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Corporation"},"license":"MIT","homepage":"https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/voicelive/ai-voicelive/README.md","keywords":["node","azure","cloud","typescript","browser","isomorphic"],"repository":{"type":"git","url":"git+https://github.com/Azure/azure-sdk-for-js.git","directory":"sdk/voicelive/ai-voicelive"},"description":"A generated SDK for VoiceLiveClient.","maintainers":[{"name":"azure-sdk","email":"azure-sdk-npmjs@microsoft.com"},{"name":"microsoft1es","email":"npmjs@microsoft.com"},{"name":"microsoft-oss-releases","email":"microsoft-oss-publishing@microsoft.com"}],"readme":"# Azure VoiceLive client library for JavaScript\n\nAzure VoiceLive is a managed service that enables low-latency, high-quality speech-to-speech interactions for voice agents. The service consolidates speech recognition, generative AI, and text-to-speech functionalities into a single, unified interface, providing an end-to-end solution for creating seamless voice-driven experiences.\n\nUse the client library to:\n\n- Create real-time voice assistants and conversational agents\n- Build speech-to-speech applications with minimal latency\n- Integrate advanced conversational features like noise suppression and echo cancellation\n- Leverage multiple AI models (GPT-Realtime, GPT-Realtime-Mini, Phi) for different use cases\n- Implement function calling and tool integration for dynamic responses\n- Create avatar-enabled voice interactions with visual components\n\n> Note: This package supports both browser and Node.js environments. WebSocket connections are used for real-time communication.\n\n## Getting started\n\n### Currently supported environments\n\n- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)\n- Latest versions of Safari, Chrome, Edge and Firefox\n\n### Prerequisites\n\n- An [Azure subscription](https://azure.microsoft.com/free/)\n- An [Azure AI Foundry resource](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource) with Voice Live API access\n\n### Install the package\n\nInstall the Azure VoiceLive client library using npm:\n\n```bash\nnpm install @azure/ai-voicelive\n```\n\n### Install the identity library\n\nVoiceLive clients authenticate using the Azure Identity Library. Install it as well:\n\n```bash\nnpm install @azure/identity\n```\n\n### Configure TypeScript\n\nTypeScript users need to have Node type definitions installed:\n\n```bash\nnpm install @types/node\n```\n\nYou also need to enable `compilerOptions.allowSyntheticDefaultImports` in your tsconfig.json. Note that if you have enabled `compilerOptions.esModuleInterop`, `allowSyntheticDefaultImports` is enabled by default.\n\n### JavaScript Bundle\n\nTo use this client library in the browser, first you need to use a bundler. For details on how to do this, please refer to our [bundling documentation](https://aka.ms/AzureSDKBundling).\n\n## Key concepts\n\n### VoiceLiveClient\n\nThe primary interface for establishing connections to the Azure VoiceLive service. Use this client to authenticate and create sessions for real-time voice interactions.\n\n### VoiceLiveSession\n\nRepresents an active WebSocket connection for real-time voice communication. This class handles bidirectional communication, allowing you to send audio input and receive audio output, text transcriptions, and other events in real-time.\n\n### Session Configuration\n\nThe service uses session configuration to control various aspects of voice interaction:\n\n- **Turn Detection**: Configure how the service detects when users start and stop speaking\n- **Audio Processing**: Enable noise suppression and echo cancellation\n- **Voice Selection**: Choose from standard Azure voices, custom voices, avatar-sync voices, or preview Azure realtime native voices\n- **Model Selection**: Select the AI model (GPT-Realtime, GPT-Realtime-Mini, Azure-Realtime, Phi variants) that best fits your needs\n\n### Models and Capabilities\n\nThe VoiceLive API supports multiple AI models with different capabilities:\n\n| Model                | Description                                  | Use Case                          |\n| -------------------- | -------------------------------------------- | --------------------------------- |\n| `gpt-realtime`       | Real-time audio processing model             | High-quality conversational AI    |\n| `gpt-realtime-mini`  | Lightweight real-time model                  | Fast, efficient interactions      |\n| `azure-realtime`     | Azure-native realtime model                 | Native voices and WebRTC scenarios |\n| `phi4-mm-realtime`   | Phi model with multimodal support            | Cost-effective voice applications |\n\n### Conversational Enhancements\n\nThe VoiceLive API provides Azure-specific enhancements:\n\n- **Azure Semantic VAD**: Advanced voice activity detection that removes filler words\n- **Noise Suppression**: Reduces environmental background noise\n- **Echo Cancellation**: Removes echo from the model's own voice\n- **End-of-Turn Detection**: Allows natural pauses without premature interruption\n\n### Session Modes\n\nVoiceLive supports two distinct modes for creating sessions:\n\n#### Model Mode (LLM as Main Actor)\n\nIn model mode, the LLM model is the primary AI actor. You specify a model name and optionally configure tools like functions or MCP servers.\n\n```typescript snippet:ReadmeSampleModelModeSession\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\nconst client = new VoiceLiveClient(endpoint, credential);\n\n// Model mode - LLM is the main actor\nconst session = await client.startSession(\"gpt-realtime\");\n```\n\n#### Agent Mode (Agent as Main Actor)\n\nIn agent mode, a Foundry agent is the primary AI actor. The agent's configuration (tools, instructions, temperature) is managed in the Azure AI Foundry portal, not in session code. This is ideal for:\n\n- Voice-enabling existing text-based agents\n- Scenarios where agent configuration should be managed centrally\n- Simplified integration without runtime configuration\n\n```typescript snippet:ReadmeSampleAgentModeSession\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\nconst client = new VoiceLiveClient(endpoint, credential);\n\n// Agent mode - Foundry agent is the main actor\nconst session = await client.startSession({\n  agent: {\n    agentName: \"my-agent\",\n    projectName: \"my-foundry-project\",\n  },\n});\n```\n\n## Authenticating with Azure Active Directory\n\nThe VoiceLive service relies on Azure Active Directory to authenticate requests to its APIs. The [`@azure/identity`](https://www.npmjs.com/package/@azure/identity) package provides a variety of credential types that your application can use to do this. The [README for `@azure/identity`](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/identity/identity/README.md) provides more details and samples to get you started.\n\nTo interact with the Azure VoiceLive service, you need to create an instance of the `VoiceLiveClient` class, a **service endpoint** and a credential object. The examples shown in this document use a credential object named [`DefaultAzureCredential`][defaultazurecredential], which is appropriate for most scenarios, including local development and production environments. We recommend using a [managed identity][managed_identity] for authentication in production environments.\n\nYou can find more information on different ways of authenticating and their corresponding credential types in the [Azure Identity documentation][azure_identity].\n\nHere's a quick example. First, import `DefaultAzureCredential` and `VoiceLiveClient`:\n\n```ts snippet:ReadmeSampleCreateClient\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\n\n// Build the URL to reach your AI Foundry resource\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\n\n// Create the VoiceLive client\nconst client = new VoiceLiveClient(endpoint, credential);\n```\n\n### Authentication with API Key\n\nFor development scenarios, you can also authenticate using an API key:\n\n```ts snippet:ReadmeSampleCreateClientWithApiKey\nimport { AzureKeyCredential } from \"@azure/core-auth\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\nconst credential = new AzureKeyCredential(\"your-api-key\");\n\nconst client = new VoiceLiveClient(endpoint, credential);\n```\n\n## Examples\n\nThe following sections provide code snippets that cover some of the common tasks using Azure VoiceLive. The scenarios covered here consist of:\n\n- [Creating a basic voice assistant](#creating-a-basic-voice-assistant)\n- [Creating an agent-powered voice assistant](#creating-an-agent-powered-voice-assistant)\n- [Configuring session options](#configuring-session-options)\n- [Streaming text input](#streaming-text-input)\n- [Handling real-time events](#handling-real-time-events)\n- [Implementing function calling](#implementing-function-calling)\n\n### Creating a basic voice assistant\n\nThis example shows how to create a simple voice assistant that can handle speech-to-speech interactions:\n\n```ts snippet:ReadmeSampleBasicVoiceAssistant\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\n\n// Create the client\nconst client = new VoiceLiveClient(endpoint, credential);\n\n// Create and connect a session\nconst session = await client.startSession(\"gpt-realtime-mini\");\n\n// Configure session for voice conversation\nawait session.updateSession({\n  modalities: [\"text\", \"audio\"],\n  instructions: \"You are a helpful AI assistant. Respond naturally and conversationally.\",\n  voice: {\n    type: \"azure-standard\",\n    name: \"en-US-AvaNeural\",\n  },\n  turnDetection: {\n    type: \"server_vad\",\n    threshold: 0.5,\n    prefixPaddingInMs: 300,\n    silenceDurationInMs: 500,\n  },\n  inputAudioFormat: \"pcm16\",\n  outputAudioFormat: \"pcm16\",\n});\n```\n\n### Creating an agent-powered voice assistant\n\nThis example shows how to create a voice assistant powered by a Foundry agent. In agent mode, the agent's configuration is managed in the Azure AI Foundry portal:\n\n```ts snippet:ReadmeSampleAgentVoiceAssistant\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\n\n// Create the client\nconst client = new VoiceLiveClient(endpoint, credential);\n\n// Create and connect a session with an agent as the main actor\nconst session = await client.startSession({\n  agent: {\n    agentName: \"your-agent-name\",\n    projectName: \"your-foundry-project\",\n  },\n});\n\n// Subscribe to events - audio settings can still be configured\nconst subscription = session.subscribe({\n  onResponseAudioDelta: async (event, context) => {\n    // Handle audio from the agent\n    playAudioChunk(event.delta);\n  },\n  onResponseTextDelta: async (event, context) => {\n    console.log(\"Agent:\", event.delta);\n  },\n});\n\n// Send audio data from microphone\nfunction sendAudioChunk(audioBuffer: ArrayBuffer) {\n  session.sendAudio(audioBuffer);\n}\n```\n\n### Configuring session options\n\nYou can customize various aspects of the voice interaction:\n\n```ts snippet:ReadmeSampleAdvancedConfiguration\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\nconst client = new VoiceLiveClient(endpoint, credential);\nconst session = await client.startSession(\"gpt-realtime\");\n\n// Advanced session configuration\nawait session.updateSession({\n  modalities: [\"audio\", \"text\"],\n  instructions: \"You are a customer service representative. Be helpful and professional.\",\n  voice: {\n    type: \"azure-custom\",\n    name: \"your-custom-voice-name\",\n    endpointId: \"your-custom-voice-endpoint\",\n  },\n  turnDetection: {\n    type: \"server_vad\",\n    threshold: 0.6,\n    prefixPaddingInMs: 200,\n    silenceDurationInMs: 300,\n  },\n  inputAudioFormat: \"pcm16\",\n  outputAudioFormat: \"pcm16\",\n});\n```\n\nFor preview native voices on the `azure-realtime` model, use the dedicated voice type:\n\n```ts snippet:ReadmeSampleRealtimeNativeVoice\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\nconst client = new VoiceLiveClient(endpoint, credential);\nconst session = await client.startSession(\"azure-realtime\");\n\nawait session.updateSession({\n  voice: {\n    type: \"azure-realtime-native\",\n    name: \"ava\",\n  },\n});\n```\n\n### Streaming text input\n\nInstead of sending audio, you can stream text into a conversation item incrementally with `input_text.delta` events and finish with a single `input_text.done` event. This is useful when the text itself is produced token-by-token (for example, forwarded from another model):\n\n```ts snippet:ReadmeSampleStreamInputText\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\nconst client = new VoiceLiveClient(endpoint, credential);\nconst session = await client.startSession(\"gpt-realtime-mini\");\n\n// Create the conversation item that the streamed text is appended to\nconst itemId = \"user-message-1\";\nawait session.addConversationItem({\n  type: \"message\",\n  role: \"user\",\n  id: itemId,\n  content: [{ type: \"input_text\", text: \"\" }],\n});\n\n// Stream the text in chunks as `input_text.delta` events\nfor (const chunk of [\"Tell me \", \"a fun fact \", \"about the ocean.\"]) {\n  await session.sendEvent({\n    type: \"input_text.delta\",\n    id: itemId,\n    delta: chunk,\n  });\n}\n\n// Signal that the streamed text is complete with a single `input_text.done` event\nawait session.sendEvent({\n  type: \"input_text.done\",\n  id: itemId,\n});\n\n// Ask the model to respond to the streamed text\nawait session.sendEvent({ type: \"response.create\" });\n```\n\n### Handling real-time events\n\nThe VoiceLive client provides event-driven communication for real-time interactions:\n\n```ts snippet:ReadmeSampleEventHandling\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\nconst client = new VoiceLiveClient(endpoint, credential);\nconst session = await client.startSession(\"gpt-realtime-mini\");\n\n// Set up event handlers using subscription pattern\nconst subscription = session.subscribe({\n  onResponseAudioDelta: async (event, context) => {\n    // Handle incoming audio chunks\n    const audioData = event.delta;\n    // Play audio using Web Audio API or other audio system\n    playAudioChunk(audioData);\n  },\n\n  onResponseTextDelta: async (event, context) => {\n    // Handle incoming text deltas\n    console.log(\"Assistant:\", event.delta);\n  },\n\n  onConversationItemInputAudioTranscriptionCompleted: async (event, context) => {\n    // Handle user speech transcription\n    console.log(\"User said:\", event.transcript);\n  },\n});\n\n// Send audio data from microphone\nfunction sendAudioChunk(audioBuffer: ArrayBuffer) {\n  session.sendAudio(audioBuffer);\n}\n```\n\n### Implementing function calling\n\nEnable your voice assistant to call external functions and tools:\n\n```ts snippet:ReadmeSampleFunctionCalling\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { VoiceLiveClient } from \"@azure/ai-voicelive\";\n\nconst credential = new DefaultAzureCredential();\nconst endpoint = \"https://your-resource.cognitiveservices.azure.com\";\nconst client = new VoiceLiveClient(endpoint, credential);\nconst session = await client.startSession(\"gpt-realtime-mini\");\n\n// Define available functions\nconst tools = [\n  {\n    type: \"function\",\n    name: \"get_weather\",\n    description: \"Get current weather for a location\",\n    parameters: {\n      type: \"object\",\n      properties: {\n        location: {\n          type: \"string\",\n          description: \"The city and state or country\",\n        },\n      },\n      required: [\"location\"],\n    },\n  },\n];\n\n// Configure session with tools\nawait session.updateSession({\n  modalities: [\"audio\", \"text\"],\n  instructions:\n    \"You can help users with weather information. Use the get_weather function when needed.\",\n  tools: tools,\n  toolChoice: \"auto\",\n});\n\n// Handle function calls\nconst subscription = session.subscribe({\n  onResponseFunctionCallArgumentsDone: async (event, context) => {\n    if (event.name === \"get_weather\") {\n      const args = JSON.parse(event.arguments);\n      const weatherData = await getWeatherData(args.location);\n\n      // Send function result back\n      await session.addConversationItem({\n        type: \"function_call_output\",\n        callId: event.callId,\n        output: JSON.stringify(weatherData),\n      });\n\n      // Request response generation\n      await session.sendEvent({\n        type: \"response.create\",\n      });\n    }\n  },\n});\n```\n\n## Troubleshooting\n\n### Common errors and exceptions\n\n**Authentication Errors**: If you receive authentication errors, verify that:\n\n- Your Azure AI Foundry resource is correctly configured\n- Your API key or credential has the necessary permissions\n- The endpoint URL is correct and accessible\n\n**WebSocket Connection Issues**: VoiceLive uses WebSocket connections. Ensure that:\n\n- Your network allows WebSocket connections\n- Firewall rules permit connections to `*.cognitiveservices.azure.com`\n- Browser policies allow WebSocket and microphone access (for browser usage)\n\n**Audio Issues**: For audio-related problems:\n\n- Verify microphone permissions in the browser\n- Check that audio formats (PCM16, PCM24) are supported\n- Ensure proper audio context setup for playback\n\n### Telemetry / Distributed Tracing\n\nThe VoiceLive SDK supports [distributed tracing](https://learn.microsoft.com/azure/azure-monitor/app/distributed-tracing) via the `@azure/core-tracing` package. Tracing is **no-op by default** — no spans are created unless you opt in by registering an OpenTelemetry-compatible tracing provider.\n\n#### How it works\n\nWhen tracing is enabled, the SDK automatically creates spans for the session lifecycle:\n\n```\nconnect (parent span — open for the entire session lifetime)\n├── send session.update\n├── send conversation.item.create\n├── send response.create\n├── recv session.created\n├── recv response.done          ← turn count incremented, token usage recorded\n├── send response.cancel        ← interruption count incremented\n├── recv error                  ← error event recorded\n└── close                       ← session-level counters finalized\n```\n\nSpan attributes follow the [OpenTelemetry GenAI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/) (`gen_ai.system`, `gen_ai.operation.name`, `gen_ai.request.model`, etc.) plus VoiceLive-specific extensions (`gen_ai.voice.session_id`, `gen_ai.voice.turn_count`, `gen_ai.voice.audio_bytes_sent`, …). Session-level metrics are aggregated onto the `connect` span when the session ends.\n\n#### Enable tracing (Node.js, CommonJS — recommended)\n\nFor CommonJS apps, use the standard Azure SDK instrumentation bridge:\n\n```bash\nnpm install @azure/opentelemetry-instrumentation-azure-sdk @opentelemetry/instrumentation @opentelemetry/sdk-trace-node\n```\n\n```\nconst {\n  NodeTracerProvider,\n  SimpleSpanProcessor,\n  ConsoleSpanExporter,\n} = require(\"@opentelemetry/sdk-trace-node\");\nconst { registerInstrumentations } = require(\"@opentelemetry/instrumentation\");\nconst { createAzureSdkInstrumentation } = require(\"@azure/opentelemetry-instrumentation-azure-sdk\");\n\n// 1. Configure an OpenTelemetry tracer provider\nconst provider = new NodeTracerProvider({\n  spanProcessors: [new SimpleSpanProcessor(new ConsoleSpanExporter())],\n});\nprovider.register();\n\n// 2. Register the Azure SDK instrumentation BEFORE requiring @azure/ai-voicelive\nregisterInstrumentations({\n  instrumentations: [createAzureSdkInstrumentation()],\n});\n\n// 3. Use VoiceLive — spans are emitted automatically\nconst { VoiceLiveClient } = require(\"@azure/ai-voicelive\");\nconst { DefaultAzureCredential } = require(\"@azure/identity\");\n\nconst client = new VoiceLiveClient(endpoint, new DefaultAzureCredential());\nconst session = client.createSession(\"gpt-realtime\");\nawait session.connect(); // creates \"connect\" span\n```\n\n#### Enable tracing (Node.js ESM and browsers)\n\n`createAzureSdkInstrumentation` relies on CommonJS require-hooks and produces no spans when the SDK is loaded as ESM (i.e. `\"type\": \"module\"` packages or browser bundlers like Vite). For those environments, register a minimal `Instrumenter` directly through `useInstrumenter` from `@azure/core-tracing`:\n\n```\nimport { useInstrumenter } from \"@azure/core-tracing\";\nimport { trace, context } from \"@opentelemetry/api\";\n\nuseInstrumenter({\n  startSpan(name, spanOptions) {\n    const ctx = spanOptions.tracingContext ?? context.active();\n    const tracer = trace.getTracer(spanOptions.packageName ?? \"@azure/ai-voicelive\", spanOptions.packageVersion);\n    const span = tracer.startSpan(name, { attributes: spanOptions.spanAttributes }, ctx);\n    return {\n      span: {\n        end: () => span.end(),\n        setStatus: (s) => { if (s.status === \"error\") span.setStatus({ code: 2, message: String(s.error ?? \"\") }); },\n        setAttribute: (k, v) => span.setAttribute(k, v),\n        isRecording: () => span.isRecording(),\n        recordException: (e) => span.recordException(e),\n      },\n      tracingContext: trace.setSpan(ctx, span),\n    };\n  },\n  withContext: (ctx, fn, ...args) => context.with(ctx, fn, undefined, ...args),\n  parseTraceparentHeader: () => undefined,\n  createRequestHeaders: () => ({}),\n});\n```\n\nThis produces spans identical to the CommonJS bridge.\n\n> Complete, runnable samples:\n> - **Node.js (ESM):** [`samples/telemetry/`](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/voicelive/ai-voicelive/samples/telemetry/README.md) — console exporter and Azure Monitor variant.\n> - **Browser (Vite):** [`samples/telemetry-browser/`](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/voicelive/ai-voicelive/samples/telemetry-browser/README.md) — in-page span viewer.\n\n#### Span attributes\n\nThe SDK sets attributes following [GenAI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/):\n\n| Attribute | Description |\n|---|---|\n| `az.namespace` | Always `Microsoft.CognitiveServices` |\n| `gen_ai.system` | Always `az.ai.voicelive` |\n| `gen_ai.operation.name` | `connect`, `send`, `recv`, or `close` |\n| `gen_ai.request.model` | The model name (e.g., `gpt-realtime`) |\n| `gen_ai.voice.session_id` | Voice session ID from `session.created` |\n| `gen_ai.voice.turn_count` | Total completed response turns (connect span) |\n| `gen_ai.voice.interruption_count` | Number of `response.cancel` events (connect span) |\n| `gen_ai.voice.audio_bytes_sent` | Total audio bytes sent (connect span) |\n| `gen_ai.voice.audio_bytes_received` | Total audio bytes received (connect span) |\n| `gen_ai.voice.first_token_latency_ms` | Time from `response.create` to first audio/text delta |\n| `gen_ai.usage.input_tokens` | Input token count from `response.done` |\n| `gen_ai.usage.output_tokens` | Output token count from `response.done` |\n\n### Logging\n\nEnabling logging may help uncover useful information about failures. In order to see a log of WebSocket messages and responses, set the `AZURE_LOG_LEVEL` environment variable to `info`. Alternatively, logging can be enabled at runtime by calling `setLogLevel` in the `@azure/logger`:\n\n```ts snippet:SetLogLevel\nimport { setLogLevel } from \"@azure/logger\";\n\nsetLogLevel(\"info\");\n```\n\nFor more detailed instructions on how to enable logs, you can look at the [@azure/logger package docs](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/core/logger).\n\n## Next steps\n\nYou can find more code samples through the following links:\n\n- [VoiceLive Samples (JavaScript/TypeScript)](https://github.com/rhurey/azure-sdk-for-js/tree/rhurey/move_voicelive/sdk/voicelive/ai-voicelive/samples)\n- [VoiceLive Test Cases](https://github.com/rhurey/azure-sdk-for-js/tree/rhurey/move_voicelive/sdk/voicelive/ai-voicelive/test)\n\n## Contributing\n\nIf you'd like to contribute to this library, please read the [contributing guide](https://github.com/Azure/azure-sdk-for-js/blob/main/CONTRIBUTING.md) to learn more about how to build and test the code.\n\n[defaultazurecredential]: https://learn.microsoft.com/javascript/api/@azure/identity/defaultazurecredential?view=azure-node-latest\n[managed_identity]: https://learn.microsoft.com/azure/active-directory/managed-identities-azure-resources/overview\n[azure_identity]: https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest\n","readmeFilename":"README.md"}