ILLA Cloud + Hugging Face 调用 whisper

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ILLA Cloud + Hugging Face 调用 whisper

Reprinted and adapted from the Hugging Face public account: ILLA Cloud: Call Hugging Face Inference Endpoints to open the door to the large model world
https://mp.weixin.qq.com/s?__biz=Mzk0MDQyNTY4Mw==&mid=2247486349&idx=1&sn=afe18512de379fdd6cb66a3f7f9536b

On February 13, 2023, Hugging Face announced the cooperation with ILLA Cloud : ILLA Cloud officially supports the integration of the AI model library and other related functions on Hugging Face Hub.

Today, I will bring you an update of ILLA Cloud's integrated Hugging Face function. After the communication and promotion of both teams, ILLA Cloud has now released the official version 2.0 - users can combine the application building capabilities of ILLA Cloud with the advanced AI model on Hugging Face Combining the advantages of the two platforms will bring further efficiency improvements to the team.

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ILLA Cloud is an open source low-code development platform that allows users to build internal enterprise applications by connecting various components and operations, where Hugging Face acts as a provider of AI models, tools and resources.

In the following content, we will guide you to create an audio-to-text application in ILLA Cloud using Hugging Face's Inference Endpoints and openai/whisper-basemodels to demonstrate the content and advantages of this cooperation and the advantages of this technology. Some possible use cases.

Step 1: Build the front-end interface with components

First, design an intuitive interface using components of ILLA Cloud such as file uploads and buttons. This interface will enable users to easily upload audio files and initiate the transcription process.

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Make sure the user interface is friendly and visually appealing. Consider including clear instructions so users know how to use the app effectively.

Step 2: Add Hugging Face resource

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In order to add a Hugging Face resource, please fill in the required fields as follows:

Endpoint URL: Obtained by creating Endpoints on the Hugging Face platform.
Token: Found in your Hugging Face profile page.

Endpoints creation link:
https://ui.endpoints.huggingface.co/new

This step establishes the connection between your ILLA Cloud application and the Hugging Face model for seamless integration and execution.

The third step: configuration operation

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Next, configure the action to execute the Hugging Face model:

Choose the appropriate parameter type: for openai/whisper-basemodel, choose Binarybecause it requires a binary file input;
Map the input files of the front-end interface to the operation parameters.

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Careful configuration of actions ensures that your application handles audio input correctly and efficiently.

Step 4: Connect Components and Actions

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Now, establish the connection between components and operations in ILLA Cloud:

1. Add an event handler for the button, and trigger the operation to run when clicked;

2. Set the value of the text component to { {whisper.data[0].text}}. This will display the transcription result on the text component.

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By connecting components and operations, you provide a seamless experience for your users to experience the power of NLP models on the Hugging Face Hub for themselves.

Use Cases and Applications

The audio-to-text application you create using openai/whisper-basemodels has many potential use cases and applications, including:

Meeting minutes: automatically transcribe meeting recordings, saving time and effort and ensuring accurate records;
Podcast transcription: convert podcast episodes to text, making them more accessible and searchable;
Interview transcription: transcribing interviews for qualitative research, enabling researchers to analyze and encode text-based data;
Voice assistants: Improve the functionality of voice assistants by converting the user's spoken commands into text for further processing.

These use cases are just some of the many possibilities thanks to this powerful collaboration.

extended application

To further enhance your audio-to-text application, consider adding the following additional features:

Language translation: Integrate machine translation models to automatically translate transcribed texts into different languages, making your application more versatile and adaptable to global audiences;
Sentiment Analysis: Analyzes the sentiment of the transcribed text to help users understand the overall tone of the audio content;
Keyword extraction: implement a keyword extraction model to identify key themes and concepts from the transcribed text, allowing users to quickly understand the main focus of the audio content;
Text summarization: Summarize transcribed text using abstract or extractive summarization models to provide users with a condensed version of the content.

By adding these features, you can create a more comprehensive and powerful application that meets various user needs and requirements.

epilogue

The partnership between ILLA Cloud and Hugging Face provides users with a seamless and powerful way to build applications that leverage cutting-edge NLP models. Following this tutorial, you can quickly create an audio-to-text application utilizing Hugging Face Inference Endpoints in ILLA Cloud. This collaboration not only simplifies the application building process, but also opens up new possibilities for innovation and growth.