Teach you step by step how to use Python to connect to Qiniu Cloud interface to achieve audio cutting

Teach you step by step how to use Python to connect to Qiniu Cloud interface to achieve audio cutting

In the field of audio processing, Qiniu Cloud is a very excellent cloud storage platform, providing a wealth of interfaces to perform various audio processing kind of processing. This article will use Python as an example to teach you step by step how to connect to the Qiniu Cloud interface to realize the audio cutting function.

First, we need to install the corresponding Python library for interacting with Qiniu Cloud. Enter the following command on the command line to install:

pip install qiniu

After the installation is completed, we need to create a storage space on the Qiniu Cloud Platform and obtain the relevant Access Key and Secret Key to authenticate our requests. . Next, we can start writing code.

First, import the necessary libraries:

from qiniu import Auth, BucketManager

Then, we need to initialize the authentication object and storage space object:

access_key = 'your_access_key'
secret_key = 'your_secret_key'
bucket_name = 'your_bucket_name'

q = Auth(access_key, secret_key)
bucket = BucketManager(q)

Next, let us define a function for Implement audio cutting function. This function accepts three parameters: source audio file name, target audio file name, and cutting time point (in seconds). For example, we cut the source audio file into two segments, the first segment is from 0 seconds to 30 seconds, and the second segment is from 30 seconds to 60 seconds:

def audio_segmentation(source_key, target_key, split_time):
    ops = 'avthumb/mp3/ss/%d/t/%d' % (split_time, split_time)
    source_url = 'http://%s/%s' % (bucket_domain, source_key)
    target_key = '%s_%d.mp3' % (target_key, split_time)
    
    ret, info = bucket.fetch(source_url, bucket_name, source_key)
    if ret is None:
        print('Fetch source audio failed:', info)
        return
    
    ret, info = bucket.fetch(source_url, bucket_name, target_key, op=ops)
    if ret is None:
        print('Segmentation failed:', info)
        return
    
    target_url = 'http://%s/%s' % (bucket_domain, target_key)
    print('Segmentation success:', target_url)

Finally, we can call this function to cut the audio:

audio_segmentation('source_audio.mp3', 'target_audio', 30)

In the above code, we first use the bucket.fetch method to pull the source audio file from the external URL to the Qiniu cloud storage space. Then, cut the audio by passing the op parameter. Finally, we can get the URL of the cut audio file by splicing the storage space domain name and the target audio file name.

The above are all code examples for using Python to connect to the Qiniu Cloud interface to implement audio cutting. I hope this article can help you quickly get started with audio processing related work. At the same time, Qiniu Cloud also provides other rich interfaces and functions, which you can further explore and use according to your own needs.

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