--- title: "Azure AI Speech (Cognitive Services)" description: "Azure AI Speech is Azure's Cognitive Services text-to-speech API, separate from Azure OpenAI. It provides high-quality neural voices with broader language support and advanced speech customization." type: skill canonical_url: https://claudary.paisolsolutions.com/skills/azure-ai-speech source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:08:09.213Z license: CC-BY-4.0 attribution: "Azure AI Speech (Cognitive Services) — Claudary (https://claudary.paisolsolutions.com/skills/azure-ai-speech)" --- # Azure AI Speech (Cognitive Services) Azure AI Speech is Azure's Cognitive Services text-to-speech API, separate from Azure OpenAI. It provides high-quality neural voices with broader language support and advanced speech customization. ## Overview # Azure AI Speech (Cognitive Services) Azure AI Speech is Azure's Cognitive Services text-to-speech API, separate from Azure OpenAI. It provides high-quality neural voices with broader language support and advanced speech customization. **When to use this vs Azure OpenAI TTS:** - **Azure AI Speech** - More languages, neural voices, SSML support, speech customization - **Azure OpenAI TTS** - OpenAI models, integrated with Azure OpenAI services ## Overview | Property | Details | |-------|-------| | Description | Azure AI Speech is Azure's Cognitive Services text-to-speech API, separate from Azure OpenAI. It provides high-quality neural voices with broader language support and advanced speech customization. | | Provider Route on LiteLLM | `azure/speech/` | ## Quick Start **LiteLLM SDK** ```python showLineNumbers title="SDK Usage" from litellm import speech from pathlib import Path import os os.environ["AZURE_TTS_API_KEY"] = "your-cognitive-services-key" speech_file_path = Path(__file__).parent / "speech.mp3" response = speech( model="azure/speech/azure-tts", voice="alloy", input="Hello, this is Azure AI Speech", api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], ) response.stream_to_file(speech_file_path) ``` **LiteLLM Proxy** ```yaml showLineNumbers title="proxy_config.yaml" model_list: - model_name: azure-speech litellm_params: model: azure/speech/azure-tts api_base: https://eastus.tts.speech.microsoft.com api_key: os.environ/AZURE_TTS_API_KEY ``` ## Setup 1. Create an Azure Cognitive Services resource in the [Azure Portal](https://portal.azure.com) 2. Get your API key from the resource 3. Note your region (e.g., `eastus`, `westus`, `westeurope`) 4. Use the regional endpoint: `https://{region}.tts.speech.microsoft.com` ## Cost Tracking (Pricing) LiteLLM automatically tracks costs for Azure AI Speech based on the number of characters processed. ### Available Models | Model | Voice Type | Cost per 1M Characters | |-------|-----------|----------------------| | `azure/speech/azure-tts` | Neural | $15 | | `azure/speech/azure-tts-hd` | Neural HD | $30 | ### How Costs are Calculated Azure AI Speech charges based on the number of characters in your input text. LiteLLM automatically: - Counts the number of characters in your `input` parameter - Calculates the cost based on the model pricing - Returns the cost in the response object ```python showLineNumbers title="View Request Cost" from litellm import speech response = speech( model="azure/speech/azure-tts", voice="alloy", input="Hello, this is a test message", api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], ) # Access the calculated cost cost = response._hidden_params.get("response_cost") print(f"Request cost: ${cost}") ``` ### Verify Azure Pricing To check the latest Azure AI Speech pricing: 1. Visit the [Azure Pricing Calculator](https://azure.microsoft.com/en-us/pricing/calculator/) 2. Set **Service** to "AI Services" 3. Set **API** to "Azure AI Speech" 4. Select **Text to Speech** and your region 5. View the current pricing per million characters **Note:** Pricing may vary by region and Azure subscription type. ## Voice Mapping LiteLLM automatically maps OpenAI voice names to Azure Neural voices: | OpenAI Voice | Azure Neural Voice | Description | |-------------|-------------------|-------------| | `alloy` | en-US-JennyNeural | Neutral and balanced | | `echo` | en-US-GuyNeural | Warm and upbeat | | `fable` | en-GB-RyanNeural | Expressive and dramatic | | `onyx` | en-US-DavisNeural | Deep and authoritative | | `nova` | en-US-AmberNeural | Friendly and conversational | | `shimmer` | en-US-AriaNeural | Bright and cheerful | ## Supported Parameters ```python showLineNumbers title="All Parameters" response = speech( model="azure/speech/azure-tts", voice="alloy", # Required: Voice selection input="text to convert", # Required: Input text speed=1.0, # Optional: 0.25 to 4.0 (default: 1.0) response_format="mp3", # Optional: mp3, opus, wav, pcm api_base="https://eastus.tts.speech.microsoft.com", api_key="your-key", ) ``` ### Response Formats | Format | Azure Output Format | Sample Rate | |--------|-------------------|-------------| | `mp3` | audio-24khz-48kbitrate-mono-mp3 | 24kHz | | `opus` | ogg-48khz-16bit-mono-opus | 48kHz | | `wav` | riff-24khz-16bit-mono-pcm | 24kHz | | `pcm` | raw-24khz-16bit-mono-pcm | 24kHz | ## Passing Raw SSML LiteLLM automatically detects when your `input` contains SSML (by checking for `` tags) and passes it through to Azure without any transformation. This gives you complete control over speech synthesis. **When to use raw SSML:** - Using the `` element with multilingual voices to translate text (e.g., English text → Spanish speech) - Complex SSML structures with multiple voices or prosody changes - Fine-grained control over pronunciation, breaks, emphasis, and other speech features ### LiteLLM SDK ```python showLineNumbers title="Raw SSML for Multilingual Translation" from litellm import speech # Use element to convert English text to Spanish speech # The element forces the output language regardless of input text language language_code = "es-ES" text = "Hello, how are you today?" # English text voice = "en-US-AvaMultilingualNeural" ssml = f""" {text} """ response = speech( model="azure/speech/azure-tts", voice=voice, input=ssml, # LiteLLM auto-detects SSML and sends as-is api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], ) response.stream_to_file("speech.mp3") ``` ```python showLineNumbers title="Raw SSML with Complex Features" from litellm import speech # Complex SSML with multiple prosody adjustments ssml = """ Welcome to our service! How can I help you today? """ response = speech( model="azure/speech/azure-tts", voice="en-US-JennyNeural", input=ssml, # LiteLLM detects and passes through unchanged api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], ) response.stream_to_file("speech.mp3") ``` ### LiteLLM Proxy ```bash curl http://0.0.0.0:4000/v1/audio/speech \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "model": "azure-speech", "voice": "en-US-AvaMultilingualNeural", "input": "Hello, how are you today?" }' \\ --output speech.mp3 ``` ## Sending Azure-Specific Params Azure AI Speech supports advanced SSML features through optional parameters: - `style`: Speaking style (e.g., "cheerful", "sad", "angry", "whispering") - `styledegree`: Style intensity (0.01 to 2) - `role`: Voice role (e.g., "Girl", "Boy", "SeniorFemale", "SeniorMale") - `lang`: Language code for multilingual voices (e.g., "es-ES", "fr-FR", "hi-IN") ### **LiteLLM SDK** #### Custom Azure Voice ```python showLineNumbers title="Custom Azure Voice" from litellm import speech response = speech( model="azure/speech/azure-tts", voice="en-US-AndrewNeural", # Use Azure voice directly input="Hello, this is a test", api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], response_format="mp3" ) response.stream_to_file("speech.mp3") ``` #### Speaking Style ```python showLineNumbers title="Speaking Style" from litellm import speech response = speech( model="azure/speech/azure-tts", voice="en-US-JennyNeural", # Must be a voice that supports styles input="Who are you? What is chicken dinner?", api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], style="whispering", # Azure-specific: cheerful, sad, angry, whispering, etc. ) response.stream_to_file("speech.mp3") ``` #### Style with Degree and Role ```python showLineNumbers title="Style with Degree and Role" from litellm import speech response = speech( model="azure/speech/azure-tts", voice="en-US-AriaNeural", input="Good morning! How are you today?", api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], style="cheerful", # Azure-specific: Speaking style styledegree="2", # Azure-specific: 0.01 to 2 (intensity) role="SeniorFemale", # Azure-specific: Girl, Boy, SeniorFemale, etc. ) response.stream_to_file("speech.mp3") ``` #### Language Override for Multilingual Voices ```python showLineNumbers title="Language Override" from litellm import speech response = speech( model="azure/speech/azure-tts", voice="en-US-AvaMultilingualNeural", # Multilingual voice input="आप कौन हैं? चिकन डिनर क्या है?", # Hindi text api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], lang="hi-IN", # Azure-specific: Override language ) response.stream_to_file("speech.mp3") ``` ### **LiteLLM AI Gateway (CURL)** First, ensure you have set up your proxy config as shown in the [LiteLLM Proxy setup](#quick-start) above. **Using the model name from your config:** ```yaml model_list: - model_name: azure-speech # This is what you'll use in your API calls litellm_params: model: azure/speech/azure-tts api_base: https://eastus.tts.speech.microsoft.com api_key: os.environ/AZURE_TTS_API_KEY ``` #### Custom Azure Voice ```bash curl http://0.0.0.0:4000/v1/audio/speech \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "model": "azure-speech", "voice": "en-US-AndrewNeural", "input": "Hello, this is a test" }' \\ --output speech.mp3 ``` #### Speaking Style ```bash curl http://0.0.0.0:4000/v1/audio/speech \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "model": "azure-speech", "input": "Who are you? What is chicken dinner?", "voice": "en-US-JennyNeural", "style": "whispering" }' \\ --output speech.mp3 ``` #### Style with Degree and Role ```bash curl http://0.0.0.0:4000/v1/audio/speech \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "model": "azure-speech", "voice": "en-US-AriaNeural", "input": "Good morning! How are you today?", "style": "cheerful", "styledegree": "2", "role": "SeniorFemale" }' \\ --output speech.mp3 ``` #### Language Override ```bash curl http://0.0.0.0:4000/v1/audio/speech \\ -H "Authorization: Bearer sk-1234" \\ -H "Content-Type: application/json" \\ -d '{ "model": "azure-speech", "input": "आप कौन हैं? चिकन डिनर क्या है?", "voice": "en-US-AvaMultilingualNeural", "lang": "hi-IN" }' \\ --output speech.mp3 ``` ### Azure-Specific Parameters Reference | Parameter | Description | Example Values | Notes | |-----------|-------------|----------------|-------| | `style` | Speaking style | `cheerful`, `sad`, `angry`, `excited`, `friendly`, `hopeful`, `shouting`, `terrified`, `unfriendly`, `whispering` | Only supported by certain voices. See [Azure voice styles documentation](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-synthesis-markup-voice#use-speaking-styles-and-roles) | | `styledegree` | Style intensity | `0.01` to `2` | Higher values = more intense. Default is `1` | | `role` | Voice role | `Girl`, `Boy`, `YoungAdultFemale`, `YoungAdultMale`, `OlderAdultFemale`, `OlderAdultMale`, `SeniorFemale`, `SeniorMale` | Only supported by certain voices | | `lang` | Language code | `es-ES`, `fr-FR`, `de-DE`, `hi-IN`, etc. | For multilingual voices. Overrides the default language | ## Async Support ```python showLineNumbers title="Async Usage" import asyncio from litellm import aspeech from pathlib import Path async def generate_speech(): response = await aspeech( model="azure/speech/azure-tts", voice="alloy", input="Hello from async", api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], ) speech_file_path = Path(__file__).parent / "speech.mp3" response.stream_to_file(speech_file_path) asyncio.run(generate_speech()) ``` ## Regional Endpoints Replace `{region}` with your Azure resource region: - US East: `https://eastus.tts.speech.microsoft.com` - US West: `https://westus.tts.speech.microsoft.com` - Europe West: `https://westeurope.tts.speech.microsoft.com` - Asia Southeast: `https://southeastasia.tts.speech.microsoft.com` [Full list of regions](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/regions) ## Advanced Features ### Custom Neural Voices You can use any Azure Neural voice by passing the full voice name: ```python showLineNumbers title="Custom Voice" response = speech( model="azure/speech/azure-tts", voice="en-US-AriaNeural", # Direct Azure voice name input="Using a specific neural voice", api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], ) ``` Browse available voices in the [Azure Speech Gallery](https://speech.microsoft.com/portal/voicegallery). ## Error Handling ```python showLineNumbers title="Error Handling" from litellm import speech from litellm.exceptions import APIError try: response = speech( model="azure/speech/azure-tts", voice="alloy", input="Test message", api_base="https://eastus.tts.speech.microsoft.com", api_key=os.environ["AZURE_TTS_API_KEY"], ) except APIError as e: print(f"Azure Speech error: {e}") ``` ## Reference - [Azure Speech Service Documentation](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/) - [Text-to-Speech REST API](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/rest-text-to-speech) --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/azure-ai-speech) · https://claudary.paisolsolutions.com