> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pyannote.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use AWS S3 private objects

> This tutorial shows how to use signed urls to create jobs with private objects stored in AWS S3

The AWS Simple Storage Service (S3) can be used as a source for jobs. If your
audio already resides on S3, you can process it there directly with pyannoteAI
APIs by providing temporary access credentials to your data.

## Pre-Signed URLs

A pre-signed URL can be a convenient way to provide a URL containing temporary
credentials as a basic string. pyannoteAI Jobs APIs need to GET the `url` parameter
when reading it. Therefore, to make a request with pre-signed URLs, you'll need
to generate it.

Here's an example for how to do this:

```python generate-signed-url.py theme={null}

# pip install boto3

import boto3
from botocore.exceptions import ClientError

def create_presigned_url(bucket_name, object_name,
                         operation='get_object', expiration=3600):
    client = boto3.client('s3')
    try:
        return client.generate_presigned_url(operation,
                Params={'Bucket': bucket_name, 'Key': object_name},
                ExpiresIn=expiration
                )
    except ClientError as e:
        print(e)


# Use the presigned URL as the input parameter
signed_url = presign.create_presigned_url('your-bucket-name', 'object/name.mp3')

request = {
 'url': signed_url,
}

# ... send the request to the Job API

```

For further examples, see the AWS documentation on [Creating a pre-signed URL for Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/ShareObjectPreSignedURL.html).