macloop 0.1.1

Low-latency macOS audio capture and processing (AEC/NS) powered by Rust

macloop

Low-latency macOS audio capture and processing for Python, powered by Rust.

• System audio and microphone capture via ScreenCaptureKit
• WebRTC-based AEC (echo cancellation) and NS (noise suppression)
• Sync and async streaming APIs

Requirements

• macOS (Apple Silicon or Intel)
• Python 3.9+

Installation

pip install macloop

Quick Start (sync)

import macloop

sources = macloop.list_audio_sources()
display = next(s for s in sources if s["type"] == "display")

cfg = macloop.AudioProcessingConfig(
    sample_rate=16000,
    channels=1,
    sample_format="i16",
    enable_aec=True,
    enable_ns=False,
)

with macloop.Capture(
    display_id=display["display_id"],
    config=cfg,
    capture_system=True,
    capture_mic=True,
) as stream:
    for chunk in stream:
        print(chunk.source, len(chunk.samples))
        # chunk.samples is numpy.ndarray (int16 or float32)

Quick Start (asyncio)

import asyncio
import macloop

async def main():
    sources = macloop.list_audio_sources()
    display = next(s for s in sources if s["type"] == "display")
    cfg = macloop.AudioProcessingConfig()

    async with macloop.Capture(
        display_id=display["display_id"],
        config=cfg,
        capture_system=True,
        capture_mic=True,
    ) as stream:
        async for chunk in stream:
            print(chunk.source, len(chunk.samples))

asyncio.run(main())

Data Format

AudioChunk.samples is a numpy.ndarray:

• np.int16 when sample_format="i16"
• np.float32 when sample_format="f32"

This makes it convenient to pass chunks directly to model inference pipelines without extra conversion.

# Example: direct handoff to inference-friendly float32
import numpy as np
import macloop

sources = macloop.list_audio_sources()
display = next(s for s in sources if s["type"] == "display")
cfg = macloop.AudioProcessingConfig(sample_format="f32", sample_rate=16000, channels=1)

with macloop.Capture(display_id=display["display_id"], config=cfg) as stream:
    for chunk in stream:
        x = chunk.samples.astype(np.float32, copy=False)
        # model(x)

Sherpa ASR Demo

For speech-to-text (ASR), use this short example:

uv run --with sherpa-onnx --with huggingface_hub --with macloop \
  python examples/sherpa_asr_demo.py --seconds 5

It captures microphone audio with macloop, downloads a Sherpa model from Hugging Face (or uses --model-dir), and prints transcript text.

Notes

• One capture target per stream: either display_id or pid.
• For app capture use pid=....

License

MIT