Java implements audio to text sample code (voice recognition)

Implementing audio to text (also known as speech recognition or ASR) in Java usually involves the use of dedicated speech recognition services such as Google Cloud Speech-to-Text, IBM Watson Speech to Text, Amazon Transcribe, Microsoft Azure Speech Services, or some open source libraries such as CMU Sphinx.

Since a full demonstration using the API of an open source library or cloud service for a complete demonstration may involve complex setup and dependency management, a simplified overview will be provided here, and using Google Cloud Speech-to-Text as an example, giving rough steps and pseudo-code.

1. Implementation steps

Set up account and API keys:

Register an account at a cloud service provider (such as Google Cloud Platform).
Enable Speech-to-Text service.
Create an API key or set up service account credentials.

Add dependencies:

If you use build tools such as Maven or Gradle, add client library dependencies for the corresponding service.

Write code:

Initialize the client library.
Read an audio file or audio stream.
Call the speech recognition API to pass in audio data.
Receive and process identification results.

test:

Run the code and verify the results.

2. Pseudocode/example code

Here is a very simplified example that does not include complete error handling and configuration settings.

Maven dependencies (if using Google Cloud Speech-to-Text)

<!-- Add Google Cloud Speech-to-Text dependency -->
<dependency>
    <groupId></groupId>
    <artifactId>google-cloud-speech</artifactId>
    <version>YOUR_VERSION</version>
</dependency>

3. Java code examples (pseudocode)

// Import the necessary librariesimport .;
import .;
import .;
import .;
import .;
import .;
import .;

import ;
import ;
import ;

public class AudioToText {

    public static void main(String[] args) throws Exception {
        // Initialize SpeechClient (requires API key or service account credentials)        try (SpeechClient speechClient = ()) {

            // Read audio files (assuming it is WAV format here)            byte[] audioBytes = (("path_to_your_audio_file.wav"));

            // Set the identification configuration            RecognitionConfig config = ()
                .setEncoding(AudioEncoding.LINEAR16) // Set audio encoding format                .setSampleRateHertz(16000) // Set the audio sampling rate (based on the actual situation of the file)                .setLanguageCode("en-US") // Set the recognition language                .build();

            // Set audio data            RecognitionAudio audio = ().setContent(audioBytes).build();

            // Call the synchronization recognition method            SyncRecognizeResponse response = (config, audio);

            // Process the identification results            for (SpeechRecognitionResult result : ()) {
                // Each result may contain multiple alternatives (i.e. different identification possibilities)                for (SpeechRecognitionAlternative alternative : ()) {
                    ("Transcription: %s%n", ());
                }
            }
        }
    }
}

Notice：

The above code is a simplified example that may need to be adjusted based on your actual audio file format and cloud service settings.
Make sure that the correct API key or service account credentials are set up so that the client library can access cloud services.
Depending on your audio file, adjustments may be requiredsetSampleRateHertzandsetEncodingetc.
Error handling and logging are required in production environments.
If you use an open source library like Sphinx, the setup and code will be completely different, but the basic steps are still similar.

4. Complete code examples

Use the Google Cloud Speech-to-Text API, which includes basic error handling and configuration settings. To run this example, we need to set up the Speech-to-Text API on our Google Cloud Platform and get a valid credential file (usually a JSON file).

First, make sure we have added Google Cloud's client library to our project. We can add dependencies through Maven (inIn the file):

&lt;dependencies&gt;
    &lt;!-- ... Other dependencies ... --&gt;
    &lt;dependency&gt;
        &lt;groupId&gt;&lt;/groupId&gt;
        &lt;artifactId&gt;google-cloud-speech&lt;/artifactId&gt;
        &lt;version&gt;YOUR_VERSION&lt;/version&gt; &lt;!-- Please replace it with the latest version --&gt;
    &lt;/dependency&gt;
    &lt;!-- ... Other dependencies ... --&gt;
&lt;/dependencies&gt;

Here is a complete Java code example containing error handling and configuration settings:

import ;
import .;
import .;
import .;
import .;
import .;
import .;
import .;
import .;
import .;

import ;
import ;
import ;
import ;
import ;

public class AudioToTextWithErrorHandling {

    // The path to the JSON file of the service account credentials downloaded from Google Cloud platform    private static final String CREDENTIALS_FILE_PATH = "/path/to/your/";

    // Audio file path    private static final String AUDIO_FILE_PATH = "/path/to/your/audio_file.wav";

    public static void main(String[] args) {
        try {
            // Initialize SpeechClient            try (SpeechClient speechClient = createSpeechClient()) {

                // Read audio files                byte[] audioBytes = ((AUDIO_FILE_PATH));

                // Set the identification configuration                RecognitionConfig config = ()
                        .setEncoding(AudioEncoding.LINEAR16) // Set audio encoding format                        .setSampleRateHertz(16000) // Set the audio sampling rate (based on the actual situation of the file)                        .setLanguageCode("en-US") // Set the recognition language                        .build();

                // Set audio data                RecognitionAudio audio = ().setContent(audioBytes).build();

                // Call the synchronization recognition method                SyncRecognizeResponse response = (config, audio);

                // Process the identification results                List&lt;SpeechRecognitionResult&gt; results = ();
                for (SpeechRecognitionResult result : results) {
                    // Each result may contain multiple alternatives (i.e. different identification possibilities)                    SpeechRecognitionAlternative alternative = ().get(0);
                    ("Transcription: %s%n", ());
                }

            } catch (ApiException e) {
                // Handle API exceptions                ("API Exception: " + ());
                ();
            } catch (Exception e) {
                // Handle other exceptions                ("General Exception: " + ());
                ();
            }

        } catch (IOException e) {
            // Handle file reading exception            ("Error reading audio file: " + ());
            ();
        }
    }

    // Create a SpeechClient with service account credentials    private static SpeechClient createSpeechClient() throws IOException {
        // Use Google Services Account Credentials        try (FileInputStream serviceAccountStream =
                     new FileInputStream(CREDENTIALS_FILE_PATH)) {

            // Load service account credentials            GoogleCredentials credentials = (serviceAccountStream);

            // Build SpeechClient            SpeechClient speechClient = (().withCredentials(credentials));
            return speechClient;
        }
    }
}

Please note that we need toCREDENTIALS_FILE_PATHandAUDIO_FILE_PATHReplace the variable with its actual credential file path and audio file path. at the same time,YOUR_VERSIONShould be replaced withgoogle-cloud-speechThe latest version number of the library.

Some students may not understand this code, and this sample code does the following:

Initialized aSpeechClientInstance, it uses credentials loaded from the service account credentials JSON file.
A audio file is read into a byte array.
Created aRecognitionConfigObject, which sets audio encoding, sampling rate, and recognition language.
Created aRecognitionAudioObject, which encapsulates audio data.
CallsyncRecognizeMethods recognize audio as text.
Traversal and print the recognition results.
Exception handling was added in multiple places to catch and handle possible errors.

Note: We want to make sure that the Speech-to-Text API is enabled in our Google Cloud project and a valid service account credentials JSON file is downloaded. Replace the file path to the sample codeCREDENTIALS_FILE_PATH。

In addition, the encoding and sampling rate of the audio file needs to be combined withRecognitionConfigThe settings in the match. In this example, I assume that the audio file is linear PCM encoding at 16kHz. If your audio file uses a different encoding or sampling rate, please change it accordinglyRecognitionConfigsettings in .

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