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ai-coustics enhances audio in your application. Choose a language to install the SDK and process a recording. The Python and Node.js examples write an enhanced WAV for comparison. Create an SDK key on the developer platform and follow your language’s AIC_SDK_LICENSE setup. Check runtime and platform support in the compatibility matrix. Use the shared quickstart recording to check the same input across languages, then substitute representative audio from your application. Start with Quail Voice Focus for primary-speaker isolation before speech-to-text (STT). For human listening, use Rook Multi Speaker. The model reference explains model loading and selection.
SDK key authorization and usage reporting are distinct concerns. See authentication and telemetry before deploying. Downloading a model in advance does not by itself enable offline operation.

Installation

Use Python 3.11 for this example on a supported platform. The SDK package declares Python 3.10 or later. This example pins aic-sdk 3.2.0 and NumPy 2.2.6. Create a project directory and a virtual environment:

Quickstart

Prepare the key and input

Generate an SDK key on the developer platform. Set it in the terminal where you will run the example, replacing YOUR_SDK_KEY with your key. Keep it out of source control and shared terminal logs.
Download the noisy speech fixture and save it as input.wav in aic-python-quickstart. It contains 56,080 mono PCM16 samples at 16 kHz (3.505 seconds). The fixture guide includes attribution, checksums and the aligned clean reference.For your own recording, export a short sentence with background noise as mono, 16 kHz, signed 16-bit PCM WAV. Renaming a file does not convert it.The example downloads quail-vf-2.2-l-16khz into ./models. Quail Voice Focus isolates the primary speaker for speech-to-text (STT) input. The first run needs network access for the model download and SDK key authorization. See authentication for deployment options.

Save and run

Save this complete program as quickstart.py in the same directory:
quickstart.py
Run it from aic-python-quickstart, with the virtual environment active:

Check the result

The program prints the native SDK version, resolved model ID and processed sample count. It writes enhanced.wav beside input.wav at 16 kHz with the same sample count, compensating for processing delay.With the supplied fixture, the processed sample count is 56080. Verify the file headers:
Listen to both recordings and compare them with the same STT settings to evaluate quality.

Recover from an error

Adapt this example

Use one processor per independent stream and retain it between consecutive blocks. Reset its context when seeking or starting unrelated audio. Downmix stereo input or give each channel its own processor. See audio format, streams and state and the Python binding guide for async processing and lifecycle details.This example reads the full file into memory. For live audio or long files, process bounded blocks and keep file I/O, model downloads and session termination outside the audio callback. For human listening, evaluate Rook Multi Speaker.

Find out more

After checking your output, evaluate representative audio, then prepare your deployment. Use troubleshooting to diagnose setup and runtime failures.

Audio format

Connect your input format, channels and frame sizes to the SDK.

Streams and state

Manage processor state across streams and interruptions.

Language bindings

Find binding-specific APIs, examples and lifecycle guidance.

Performance

Evaluate runtime cost and latency for your deployment.