Azure AI Voicelive Dotnet V2

Azure.AI.VoiceLive (.NET)

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/azure-ai-voicelive-dotnet from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Azure.AI.VoiceLive (.NET) Real-time voice AI SDK for building bidirectional voice assistants with Azure AI.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Authentication, Client Hierarchy, Voice Options, Supported Models, Error Handling.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Azure AI Voice Live SDK for .NET. Build real-time voice AI applications with bidirectional WebSocket communication.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. `bash dotnet add package Azure.AI.VoiceLive dotnet add package Azure.Identity dotnet add package NAudio # For audio capture/playback Current Versions: Stable v1.0.0, Preview v1.1.0-beta.1 ### 1.
2. Start Session and Configure `csharp using Azure.Identity; using Azure.AI.VoiceLive; var endpoint = new Uri(Environment.GetEnvironmentVariable("AZUREVOICELIVEENDPOINT")); var client = new VoiceLiveClient(endpoint, new DefaultAzureCredential()); var model = "gpt-4o-mini-realtime-preview"; // Start session using VoiceLiveSession session = await client.StartSessionAsync(model); // Configure session VoiceLiveSessionOptions sessionOptions = new() { Model = model, Instructions = "You are a helpful AI assistant.
3. Respond naturally.", Voice = new AzureStandardVoice("en-US-AvaNeural"), TurnDetection = new AzureSemanticVadTurnDetection() { Threshold = 0.5f, PrefixPadding = TimeSpan.FromMilliseconds(300), SilenceDuration = TimeSpan.FromMilliseconds(500) }, InputAudioFormat = InputAudioFormat.Pcm16, OutputAudioFormat = OutputAudioFormat.Pcm16 }; // Set modalities (both text and audio for voice assistants) sessionOptions.Modalities.Clear(); sessionOptions.Modalities.Add(InteractionModality.Text); sessionOptions.Modalities.Add(InteractionModality.Audio); await session.ConfigureSessionAsync(sessionOptions); ` ### 2.
4. Process Events csharp await foreach (SessionUpdate serverEvent in session.GetUpdatesAsync()) { switch (serverEvent) { case SessionUpdateResponseAudioDelta audioDelta: byte[] audioData = audioDelta.Delta.ToArray(); // Play audio via NAudio or other audio library break; case SessionUpdateResponseTextDelta textDelta: Console.Write(textDelta.Delta); break; case SessionUpdateResponseFunctionCallArgumentsDone functionCall: // Handle function call (see Function Calling section) break; case SessionUpdateError error: Console.WriteLine($"Error: {error.Error.Message}"); break; case SessionUpdateResponseDone: Console.WriteLine("\n--- Response complete ---"); break; } } ### 3.
5. Send User Message csharp await session.AddItemAsync(new UserMessageItem("Hello, can you help me?")); await session.StartResponseAsync(); ### 4.
6. Function Calling `csharp // Define function var weatherFunction = new VoiceLiveFunctionDefinition("getcurrentweather") { Description = "Get the current weather for a given location", Parameters = BinaryData.FromString(""" { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state or country" } }, "required": ["location"] } """) }; // Add to session options sessionOptions.Tools.Add(weatherFunction); // Handle function call in event loop if (serverEvent is SessionUpdateResponseFunctionCallArgumentsDone functionCall) { if (functionCall.Name == "getcurrentweather") { var parameters = JsonSerializer.Deserialize<Dictionary<string, string>>(functionCall.Arguments); string location = parameters?["location"] ??
7. ""; // Call external service string weatherInfo = $"The weather in {location} is sunny, 75°F."; // Send response await session.AddItemAsync(new FunctionCallOutputItem(functionCall.CallId, weatherInfo)); await session.StartResponseAsync(); } } ``

Imported Workflow Notes

Imported: Installation

dotnet add package Azure.AI.VoiceLive
dotnet add package Azure.Identity
dotnet add package NAudio                    # For audio capture/playback

Current Versions: Stable v1.0.0, Preview v1.1.0-beta.1

Imported: Core Workflow

1. Start Session and Configure

using Azure.Identity;
using Azure.AI.VoiceLive;

var endpoint = new Uri(Environment.GetEnvironmentVariable("AZURE_VOICELIVE_ENDPOINT"));
var client = new VoiceLiveClient(endpoint, new DefaultAzureCredential());

var model = "gpt-4o-mini-realtime-preview";

// Start session
using VoiceLiveSession session = await client.StartSessionAsync(model);

// Configure session
VoiceLiveSessionOptions sessionOptions = new()
{
    Model = model,
    Instructions = "You are a helpful AI assistant. Respond naturally.",
    Voice = new AzureStandardVoice("en-US-AvaNeural"),
    TurnDetection = new AzureSemanticVadTurnDetection()
    {
        Threshold = 0.5f,
        PrefixPadding = TimeSpan.FromMilliseconds(300),
        SilenceDuration = TimeSpan.FromMilliseconds(500)
    },
    InputAudioFormat = InputAudioFormat.Pcm16,
    OutputAudioFormat = OutputAudioFormat.Pcm16
};

// Set modalities (both text and audio for voice assistants)
sessionOptions.Modalities.Clear();
sessionOptions.Modalities.Add(InteractionModality.Text);
sessionOptions.Modalities.Add(InteractionModality.Audio);

await session.ConfigureSessionAsync(sessionOptions);

2. Process Events

await foreach (SessionUpdate serverEvent in session.GetUpdatesAsync())
{
    switch (serverEvent)
    {
        case SessionUpdateResponseAudioDelta audioDelta:
            byte[] audioData = audioDelta.Delta.ToArray();
            // Play audio via NAudio or other audio library
            break;

        case SessionUpdateResponseTextDelta textDelta:
            Console.Write(textDelta.Delta);
            break;

        case SessionUpdateResponseFunctionCallArgumentsDone functionCall:
            // Handle function call (see Function Calling section)
            break;

        case SessionUpdateError error:
            Console.WriteLine($"Error: {error.Error.Message}");
            break;

        case SessionUpdateResponseDone:
            Console.WriteLine("\n--- Response complete ---");
            break;
    }
}

3. Send User Message

await session.AddItemAsync(new UserMessageItem("Hello, can you help me?"));
await session.StartResponseAsync();

4. Function Calling

// Define function
var weatherFunction = new VoiceLiveFunctionDefinition("get_current_weather")
{
    Description = "Get the current weather for a given location",
    Parameters = BinaryData.FromString("""
        {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state or country"
                }
            },
            "required": ["location"]
        }
        """)
};

// Add to session options
sessionOptions.Tools.Add(weatherFunction);

// Handle function call in event loop
if (serverEvent is SessionUpdateResponseFunctionCallArgumentsDone functionCall)
{
    if (functionCall.Name == "get_current_weather")
    {
        var parameters = JsonSerializer.Deserialize<Dictionary<string, string>>(functionCall.Arguments);
        string location = parameters?["location"] ?? "";

        // Call external service
        string weatherInfo = $"The weather in {location} is sunny, 75°F.";

        // Send response
        await session.AddItemAsync(new FunctionCallOutputItem(functionCall.CallId, weatherInfo));
        await session.StartResponseAsync();
    }
}

Imported: Environment Variables

AZURE_VOICELIVE_ENDPOINT=https://<resource>.services.ai.azure.com/
AZURE_VOICELIVE_MODEL=gpt-4o-realtime-preview
AZURE_VOICELIVE_VOICE=en-US-AvaNeural
# Optional: API key if not using Entra ID
AZURE_VOICELIVE_API_KEY=<your-api-key>

Examples

Example 1: Ask for the upstream workflow directly

Use @azure-ai-voicelive-dotnet-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @azure-ai-voicelive-dotnet-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @azure-ai-voicelive-dotnet-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @azure-ai-voicelive-dotnet-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Always set both modalities — Include Text and Audio for voice assistants
• Use AzureSemanticVadTurnDetection — Provides natural conversation flow
• Configure appropriate silence duration — 500ms typical to avoid premature cutoffs
• Use using statement — Ensures proper session disposal
• Handle all event types — Check for errors, audio, text, and function calls
• Use DefaultAzureCredential — Never hardcode API keys
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.

Imported Operating Notes

Imported: Best Practices

1. Always set both modalities — Include Text and Audio for voice assistants
2. Use AzureSemanticVadTurnDetection — Provides natural conversation flow
3. Configure appropriate silence duration — 500ms typical to avoid premature cutoffs
4. Use using statement — Ensures proper session disposal
5. Handle all event types — Check for errors, audio, text, and function calls
6. Use DefaultAzureCredential — Never hardcode API keys

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/azure-ai-voicelive-dotnet, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Key Types Reference

Imported: Reference Links

Imported: Authentication

Microsoft Entra ID (Recommended)

using Azure.Identity;
using Azure.AI.VoiceLive;

Uri endpoint = new Uri("https://your-resource.cognitiveservices.azure.com");
DefaultAzureCredential credential = new DefaultAzureCredential();
VoiceLiveClient client = new VoiceLiveClient(endpoint, credential);

Required Role: Cognitive Services User (assign in Azure Portal → Access control)

API Key

Uri endpoint = new Uri("https://your-resource.cognitiveservices.azure.com");
AzureKeyCredential credential = new AzureKeyCredential("your-api-key");
VoiceLiveClient client = new VoiceLiveClient(endpoint, credential);

Imported: Client Hierarchy

VoiceLiveClient
└── VoiceLiveSession (WebSocket connection)
    ├── ConfigureSessionAsync()
    ├── GetUpdatesAsync() → SessionUpdate events
    ├── AddItemAsync() → UserMessageItem, FunctionCallOutputItem
    ├── SendAudioAsync()
    └── StartResponseAsync()

Imported: Voice Options

Imported: Supported Models

Imported: Error Handling

if (serverEvent is SessionUpdateError error)
{
    if (error.Error.Message.Contains("Cancellation failed: no active response"))
    {
        // Benign error, can ignore
    }
    else
    {
        Console.WriteLine($"Error: {error.Error.Message}");
    }
}

Imported: Audio Configuration

• Input Format: InputAudioFormat.Pcm16 (16-bit PCM)
• Output Format: OutputAudioFormat.Pcm16
• Sample Rate: 24kHz recommended
• Channels: Mono

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.