Audio-to-Text Conversion with Advanced Embeddings and Retrieval
Industry
E-commerce
Project
AI services
Client
US-based E-commerce platform
Our Role
Solution Provider
Technologies
AWS Transcribe, Huggingface (SDG)
The Challenge
A sophisticated system is needed to generate high-quality embeddings from the text to transform simple audio-to-text conversion. These embeddings should be stored in a vector database for swift access and efficient search and retrieval operations. The system must also support advanced features like similarity and semantic searches, which require a deep understanding of context. Additionally, incorporating retrieval-augmented generation (RAG) for Q&A tasks can enhance capabilities, particularly in call centers.
The Solution
Integrating voice and video search capabilities enhances team efficiency by enabling rapid access to information and responses. The solution employs an Audio-to-Text Conversion Pipeline to transform spoken prompts into text, followed by Text Embedding Generation for creating vector representations that integrate with a Vector Database. Our AI model then conducts Similarity and Semantic Search on this data, delivering relevant responses tailored to user needs.
The system streamlines audio upload and processing for effective interactions, utilizing APIs for quick data retrieval. Features like Metadata Indexing and Search Optimization improve search efficiency, while RAG (Retrieval-Augmented Generation) and call center functionalities enhance customer engagement. The final responses can be converted into high-quality audio or video formats, ensuring users receive information in their preferred medium.
Key Benefits
Enhanced Understanding of Context
Generating high-quality embeddings can improve transcription accuracy to 95%, reduce manual correction needs by over 40%, and save significant time and resources.
Improved Search and Retrieval Efficiency
Search response times can be reduced to under one second, enhancing data retrieval ability by up to 70% and facilitating faster decision-making processes.
Q&A Capabilities
Incorporating retrieval-augmented generation (RAG) can improve query resolution times by 50%, leading to an average customer satisfaction increase of 20% through accurate and timely responses.
Similarity and Semantic Searches
With semantic searches, the system can boost search result relevance by up to 80%, resulting in a 30% improvement in the effectiveness of strategic decision-making through better-quality analytics.
