Use the Gin framework to implement natural language processing and speech recognition functions

With the continuous development of artificial intelligence technology, natural language processing and speech recognition technology are receiving more and more attention and application. Today, in this article, we will introduce how to use the Gin framework to implement natural language processing and speech recognition functions.

Gin is a web framework written in Go language. It provides easy-to-use, efficient and flexible features. The Gin framework can easily cooperate with routing, middleware and other functions. Because of its easy-to-learn and quick-to-get started features, the Gin framework is widely used in scenarios such as building web applications and RESTful APIs. Below, we will see how to use the Gin framework to build natural language processing and speech recognition capabilities.

First, we need to install the necessary Go language and related libraries. Make sure you have installed the Go language and related dependencies, as shown below:

$ go version
$ go get -u github.com/gin-gonic/gin
$ go get -u google.golang.org/api/cloudspeech/v1
$ go get -u cloud.google.com/go/storage
$ go get -u github.com/tidwall/gjson

Before we begin, we need to convert speech to text, which requires the use of the Google Cloud Speech API. The Google Cloud Speech API is built on Google's speech recognition technology and can convert audio streams or audio files into text. Since the Google Cloud Speech API is part of the Google Cloud Platform, we need to use a Google Cloud Platform account to access the Speech API.

Next, we need to create a Gin project and register the route. In this project, we need to create a POST request route and a socket route, as shown below:

router.POST("/upload", uploadFile)
router.GET("/ws", func(c *gin.Context) {
    handleWebsocket(c.Writer, c.Request)
})

where the uploadFile function handles the POST request and sends the uploaded audio file to the Google Cloud Speech API for processing Text conversion operations. The handleWebsocket function will handle the WebSocket handshake operation and receive text data sent via WebSocket.

func uploadFile(c *gin.Context) {
    file, err := c.FormFile("audio")
    if err != nil {
        c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
        return
    }
    client, err := speech.NewService(context.Background())
    if err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
        return
    }
    ctx := context.Background()
    ctx, cancel := context.WithTimeout(ctx, time.Minute*5)
    defer cancel()

    f, err := file.Open()
    if err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
        return
    }
    defer f.Close()
    res, err := client.Speech(ctx, speech.Config{
        Encoding:                   encoding,
        SampleRateHertz:            sampleRateHertz,
        LanguageCode:               languageCode,
    }, f)
    if err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
        return
    }
    var transcript string
    for _, result := range res.Results {
        for _, alt := range result.Alternatives {
            transcript = alt.Transcript
            break
        }
    }
    c.JSON(http.StatusOK, gin.H{"transcript": transcript})
}

In the uploadFile function, we first get the uploaded audio file and then convert it to text using the Google Cloud Speech API. After conversion, the text data is returned to the client in JSON format.

Now we can start processing the text data sent via WebSocket and analyze it using natural language processing techniques. In this example, we will use the Google Natural Language API to analyze text data.

First, we need to set up the authentication file for Google Natural Language API. Go to the Google Cloud Console and create a new project there. In this project, you need to enable the Google Natural Language API and create a service account. After creation, download the authentication file for the service account. Create a new certification folder in your project and place your certification files in it.

Now we can define a function to handle text data sent via WebSocket. This function will use the gjson library to get the text and call the Google Natural Language API for analysis. After the analysis is complete, we will print various information about the text from the function. Finally, we send the analysis results back to the client in JSON format.

func handleWebsocket(w http.ResponseWriter, r *http.Request) {
    conn, err := upgrader.Upgrade(w, r, nil)
    if err != nil {
        log.Println(err)
        return
    }
    defer conn.Close()

    for {
        messageType, p, err := conn.ReadMessage()
        if err != nil {
            log.Println(err)
            return
        }
        if messageType == websocket.TextMessage {
            text := gjson.GetBytes(p, "text").String()
            client, err := language.NewClient(context.Background(), option.WithCredentialsFile("credentials.json"))
            if err != nil {
                log.Println(err)
                return
            }

            resp, err := client.AnnotateText(context.Background(), &languagepb.AnnotateTextRequest{
                Document: &languagepb.Document{
                    Type:   languagepb.Document_PLAIN_TEXT,
                    Source: &languagepb.Document_Content{Content: text},
                },
                Features: &languagepb.AnnotateTextRequest_Features{
                    ExtractSyntax:          true,
                    ExtractEntities:        true,
                    ExtractDocumentSentiment:    true,
                    ExtractEntitySentiment: true,
                },
            })
            if err != nil {
                log.Println(err)
                return
            }
            s, _ := json.MarshalIndent(resp, "", "    ")
            if err = conn.WriteMessage(websocket.TextMessage, []byte(s)); err != nil {
                log.Println(err)
                return
            }
        }
    }
}

Now, we have completed the implementation of natural language processing and speech recognition functions. By using the Gin framework, we can quickly create a web service that can interact between speech-to-text conversion and text analysis. At the same time, we also use Google Cloud Speech and Google Natural Language API to help us implement these functions. It's all very convenient, efficient and simple, and the Gin framework once again proves its importance in web development.

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