Building a chatbot with ASR [P]

A developer building a chatbot for a startup is tackling a common but critical challenge: adding speech-to-text functionality on a tight budget while meeting stringent security and compliance requirements. The core constraint is a firm need to avoid external APIs, pushing the solution toward self-hosted, open-source Automatic Speech Recognition (ASR) models. The founder is actively researching options like Meta's Whisper and NVIDIA's Parakeet RNNT models but is seeking community guidance to navigate the practical trade-offs for a minimum viable product (MVP) or pilot launch.

The central dilemma pits the convenience and performance of paid API services against the control and data privacy of self-managed solutions. The developer is explicitly ready to handle deployment challenges, indicating the priority is on data sovereignty and long-term cost control over initial development speed. Key considerations include the computational resources required for real-time inference, model accuracy for their specific use case, and the maintenance overhead of an in-house ASR pipeline. The community's response will likely highlight the maturity of Whisper for offline transcription, the latency profiles of different models, and potential hidden costs in GPU infrastructure versus API pricing.