%20Fal.mp3",
  "prompt": "What is being discussed in this audio?"
}

Output#

output string* required

The analysis of the audio content based on the prompt

{
  "output": "Based on the audio, this appears to be a business meeting discussing quarterly sales results. The speakers are analyzing performance metrics and discussing strategies for the upcoming quarter. The tone is professional and collaborative, with multiple participants contributing to the discussion."
}

Other types#