Base64 encoded audio data containing the user's voice input.
The audio data is encoded in Base64 format to ensure safe transmission and storage within the message system. This encoding allows the voice input to be processed by speech-to-text services and integrated seamlessly into the conversation flow alongside other content types.
The encoded audio maintains the original quality necessary for accurate speech recognition while being compatible with standard data transmission and storage protocols.
The format of the encoded audio data.
Specifies the audio codec and container format used for the voice input. Currently supports "wav" for uncompressed high-quality audio and "mp3" for compressed audio with smaller file sizes. The format information ensures proper decoding and processing of the audio content.
Different formats may be preferred based on quality requirements, file size constraints, and compatibility with speech recognition services.
Type discriminator for identifying the specific content modality.
Provides type-safe discrimination between different content types such as "text", "audio", "image", and "file". This discriminator enables proper type narrowing and ensures that each content type is processed according to its specific characteristics and requirements.
The type field is essential for the multimodal content processing pipeline, allowing the system to route different content types to appropriate handlers while maintaining type safety throughout the conversation flow.
Content type representing audio input from users in the conversation.
Enables natural voice interaction by allowing users to communicate requirements, ask questions, and provide specifications through spoken input. Voice input enhances the vibe coding experience by providing a more natural and efficient way to express complex requirements, especially when describing workflows, business processes, or detailed specifications.
The audio content is processed through speech-to-text capabilities, allowing the AI assistant to understand and respond to voice-based requirements just as effectively as text input. This multimodal approach makes the development conversation more accessible and user-friendly.
Author
Samchon