Audio Support in OpenAI's Chat Completions API
OpenAI has recently introduced audio support in their Chat Completions API, opening up exciting new possibilities for developers working with audio and text interactions. This feature is powered by the new gpt-4o-audio-preview model, which brings advanced voice capabilities to the familiar Chat Completions API interface.
Key Features
The new audio support in the Chat Completions API offers several compelling features:
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Flexible Input Handling: The API can now process any combination of text and audio inputs, allowing for more versatile applications.
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Natural, Steerable Voices: Similar to the Realtime API, developers can use prompting to shape various aspects of the generated audio, including language, pronunciation, and emotional range.
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Tool Calling Integration: The audio support seamlessly integrates with existing tool calling functionality, enabling complex workflows that combine audio, text, and external tools.
Practical Example
To demonstrate how to use this new functionality, let's look at a simple example using the instructor library:
"""python from openai import OpenAI from pydantic import BaseModel import instructor from instructor.multimodal import Audio import base64
client = instructor.from_openai(OpenAI())
class Person(BaseModel): name: str age: int
resp = client.chat.completions.create( model="gpt-4o-audio-preview", response_model=Person, modalities=["text"], audio={"voice": "alloy", "format": "wav"}, messages=[ { "role": "user", "content": [ "Extract the following information from the audio", Audio.from_path("./output.wav"), ], }, ], )
print(resp)
Expected output: Person(name='Jason', age=20)
"""
In this example, we're using the gpt-4o-audio-preview model to extract information from an audio file. The API processes the audio input and returns structured data (a Person object with name and age) based on the content of the audio.
Use Cases
The addition of audio support to the Chat Completions API enables a wide range of applications:
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Voice-based Personal Assistants: Create more natural and context-aware voice interfaces for various applications.
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Audio Content Analysis: Automatically extract information, sentiments, or key points from audio recordings or podcasts.
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Language Learning Tools: Develop interactive language learning applications that can process and respond to spoken language.
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Accessibility Features: Improve accessibility in applications by providing audio-based interactions and text-to-speech capabilities.
Considerations
While this new feature is exciting, it's important to note that it's best suited for asynchronous use cases that don't require extremely low latencies. For more dynamic and real-time interactions, OpenAI recommends using their Realtime API.
As with any AI-powered feature, it's crucial to consider ethical implications and potential biases in audio processing and generation. Always test thoroughly and consider the diversity of your user base when implementing these features.