LLM speech for speech transcription and translation (preview)

Prerequisites

• An Azure Speech in Foundry Tools resource in one of the regions where the LLM speech API is available. For the current list of supported regions, see Speech service regions.

• An audio file (less than 2 hours long and less than 300 MB in size) in one of the formats and codecs supported by the batch transcription API: WAV, MP3, OPUS/OGG, FLAC, WMA, AAC, ALAW in WAV container, MULAW in WAV container, AMR, WebM, and SPEEX. For more information about supported audio formats, see supported audio formats.

Use the LLM speech API

Supported languages

The following languages are currently supported for both transcribe and translate tasks:

• English, Chinese, German, French, Italian, Japanese, Spanish, Portuguese, and Korean.

Upload audio

You can provide audio data in the following ways:

• Pass inline audio data.

  --form 'audio=@"YourAudioFile"'

• Upload audio file from a public audioUrl.

  --form 'definition": "{\"audioUrl\": \"https://crbn.us/hello.wav"}"'

In the sections below, inline audio upload is used as an example.

Call the LLM speech API

Make a multipart/form-data POST request to the transcriptions endpoint with the audio file and the request body properties.

The following example shows how to transcribe an audio file with a specified locale. If you know the locale of the audio file, you can specify it to improve transcription accuracy and minimize the latency.

• Replace YourSpeechResoureKey with your Speech resource key.
• Replace YourServiceRegion with your Speech resource region.
• Replace YourAudioFile with the path to your audio file.

Use LLM speech to transcribe an audio

You can transcribe audio in the input language without specifying a locale code. The model automatically detects and selects the appropriate language based on the audio content.

curl --location 'https://<YourServiceRegion>.api.cognitive.microsoft.com/speechtotext/transcriptions:transcribe?api-version=2025-10-15' \
--header 'Content-Type: multipart/form-data' \
--header 'Ocp-Apim-Subscription-Key: <YourSpeechResourceKey>' \
--form 'audio=@"YourAudioFile.wav"' \
--form 'definition={
  "enhancedMode": {
    "enabled": true,
    "task": "transcribe"
  }
}'

Use LLM speech to translate an audio file

You can translate audio into a specified target language. To enable translation, you must provide the target language code in the request.

curl --location 'https://<YourServiceRegion>.api.cognitive.microsoft.com/speechtotext/transcriptions:transcribe?api-version=2025-10-15' \
--header 'Content-Type: multipart/form-data' \
--header 'Ocp-Apim-Subscription-Key: <YourSpeechResourceKey>' \
--form 'audio=@"YourAudioFile.wav"' \
--form 'definition={
  "enhancedMode": {
    "enabled": true,
    "task": "translate",
    "targetLanguage": "ko"
  }
}'

Use prompt-tuning to alter performance

You can provide an optional text to guide the output style for transcribe or translate task.

curl --location 'https://<YourServiceRegion>.api.cognitive.microsoft.com/speechtotext/transcriptions:transcribe?api-version=2025-10-15' \
--header 'Content-Type: multipart/form-data' \
--header 'Ocp-Apim-Subscription-Key: <YourSpeechResourceKey>' \
--form 'audio=@"YourAudioFile.wav"' \
--form 'definition={
  "enhancedMode": {
    "enabled": true,
    "task": "transcribe",
    "prompt": ["Output must be in lexical format."]
  }
}'

Here are some best practices for prompts:

• Prompts are subject to a maximum length of 4,096 characters.
• Prompts should preferably be written in English.
• Prompts can guide output formatting. By default, responses use a display format optimized for readability. To enforce lexical formatting, include: Output must be in lexical format.
• Prompts can amplify the salience of specific phrases or acronyms, improving recognition likelihood. Use: Pay attention to *phrase1*, *phrase2*, …. For best results, limit the number of phrases per prompt.
• Prompts that aren’t related to speech tasks (e.g., Tell me a story.) are typically disregarded.

More configuration options

You can combine additional configuration options with fast transcription to enable enhanced features such as diarization, profanityFilterMode, and channels.

curl --location 'https://<YourServiceRegion>.api.cognitive.microsoft.com/speechtotext/transcriptions:transcribe?api-version=2025-10-15' \
--header 'Content-Type: multipart/form-data' \
--header 'Ocp-Apim-Subscription-Key: <YourSpeechResourceKey>' \
--form 'audio=@"YourAudioFile.wav"' \
--form 'definition={
  "enhancedMode": {
    "enabled": true,
    "task": "transcribe",
    "prompt": ["Output must be in lexical format."]
  },
  "diarization": {
    "maxSpeakers": 2,
    "enabled": true
  },
  "profanityFilterMode": "Masked"
}'

Some configuration options, such as locales and phraseLists, are either not required or not applicable with LLM speech, and can be omitted from the request. Learn more from configuration options of fast transcription.

Sample response

In the JSON response, the combinedPhrases property contains the full transcribed or translated text, and the phrases property contains segment-level and word-level details.

{
    "durationMilliseconds": 57187,
    "combinedPhrases": [
        {
            "text": "With custom speech,you can evaluate and improve the microsoft speech to text accuracy for your applications and products 现成的语音转文本,利用通用语言模型作为一个基本模型,使用microsoft自有数据进行训练,并反映常用的口语。此基础模型使用那些代表各常见领域的方言和发音进行了预先训练。 Quand vous effectuez une demande de reconnaissance vocale, le modèle de base le plus récent pour chaque langue prise en charge est utilisé par défaut. Le modèle de base fonctionne très bien dans la plupart des scénarios de reconnaissance vocale. A custom model can be used to augment the base model to improve recognition of domain specific vocabulary specified to the application by providing text data to train the model. It can also be used to improve recognition based for the specific audio conditions of the application by providing audio data with reference transcriptions."
        }
    ],
    "phrases": [
        {
            "offsetMilliseconds": 80,
            "durationMilliseconds": 6960,
            "text": "With custom speech,you can evaluate and improve the microsoft speech to text accuracy for your applications and products.",
            "words": [
                {
                    "text": "with",
                    "offsetMilliseconds": 80,
                    "durationMilliseconds": 160
                },
                {
                    "text": "custom",
                    "offsetMilliseconds": 240,
                    "durationMilliseconds": 480
                },

                {
                    "text": "speech",
                    "offsetMilliseconds": 720,
                    "durationMilliseconds": 360
                },,
        // More transcription results...
        // Redacted for brevity
            ],
            "locale": "en-us",
            "confidence": 0
        },
        {
            "offsetMilliseconds": 8000,
            "durationMilliseconds": 8600,
            "text": "现成的语音转文本,利用通用语言模型作为一个基本模型,使用microsoft自有数据进行训练,并反映常用的口语。此基础模型使用那些代表各常见领域的方言和发音进行了预先训练。",
            "words": [
                {
                    "text": "现",
                    "offsetMilliseconds": 8000,
                    "durationMilliseconds": 40
                },
                {
                    "text": "成",
                    "offsetMilliseconds": 8040,
                    "durationMilliseconds": 40
                },
        // More transcription results...
        // Redacted for brevity
                {
                    "text": "训",
                    "offsetMilliseconds": 16400,
                    "durationMilliseconds": 40
                },
                {
                    "text": "练",
                    "offsetMilliseconds": 16560,
                    "durationMilliseconds": 40
                },
            ],
            "locale": "zh-cn",
            "confidence": 0
        // More transcription results...
        // Redacted for brevity
                {
                    "text": "with",
                    "offsetMilliseconds": 54720,
                    "durationMilliseconds": 200
                },
                {
                    "text": "reference",
                    "offsetMilliseconds": 54920,
                    "durationMilliseconds": 360
                },
                {
                    "text": "transcriptions.",
                    "offsetMilliseconds": 55280,
                    "durationMilliseconds": 1200
                }
            ],
            "locale": "en-us",
            "confidence": 0
        }
    ]
}

The response format is consistent with other existing speech-to-text outputs, such as fast transcription and batch transcription. Key differences include:

• Word-level durationMilliseconds and offsetMilliseconds are not supported for translate task.
• Diarization is not supported for translate task, only the speaker1 label is returned.
• confidence is not available and always 0.

Reference documentation | Package (PyPi) | GitHub Samples

Prerequisites

• An Azure subscription. Create one for free.
• Python 3.9 or later version. If you don't have a suitable version of Python installed, you can follow the instructions in the VS Code Python Tutorial for the easiest way of installing Python on your operating system.
• A Microsoft Foundry resource created in one of the supported regions. For more information about region availability, see Region support.
• A sample .wav audio file to transcribe.

Microsoft Entra ID prerequisites

For the recommended keyless authentication with Microsoft Entra ID, you need to:

• Install the Azure CLI used for keyless authentication with Microsoft Entra ID.
• Assign the Cognitive Services User role to your user account. You can assign roles in the Azure portal under Access control (IAM) > Add role assignment.

Setup

1. Create a new folder named llm-speech-quickstart and go to the quickstart folder with the following command:

   mkdir llm-speech-quickstart && cd llm-speech-quickstart
   

2. Create and activate a virtual Python environment to install the packages you need for this tutorial. We recommend you always use a virtual or conda environment when installing Python packages. Otherwise, you can break your global installation of Python. If you already have Python 3.9 or higher installed, create a virtual environment by using the following commands:

   • Windows
   • Linux
   • macOS

   py -3 -m venv .venv
   .venv\Scripts\Activate.ps1
   

   When you activate the Python environment, running python or pip from the command line uses the Python interpreter in the .venv folder of your application. Use the deactivate command to exit the Python virtual environment. You can reactivate it later when needed.

3. Create a file named requirements.txt. Add the following packages to the file:

   azure-ai-transcription
   azure-identity
   

4. Install the packages:

   pip install -r requirements.txt
   

Set environment variables

You need to retrieve your resource endpoint and API key for authentication.

1. Sign in to Foundry portal (classic).

2. Select Management center from the left menu.

3. Select Connected resources on the left, and find your Microsoft Foundry resource (or add a connection if it isn't there). Then copy the API Key and Target (endpoint) values. Use these values to set environment variables.

4. Set the following environment variables:

   • Windows
   • Linux
   • macOS

   $env:AZURE_SPEECH_ENDPOINT="<your-speech-endpoint>"
   $env:AZURE_SPEECH_API_KEY="<your-api-key>"
   

Transcribe audio with LLM speech

LLM speech uses the EnhancedModeProperties class to enable large-language-model-enhanced transcription. The model automatically detects the language in your audio.

1. Create a file named llm_speech_transcribe.py with the following code:

   import os
   from dotenv import load_dotenv
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.transcription import TranscriptionClient
   
   load_dotenv()
   from azure.ai.transcription.models import (
       TranscriptionContent,
       TranscriptionOptions,
       EnhancedModeProperties,
   )
   
   # Get configuration from environment variables
   endpoint = os.environ["AZURE_SPEECH_ENDPOINT"]
   
   # Optional: we recommend using role based access control (RBAC) for production scenarios
   api_key = os.environ["AZURE_SPEECH_API_KEY"]
   
   if api_key:
       credential = AzureKeyCredential(api_key)
   else:
       from azure.identity import DefaultAzureCredential
       credential = DefaultAzureCredential()   
   
   # Create the transcription client
   client = TranscriptionClient(endpoint=endpoint, credential=credential)
   
   # Path to your audio file (replace with your own file path)
   audio_file_path = "<path-to-your-audio-file.wav>"
   
   # Open and read the audio file
   with open(audio_file_path, "rb") as audio_file:
       # Create enhanced mode properties for LLM speech transcription
       enhanced_mode = EnhancedModeProperties(
           task="transcribe",
           prompt=[],
       )
   
       # Create transcription options with enhanced mode
       options = TranscriptionOptions(enhanced_mode=enhanced_mode)
   
       # Create the request content
       request_content = TranscriptionContent(definition=options, audio=audio_file)
   
       # Transcribe the audio
       result = client.transcribe(request_content)
   
       # Print the transcription result
       print(f"Transcription: {result.combined_phrases[0].text}")
   
       # Print detailed phrase information
       if result.phrases:
           print("\nDetailed phrases:")
           for phrase in result.phrases:
               print(f"  [{phrase.offset_milliseconds}ms]: {phrase.text}")
   

   Reference: TranscriptionClient | TranscriptionContent | TranscriptionOptions | EnhancedModeProperties

2. Replace <path-to-your-audio-file.wav> with the path to your audio file. The service supports WAV, MP3, FLAC, OGG, and other common audio formats.

3. Run the Python script.

   python llm_speech_transcribe.py
   

Output

The script prints the transcription result to the console:

Transcription: Hi there. This is a sample voice recording created for speech synthesis testing. The quick brown fox jumps over the lazy dog. Just a fun way to include every letter of the alphabet. Numbers, like one, two, three, are spoken clearly. Let's see how well this voice captures tone, timing, and natural rhythm. This audio is provided by samplefiles.com.

Detailed phrases:
  [40ms]: Hi there.
  [800ms]: This is a sample voice recording created for speech synthesis testing.
  [5440ms]: The quick brown fox jumps over the lazy dog.
  [9040ms]: Just a fun way to include every letter of the alphabet.
  [12720ms]: Numbers, like one, two, three, are spoken clearly.
  [17200ms]: Let's see how well this voice captures tone, timing, and natural rhythm.
  [22480ms]: This audio is provided by samplefiles.com.

Translate audio with LLM speech

You can also use LLM speech to translate audio into a target language. Set the task to translate and specify the target_language.

1. Create a file named llm_speech_translate.py with the following code:

   import os
   from dotenv import load_dotenv
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.transcription import TranscriptionClient
   
   load_dotenv()
   from azure.ai.transcription.models import (
       TranscriptionContent,
       TranscriptionOptions,
       EnhancedModeProperties,
   )
   
   # Get configuration from environment variables
   endpoint = os.environ["AZURE_SPEECH_ENDPOINT"]
   
   # Optional: we recommend using role based access control (RBAC) for production scenarios
   api_key = os.environ["AZURE_SPEECH_API_KEY"]
   
   if api_key:
       credential = AzureKeyCredential(api_key)
   else:
       from azure.identity import DefaultAzureCredential
       credential = DefaultAzureCredential()   
   
   # Create the transcription client
   client = TranscriptionClient(endpoint=endpoint, credential=credential)
   
   # Path to your audio file (replace with your own file path)
   audio_file_path = "<path-to-your-audio-file.wav>"
   
   # Open and read the audio file
   with open(audio_file_path, "rb") as audio_file:
       # Create enhanced mode properties for LLM speech translation
       # Translate to another language
       enhanced_mode = EnhancedModeProperties(
           task="translate",
           target_language="de",
           prompt=[
               "Translate the following audio to German.",
               "Convert number words to numbers."
           ],  # Optional prompts to guide the enhanced mode
       )
   
       # Create transcription options with enhanced mode
       options = TranscriptionOptions(locales=["en-US"], enhanced_mode=enhanced_mode)
   
       # Create the request content
       request_content = TranscriptionContent(definition=options, audio=audio_file)
   
       # Translate the audio
       result = client.transcribe(request_content)
   
       # Print the translation result
       print(f"Translation: {result.combined_phrases[0].text}")
   

   Reference: TranscriptionClient | EnhancedModeProperties

2. Replace <path-to-your-audio-file.wav> with the path to your audio file.

3. Run the Python script.

   python llm_speech_translate.py
   

Use prompt-tuning

You can provide an optional prompt to guide the output style for transcription or translation tasks. Replace the prompt value in the EnhancedModeProperties object.

import os
from dotenv import load_dotenv
from azure.core.credentials import AzureKeyCredential
from azure.ai.transcription import TranscriptionClient

load_dotenv()
from azure.ai.transcription.models import (
TranscriptionContent,
TranscriptionOptions,
EnhancedModeProperties,
)

# Get configuration from environment variables
endpoint = os.environ["AZURE_SPEECH_ENDPOINT"]

# Optional: we recommend using role based access control (RBAC) for production scenarios
api_key = os.environ["AZURE_SPEECH_API_KEY"]

if api_key:
credential = AzureKeyCredential(api_key)
else:
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()   

# Create the transcription client
client = TranscriptionClient(endpoint=endpoint, credential=credential)

# Path to your audio file (replace with your own file path)
audio_file_path = "<path-to-your-audio-file.wav>"

# Open and read the audio file
with open(audio_file_path, "rb") as audio_file:
    # Create enhanced mode properties for LLM speech transcription
    enhanced_mode = EnhancedModeProperties(
        task="transcribe",
        prompt=[
            "Create lexical output only,",
            "Convert number words to numbers."
        ],  # Optional prompts to guide the enhanced mode, prompt="Create lexical transcription.")
    )

    # Create transcription options with enhanced mode
    options = TranscriptionOptions(enhanced_mode=enhanced_mode)

    # Create the request content
    request_content = TranscriptionContent(definition=options, audio=audio_file)

    # Print request content for debugging
    print("Request Content:", request_content, "\n")
    
    # Transcribe the audio
    result = client.transcribe(request_content)

    # Print the transcription result
    print(f"Transcription: {result.combined_phrases[0].text}")

    # Print detailed phrase information
    if result.phrases:
        print("\nDetailed phrases:")
        for phrase in result.phrases:

Best practices for prompts:

• Prompts are subject to a maximum length of 4,096 characters.
• Prompts should preferably be written in English.
• Use Output must be in lexical format. to enforce lexical formatting instead of the default display format.
• Use Pay attention to *phrase1*, *phrase2*, … to improve recognition of specific phrases or acronyms.

Reference: EnhancedModeProperties

Output

The script prints the transcription result to the console:

Transcription: Hello, this is a test of the LLM speech transcription service.

Detailed phrases:
  [0ms]: Hello, this is a test
  [1500ms]: of the LLM speech transcription service.

Reference documentation | Package (Maven) | GitHub Samples

Prerequisites

• An Azure subscription. Create one for free.
• Java Development Kit (JDK) 8 or later.
• Apache Maven for dependency management and building the project.
• A Speech resource in one of the supported regions. For more information about region availability, see Speech service supported regions.
• A sample .wav audio file to transcribe.

Set up the environment

1. Create a new folder named llm-speech-quickstart and navigate to it:

   mkdir llm-speech-quickstart && cd llm-speech-quickstart
   

2. Create a pom.xml file in the root of your project directory with the following content:

   <?xml version="1.0" encoding="UTF-8"?>
   <project xmlns="http://maven.apache.org/POM/4.0.0"
               xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
               xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
       <modelVersion>4.0.0</modelVersion>
   
       <groupId>com.example</groupId>
       <artifactId>transcription-quickstart</artifactId>
       <version>1.0.0</version>
       <packaging>jar</packaging>
   
       <name>Speech Transcription Quickstart</name>
       <description>Quickstart sample for Azure Speech Transcription client library.</description>
       <url>https://github.com/Azure/azure-sdk-for-java</url>
   
       <properties>
           <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
       </properties>
   
       <dependencies>
           <dependency>
               <groupId>com.azure</groupId>
               <artifactId>azure-ai-speech-transcription</artifactId>
               <version>1.0.0-beta.2</version>
           </dependency>
           <dependency>
               <groupId>com.azure</groupId>
               <artifactId>azure-identity</artifactId>
               <version>1.18.1</version>
           </dependency>
       </dependencies>
   
       <build>
           <sourceDirectory>.</sourceDirectory>
           <plugins>
               <plugin>
                   <groupId>org.apache.maven.plugins</groupId>
                   <artifactId>maven-compiler-plugin</artifactId>
                   <version>3.11.0</version>
                   <configuration>
                       <source>1.8</source>
                       <target>1.8</target>
                   </configuration>
               </plugin>
               <plugin>
                   <groupId>org.codehaus.mojo</groupId>
                   <artifactId>exec-maven-plugin</artifactId>
                   <version>3.1.0</version>
                   <configuration>
                       <mainClass>TranscriptionQuickstart</mainClass>
                   </configuration>
               </plugin>
           </plugins>
       </build>
   </project>
   

   Note

   The <sourceDirectory>.</sourceDirectory> configuration tells Maven to look for Java source files in the current directory instead of the default src/main/java structure. This configuration change allows for a simpler flat project structure.

3. Install the dependencies:

   mvn clean install
   

Set environment variables

Your application must be authenticated to access the Speech service. The SDK supports both API key and Microsoft Entra ID authentication. It automatically detects which method to use based on the environment variables you set.

First, set the endpoint for your Speech resource. Replace <your-speech-endpoint> with your actual resource name:

$env:AZURE_SPEECH_ENDPOINT="<your-speech-endpoint>"

export AZURE_SPEECH_ENDPOINT="<your-speech-endpoint>"

export AZURE_SPEECH_ENDPOINT="<your-speech-endpoint>"

Then, choose one of the following authentication methods:

Option 1: API key authentication (recommended for getting started)

Set the API key environment variable:

setx AZURE_SPEECH_API_KEY <your-speech-key>

export AZURE_SPEECH_API_KEY=<your-speech-key>

export AZURE_SPEECH_API_KEY=<your-speech-key>

Option 2: Microsoft Entra ID authentication (recommended for production)

Instead of setting AZURE_SPEECH_API_KEY, configure one of the following credential sources:

• Azure CLI: Run az login on your development machine.
• Managed Identity: For apps running in Azure (App Service, Azure Functions, VMs).
• Environment Variables: Set AZURE_TENANT_ID, AZURE_CLIENT_ID, and AZURE_CLIENT_SECRET.
• Visual Studio Code or IntelliJ: Sign in through your IDE.

You also need to assign the Cognitive Services User role to your identity:

az role assignment create --assignee <your-identity> \
    --role "Cognitive Services User" \
    --scope /subscriptions/<subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.CognitiveServices/accounts/<speech-resource-name>

Transcribe audio with LLM speech

LLM speech uses the EnhancedModeOptions class to enable large-language-model-enhanced transcription. Enhanced mode is automatically enabled when you create an EnhancedModeOptions instance. The model automatically detects the language in your audio.

Create a file named LlmSpeechQuickstart.java in your project directory with the following code:

import com.azure.ai.speech.transcription.TranscriptionClient;
import com.azure.ai.speech.transcription.TranscriptionClientBuilder;
import com.azure.ai.speech.transcription.models.AudioFileDetails;
import com.azure.ai.speech.transcription.models.EnhancedModeOptions;
import com.azure.ai.speech.transcription.models.TranscriptionOptions;
import com.azure.ai.speech.transcription.models.TranscriptionResult;
import com.azure.core.credential.KeyCredential;
import com.azure.core.util.BinaryData;
import com.azure.identity.DefaultAzureCredentialBuilder;

import java.nio.file.Files;
import java.nio.file.Paths;

public class LlmSpeechQuickstart {
    public static void main(String[] args) {
        try {
            // Get credentials from environment variables
            String endpoint = System.getenv("AZURE_SPEECH_ENDPOINT");
            String apiKey = System.getenv("AZURE_SPEECH_API_KEY");

            // Create client with API key or Entra ID authentication
            TranscriptionClientBuilder builder = new TranscriptionClientBuilder()
                .endpoint(endpoint);

            TranscriptionClient client;
            if (apiKey != null && !apiKey.isEmpty()) {
                // Use API key authentication
                client = builder.credential(new KeyCredential(apiKey)).buildClient();
            } else {
                // Use Entra ID authentication
                client = builder.credential(new DefaultAzureCredentialBuilder().build()).buildClient();
            }

            // Load audio file
            String audioFilePath = "<path-to-your-audio-file.wav>";
            byte[] audioData = Files.readAllBytes(Paths.get(audioFilePath));

            // Create audio file details
            AudioFileDetails audioFileDetails = new AudioFileDetails(BinaryData.fromBytes(audioData));

            // Create enhanced mode options for LLM speech transcription
            // Enhanced mode is automatically enabled when you create EnhancedModeOptions
            EnhancedModeOptions enhancedModeOptions = new EnhancedModeOptions()
                .setTask("transcribe");

            // Create transcription options with enhanced mode
            TranscriptionOptions options = new TranscriptionOptions(audioFileDetails)
                .setEnhancedModeOptions(enhancedModeOptions);

            // Transcribe the audio
            TranscriptionResult result = client.transcribe(options);

            // Print result
            System.out.println("Transcription:");
            result.getCombinedPhrases().forEach(phrase ->
                System.out.println(phrase.getText())
            );

            // Print detailed phrase information
            if (result.getPhrases() != null) {
                System.out.println("\nDetailed phrases:");
                result.getPhrases().forEach(phrase ->
                    System.out.println(String.format("  [%dms] (%s): %s",
                        phrase.getOffset(),
                        phrase.getLocale(),
                        phrase.getText()))
                );
            }

        } catch (Exception e) {
            System.err.println("Error: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

Replace <path-to-your-audio-file.wav> with the path to your audio file. The service supports WAV, MP3, FLAC, OGG, and other common audio formats.

Run the application

Run the application using Maven:

mvn compile exec:java

Translate audio by using LLM speech

You can also use LLM speech to translate audio into a target language. Modify the EnhancedModeOptions configuration to set the task to translate and specify the target language.

Create a file named LlmSpeechTranslate.java with the following code:

import com.azure.ai.speech.transcription.TranscriptionClient;
import com.azure.ai.speech.transcription.TranscriptionClientBuilder;
import com.azure.ai.speech.transcription.models.AudioFileDetails;
import com.azure.ai.speech.transcription.models.EnhancedModeOptions;
import com.azure.ai.speech.transcription.models.TranscriptionOptions;
import com.azure.ai.speech.transcription.models.TranscriptionResult;
import com.azure.core.credential.KeyCredential;
import com.azure.core.util.BinaryData;

import java.nio.file.Files;
import java.nio.file.Paths;

public class LlmSpeechTranslate {
    public static void main(String[] args) {
        try {
            // Get credentials from environment variables
            String endpoint = System.getenv("AZURE_SPEECH_ENDPOINT");
            String apiKey = System.getenv("AZURE_SPEECH_API_KEY");

            // Create client
            TranscriptionClient client = new TranscriptionClientBuilder()
                .endpoint(endpoint)
                .credential(new KeyCredential(apiKey))
                .buildClient();

            // Load audio file
            String audioFilePath = "<path-to-your-audio-file.wav>";
            byte[] audioData = Files.readAllBytes(Paths.get(audioFilePath));

            // Create audio file details
            AudioFileDetails audioFileDetails = new AudioFileDetails(BinaryData.fromBytes(audioData));

            // Create enhanced mode options for LLM speech translation
            // Translate to Korean (supported languages: en, zh, de, fr, it, ja, es, pt, ko)
            EnhancedModeOptions enhancedModeOptions = new EnhancedModeOptions()
                .setTask("translate")
                .setTargetLanguage("ko");

            // Create transcription options with enhanced mode
            TranscriptionOptions options = new TranscriptionOptions(audioFileDetails)
                .setEnhancedModeOptions(enhancedModeOptions);

            // Translate the audio
            TranscriptionResult result = client.transcribe(options);

            // Print translation result
            System.out.println("Translation:");
            result.getCombinedPhrases().forEach(phrase ->
                System.out.println(phrase.getText())
            );

        } catch (Exception e) {
            System.err.println("Error: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

Replace <path-to-your-audio-file.wav> with the path to your audio file.

To run the translation example, update the pom.xml main class configuration or run:

mvn exec:java -Dexec.mainClass="LlmSpeechTranslate"

Use prompt-tuning

You can provide an optional prompt to guide the output style for transcription or translation tasks.

import java.util.Arrays;

// Create enhanced mode options with prompt-tuning
EnhancedModeOptions enhancedModeOptions = new EnhancedModeOptions()
    .setTask("transcribe")
    .setPrompts(Arrays.asList("Output must be in lexical format."));

// Create transcription options with enhanced mode
TranscriptionOptions options = new TranscriptionOptions(audioFileDetails)
    .setEnhancedModeOptions(enhancedModeOptions);

Best practices for prompts:

• Prompts are subject to a maximum length of 4,096 characters.
• Prompts should preferably be written in English.
• Use Output must be in lexical format. to enforce lexical formatting instead of the default display format.
• Use Pay attention to *phrase1*, *phrase2*, … to improve recognition of specific phrases or acronyms.

Clean up resources

When you finish the quickstart, delete the project folder:

rm -rf llm-speech-quickstart