Speech-to-Text and Audio Pipelines

Speech-to-text (automatic speech recognition or ASR) is the foundation of voice-enabled AI applications. Converting audio into accurate, actionable text unlocks transcription, meeting notes, accessibility, and voice search—but building a production pipeline requires more than just an API call. You need to handle noise, preserve speaker identity, maintain precision timing, and often enhance raw transcripts with large language models to fix errors or extract meaning. This series covers the full journey: from ASR fundamentals and Whisper-class models, through streaming and speaker diarization, to production-ready systems that combine transcription with prompt engineering for meeting summaries, multi-language support, and real-time processing.

Each article is standalone but sequenced to build intuition—start with the basics if you're new to audio, or jump to specific topics like streaming or LLM post-processing if you already have transcription working. By the end of this series, you'll understand the tradeoffs between accuracy, latency, and cost; how to implement speaker identification and timestamp alignment; how to denoise and preprocess audio; and how to integrate transcription into end-to-end voice workflows using prompts to engineer better outputs.

Articles in this series

• Speech to Text Basics: ASR Explained
• Whisper API Tutorial: Transcribe Audio Files
• Real-Time Speech to Text: Streaming ASR
• Speaker Diarization: Who Spoke When
• Timestamp Alignment: Word-Level Timing
• Noise Reduction for Audio Clarity
• Post-Process Transcripts with LLM Prompts
• Multi-Language Speech Recognition Pipeline
• Meeting Transcription and Summarization
• Production ASR: Optimization and Scaling