README.md

metadata

title: Semabox
emoji: 🎙
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: apache-2.0

Semabox API Development Guide Using Whisper, SeamlessM4T, and MMS

This guide will walk you through the process of developing an API for the Semabox tool using the Whisper, SeamlessM4T, and MMS models. This API will allow users to transcribe, translate, and process audio data in multiple languages. The guide covers setting up the environment, integrating the models, and creating endpoints.

Table of Contents

1. Environment Setup

   • Prerequisites
   • Installing Dependencies
   • Setting Up the Models

2. API Architecture

   • Overview
   • Model Integration Strategy
   • Data Flow

3. API Development

   • Initializing the API
   • Creating Endpoints
   • Model Invocation
   • Error Handling
   • Response Structure

4. Deployment Considerations

   • Scaling the API
   • Security and Privacy
   • Monitoring and Logging

5. Example Requests

   • Transcription Request
   • Translation Request
   • Language Identification Request

6. Testing and Debugging

   • Unit Tests
   • Load Testing
   • Debugging Common Issues

References

• SeamlessM4T on Hugging Face
• Whisper API Example Code
• Speech to Text with Azure OpenAI Whisper Model
• Whisper Overview on OpenAI
• How to Build an OpenAI Whisper API
• Whisper API Flask GitHub Repository
• YouTube Tutorial on Whisper API

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Check out the configuration reference at Hugging Face Spaces Config Reference.

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Semalab is going to be huge.