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In this article, you learn how to use Azure AI Speech voice live with [Azure AI Foundry models](/azure/ai-foundry/concepts/foundry-models-overview) using the VoiceLive SDK for Python.
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## Output
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The output of the script is printed to the console. You see messages indicating the status of the connection, audio stream, and playback. The audio is played back through your speakers or headphones.
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The output of the script is printed to the console. You see messages indicating the status of system. The audio is played back through your speakers or headphones.
The script that you ran creates a log file named `<timestamp>_voicelive.log` in the `logs` folder.
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The default loglevel is set to **INFO** but you can change it by running the quickstart with the command line parameter `--verbose` or by changing the logging config within the code as follows:
The log file contains information about the connection to the Voice Live API, including the request and response data. You can view the log file to see the details of the conversation.
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```text
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2025-05-09 06:56:06,821:websockets.client:DEBUG:= connection is CONNECTING
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2025-05-09 06:56:07,101:websockets.client:DEBUG:> GET /voice-live/realtime?api-version=2025-05-01-preview&model=gpt-4o HTTP/1.1
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<REDACTED FOR BREVITY>
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2025-05-09 06:56:07,551:websockets.client:DEBUG:= connection is OPEN
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2025-05-09 06:56:07,551:websockets.client:DEBUG:< TEXT '{"event_id":"event_5a7NVdtNBVX9JZVuPc9nYK","typ...es":null,"agent":null}}' [1475 bytes]
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## Supported models and regions
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To power the intelligence of your voice agent, you have flexibility and choice in the generative AI model between GPT-4o, GPT-4o-mini, and Phi. Different generative AI models provide different types of capabilities, levels of intelligence, speed/latency of inferencing, and cost. Depending on what matters most for your business and use case, you can choose the model that best suits your needs.
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To power the intelligence of your voice agent, you have flexibility and choice in the generative AI model between GPT-Realtime, GPT-5, GPT-4.1, Phi, and more options. Different generative AI models provide different types of capabilities, levels of intelligence, speed/latency of inferencing, and cost. Depending on what matters most for your business and use case, you can choose the model that best suits your needs.
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All natively supported models – GPT-4o, GPT-4o-mini, and Phi – are fully managed, meaning you don’t have to deploy models, worry about capacity planning, or provisioning throughput. You can use the model you need, and the Voice live API takes care of the rest.
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All natively supported models are fully managed, meaning you don’t have to deploy models, worry about capacity planning, or provisioning throughput. You can use the model you need, and the Voice live API takes care of the rest.
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The Voice live API supports the following models. For supported regions, see the [Azure AI Speech service regions](./regions.md?tabs=voice-live#regions).
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Avatars are charged separately with [the interactive avatar pricing published here.](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services)
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For more details regarding custom voice and avatar training charges, [refer to this pricing note.](/azure/ai-services/speech-service/text-to-speech#model-training-and-hosting-time-for-custom-voice)
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For more information regarding custom voice and avatar training charges, [refer to this pricing note.](/azure/ai-services/speech-service/text-to-speech#model-training-and-hosting-time-for-custom-voice)
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