Handling Transcription Models in Spring AI

Voice assistants, automated transcription services, and other applications rely on transcription models to convert audio inputs into text. Integrating these models into a Spring AI framework can streamline the process and offer a scalable and efficient solution.

In this article, we will learn how to incorporate transcription models into a Spring AI application.

Transcription Models

Transcription models use natural language processing (NLP) and machine learning (ML) techniques to interpret audio input and convert it into text. Popular models include Google’s Speech-to-Text, IBM’s Watson, and Mozilla’s open-source DeepSpeech. These models can be integrated into your application to enable voice commands, automated note-taking, and more.

Key Concepts:

• Audio Preprocessing: Preparing audio files for transcription by cleaning and normalizing the data.
• Model Integration: Embedding a transcription model into a Spring AI application to handle the conversion of audio to text.
• Post-Processing: Refining the transcribed text to improve accuracy and formatting.

Prerequisites:

Before starting the implementation, ensure you have the following:

• Java 11+: Required to run modern Spring applications.
• Spring Boot: A framework to simplify the development of Spring applications.
• Spring AI: The main framework for integrating AI features within the Spring ecosystem.
• Transcription Model API: Such as Google Speech-to-Text or IBM Watson.
• Maven or Gradle: Tools for managing dependencies.
• Familiarity with: RESTful services, dependency injection, and Spring Boot.

Step-by-Step Implementation to Handle Transcription Models in Spring AI

Step 1: Setting Up Your Spring AI Project

• Navigate to Spring Initializr to create a new project.
• Select Spring Boot with a compatible version (e.g., 3.0.x) and choose Java as the language.
• Add Spring Web, Spring AI, and any additional libraries as dependencies.

Here is the build.gradle file,

plugins {
    id 'java'
    id 'org.springframework.boot' version '3.0.x'
    id 'io.spring.dependency-management' version '1.0.15.RELEASE'
}

group = 'com.example'
version = '0.0.1-SNAPSHOT'

java {
    sourceCompatibility = '11'
}

repositories {
    mavenCentral()
}

dependencies {
    implementation 'org.springframework.boot:spring-boot-starter-web'
    implementation 'com.google.cloud:google-cloud-speech:1.22.0'
    implementation 'org.springframework.ai:spring-ai-core:1.0.0'
    
    testImplementation 'org.springframework.boot:spring-boot-starter-test'
}

tasks.named('test') {
    useJUnitPlatform()
}

Step 2: Integrating the Transcription Model API

• Add Dependencies: Ensure that the necessary dependencies for the transcription model API (e.g., Google Cloud Speech) are included in your build.gradle file.
• Configure API Credentials: Store your API keys securely, using environment variables or a configuration management tool like Spring Cloud Config.

@Configuration
public class TranscriptionConfig {

    @Bean
    public SpeechClient speechClient() throws IOException {
        return SpeechClient.create();
    }
}

Step 3: Creating the Transcription Service (Implement the Service Layer)

Create a service class to handle audio input and interact with the transcription API.

@Service
public class TranscriptionService {

    private final SpeechClient speechClient;

    @Autowired
    public TranscriptionService(SpeechClient speechClient) {
        this.speechClient = speechClient;
    }

    public String transcribe(MultipartFile audioFile) throws Exception {
        // Audio preprocessing, transcription API call, and post-processing
        ByteString audioBytes = ByteString.readFrom(audioFile.getInputStream());

        RecognitionConfig config = RecognitionConfig.newBuilder()
                .setEncoding(RecognitionConfig.AudioEncoding.LINEAR16)
                .setSampleRateHertz(16000)
                .setLanguageCode("en-US")
                .build();
        
        RecognitionAudio audio = RecognitionAudio.newBuilder()
                .setContent(audioBytes)
                .build();

        RecognizeResponse response = speechClient.recognize(config, audio);
        return response.getResultsList().stream()
                .map(RecognitionResult::getAlternativesList)
                .flatMap(Collection::stream)
                .map(SpeechRecognitionAlternative::getTranscript)
                .collect(Collectors.joining("\n"));
    }
}

Step 4: Building the REST Controller (Create a REST Endpoint)

Develop a REST controller to manage HTTP requests and trigger the transcription process.

@RestController
@RequestMapping("/api/v1/transcribe")
public class TranscriptionController {

    private final TranscriptionService transcriptionService;

    @Autowired
    public TranscriptionController(TranscriptionService transcriptionService) {
        this.transcriptionService = transcriptionService;
    }

    @PostMapping
    public ResponseEntity<String> transcribeAudio(@RequestParam("file") MultipartFile file) {
        try {
            String transcription = transcriptionService.transcribe(file);
            return new ResponseEntity<>(transcription, HttpStatus.OK);
        } catch (Exception e) {
            return new ResponseEntity<>(e.getMessage(), HttpStatus.INTERNAL_SERVER_ERROR);
        }
    }
}

Step 5: Testing and Validation (Verify the Implementation)

• Write unit tests and integration tests to ensure your transcription service works as expected.
• Manually test the REST endpoint using Postman or a similar tool.

Conclusion

In this article, we explored how to integrate transcription models into a Spring AI application. We walked through setting up the project, integrating a transcription model API, creating a service layer, building a REST controller, and validating the implementation. With this setup, you're ready to expand your application by adding AI-driven transcription capabilities within the robust Spring AI framework.

batraharshita12

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Article Tags :

• Advance Java
• Spring AI