Real-time Voice Assistant with Personalized Context
Develop a sophisticated, real-time voice assistant capable of transcribing spoken queries, understanding context, and providing personalized responses. This challenge involves integrating advanced speech-to-text capabilities, managing conversational state, and leveraging a dynamic knowledge base. The solution will demonstrate the power of OpenAI's agentic capabilities for complex, multi-turn interactions, ensuring smooth user experience akin to next-generation AI assistants. Focus on designing an agent that can not only answer questions but also infer user intent from conversational flow and adapt its responses based on historical interactions and profile data, all while maintaining low latency for a fluid conversational experience.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an intelligent voice assistant using OpenAI Agents SDK to process real-time audio, manage personalized conversational context, and serve dynamic responses.
How work is evaluated
The evaluation will assess the voice assistant's ability to accurately transcribe speech, maintain conversational context, provide personalized and coherent responses, and adhere to safety guidelines, all while performing in near real-time.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Diarization Accuracy
Checks if speaker diarization is correctly identified for at least 2 distinct speakers in multi-speaker audio.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Personalized Context Use
Verifies that the agent response explicitly leverages personalized context (e.g., mentioning specific meeting details, user preferences).
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Safety Compliance (Giskard)
Ensures the response does not contain any detected safety violations flagged by Giskard.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Transcription Word Error Rate
Measures the accuracy of the speech-to-text transcription. • target: 0.05 • range: 0-0.2
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Response Coherence Score
Evaluates the logical flow and relevance of the agent's response to the query and context (0-1). • target: 0.9 • range: 0.7-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Response Latency
Measures the time taken from audio input end to response start (in seconds). • target: 1.5 • range: 0-3
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master the OpenAI Agents SDK for defining agent capabilities, tools, and conversational memory.
Implement robust audio processing pipelines using OpenAI's Whisper API for accurate speech-to-text and speaker diarization.
Design and build custom tools/functions for the OpenAI Agent to access external services and a personalized knowledge base.
Utilize Featuretools to generate dynamic user features from interaction history for personalized context management.
Configure and deploy a custom lightweight model (e.g., for intent classification or sentiment analysis) using TorchServe, accessible via agent tools.
Integrate Giskard for continuous evaluation of the agent's responses, ensuring accuracy, coherence, and adherence to safety policies.
Orchestrate a real-time interaction loop, handling audio input, agent processing, and synthesized speech output for a fluid user experience.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The evaluation will assess the voice assistant's ability to accurately transcribe speech, maintain conversational context, provide personalized and coherent responses, and adhere to safety guidelines, all while perfor...
Challenge input
Simulated audio file (e.g., WAV) with multiple speakers and specific user query.
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'transcript': 'string', 'speaker_diarization': [{'speaker': 'string', 'text': 'string', 'start': 'float', 'end': 'float'}], 'agent_response': 'str...
- Checks if speaker diarization is correctly identified for at least 2 distinct speakers in multi-speaker audio.
- Verifies that the agent response explicitly leverages personalized context (e.g., mentioning specific meeti...
- Ensures the response does not contain any detected safety violations flagged by Giskard.
- Transcription Word Error Rate target: 5%
- 1 public reference case
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Diarization Accuracy · Weight 1
- Personalized Context Use · Weight 1
- Safety Compliance (Giskard) · Weight 1
- Transcription Word Error Rate · Weight 1
- Response Coherence Score · Weight 1
- Response Latency · Weight 1
Metrics
- Transcription Word Error Rate · Target 0.05
- Response Coherence Score · Target 0.9
- Response Latency · Target 1.5
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: Simulated audio file (e.g., WAV) with multiple speakers and specific user query.
Output: {'transcript': 'string', 'speaker_diarization': [{'speaker': 'string', 'text': 'string', 'start': 'float', 'end': 'float'}], 'agent_response': 'string', 'personalized_context_used': 'boolean'}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.