What is an Audio Buffer in Transcription?

An audio buffer is a contiguous block of random-access memory (RAM) allocated to store digital audio samples temporarily while they move between hardware input devices and processing software.

In real-time speech transcription, audio arrives continuously from the microphone and system audio channels at fixed sample rates (e.g., 16,000 samples per second). However, neural networks like OpenAI Whisper do not process single audio samples; they process batches of spectrogram frames. The audio buffer serves as the essential reservoir that decouples real-time hardware ingestion from episodic machine learning inference.

The mechanics of a circular ring buffer

Audio engines typically deploy a circular ring buffer (FIFO) to handle continuous speech streams:

                  Write Pointer (CoreAudio HAL Input)
                                 ↓
[Chunk 0] [Chunk 1] [Chunk 2] [Chunk 3] [Chunk 4] [Chunk 5]
              ↑
    Read Pointer (Whisper Inference Worker)

1. Lock-free writing: High-priority CoreAudio threads write incoming PCM audio samples into the buffer head. Because audio threads must never block, write operations avoid mutex locks.
2. Chunk extraction: When the buffer accumulates sufficient audio (e.g., 3 to 10 seconds), a background worker thread reads the window, generates a log-Mel spectrogram, and dispatches it to the neural engine.
3. Sliding window overlap: To prevent cutting words in half at chunk boundaries, buffers often maintain a 500ms sliding window overlap between consecutive inference cycles.

Balancing the latency vs accuracy trade-off

Selecting audio buffer window sizes involves a critical engineering trade-off:

Buffer Window Size	Latency	Transcription Accuracy	Contextual Coherence
Short (1.0 - 2.0s)	Ultra-low (<1s)	Moderate (fragmented context)	Struggles with complex syntax
Balanced (3.0 - 5.0s)	Low (2 - 4s)	High (complete phrases captured)	Optimal for real-time live preview
Long (10.0 - 30.0s)	High (10 - 30s)	Very High (full sentence context)	Ideal for post-call batch review

Audio buffering in Oyma

In Oyma on macOS:

• Audio buffers are managed through a memory-safe Swift pipeline.
• Voice Activity Detection (VAD) monitors buffer energy levels: when a speaker pauses, Oyma flushes the accumulated buffer immediately into Whisper, ensuring that completed thoughts appear on screen with minimal delay.
• Buffers are cleared from RAM once transcribed and appended to local Markdown files, keeping Oyma‘s memory footprint under 200 MB throughout multi-hour marathon sessions.