Audio Insight — ai-coustics
Predict and diagnose downstream failures in Voice AI stacks.
Tyto is an audio insight model by ai-coustics. Tyto predicts whether the audio reaching your agent will cause downstream failures, with a single risk score and the likely cause behind it. Catch degraded audio before it breaks your voice agent — in real time or post-call. Real-time audio intelligence that makes Voice AI work in production. Not just in the lab. Inference runs through the aic-sdk Python package (also available for Rust, C/C++, Node.js, and WebAssembly).
Setup
- Install the SDK:
pip install aic-sdk - Create a free SDK key on the ai-coustics playground — 30-day trial included, no credit card required.
Usage
# initialize the audio insight analyzer
import aic_sdk as aic
model = aic.Model.from_file(aic.Model.download("tyto-1.1-l-16khz", "./models"))
analyzer = aic.FileAnalyzer(model, "{aic-sdk-key}") # get a free SDK key: https://developers.ai-coustics.com/login
# score a mono float32 buffer — one AnalysisResult per 5-second window
results = analyzer.analyze(audio, sample_rate)
# predict downstream failures, with the exact cause behind them
for window in results:
print(f"risk={window.risk_score:.2f} noise={window.noise:.2f} interfering={window.interfering_speech:.2f}")
What you get
Every analysis window returns one headline score plus six diagnostic dimensions (all 0.0–1.0; lower is less problematic, except loudness):
| Field | Meaning |
|---|---|
risk_score |
Headline score — predicted likelihood of failure of downstream STT, VAD, turn-taking, or speech-to-speech models |
noise |
Ambient / environmental noise |
speaker_reverb |
Speaker distance and reverberation |
speaker_loudness |
Speaker loudness |
interfering_speech |
Interference from additional speakers |
packet_loss |
Dropouts / discontinuities (packet loss, frame erasure, jitter, CPU overload) |
codec_degradation |
Artifacts from lossy or narrowband speech codecs |
Streaming / real-time analysis
For live audio, use a Collector/Analyzer pair: the collector buffers on the audio thread (real-time safe), the analyzer runs on a separate thread (analysis is computationally expensive):
import aic_sdk as aic
model = aic.Model.from_file(aic.Model.download("tyto-1.1-l-16khz", "./models"))
collector, analyzer = aic.analyzer_pair(model, "{aic-sdk-key}") # get a free SDK key: https://developers.ai-coustics.com/login
config = aic.ProcessorConfig.optimal(model)
collector.initialize(config)
# audio thread (real-time safe):
for block in audio_blocks(): # np.ndarray, config.block_size samples
collector.buffer(block)
# non-audio thread, whenever you want a reading:
result = analyzer.analyze_buffered()
print(result.risk_score, result.packet_loss, result.codec_degradation)
Model variants
Weights are hosted on the ai-coustics artifact CDN. aic.Model.download() fetches the latest compatible artifact and verifies its checksum automatically; the direct links below are for manual download.
| SDK model ID | Sample rate | Artifact | SHA-256 (prefix) |
|---|---|---|---|
tyto-1.1-l-16khz — latest, recommended |
16 kHz | v7 | d350724ed394 |
tyto-l-16khz (previous generation) |
16 kHz | v6 | 02bb6b7fbce5 |
Full SHA-256 checksums for every artifact are listed next to each download at artifacts.ai-coustics.io.
Related ai-coustics models
- 🔊 Speech Enhancement — real-time, ASR-optimized speech enhancement (Quail & Rook)
- 🎙️ Voice Activity Detection — real-time, noise-robust turn-taking for voice agents
Resources
- 📖 Documentation & model guide: docs.ai-coustics.com
- 🧩 Framework quickstarts: LiveKit, Pipecat
- 📦 Python SDK: pypi.org/project/aic-sdk
- 🔑 Free SDK key: developers.ai-coustics.com
- 🏢 Enterprise licensing: ai-coustics.com