Skip to main content
ai-coustics provides different speech enhancement model families for real-time SDK use cases. Use this guide to choose a model that fits your needs.

​
Overview

Speech Enhancement for Voice AI

Best for: Improving Voice AI Agents and Speech-to-Text (STT) accuracy. Includes Quail Voice Focus.
  • Model Family: Quail
  • Platform: SDK
  • Real-time

Perceptual Speech Enhancement

Best for: Removing background noise and reverb on real-time communication use-cases.
  • Model Family: Rook
  • Platform: SDK
  • Real-time

​
Quail

SDK, Real-time, Human-to-Machine The Quail models are purpose-built for Voice AI Agents and human-to-machine interactions. Unlike standard noise suppression, Quail is tuned to improve the performance of downstream Speech-to-Text (STT) engines. Quail Voice Focus is optimized for near-field voice interactions. It prioritizes speech that sounds close to the microphone and suppresses speech that sounds distant, along with background noise. This makes it ideal for single-user, close-talk use cases (e.g., headsets or handheld devices). Quail, in contrast, is designed for far-field and multi-speaker environments. It does not suppress distant-sounding speech, making it better suited for speakerphone setups, meeting rooms, or situations with multiple participants spread across a space.
The Quail models are designed to enhance the performance of Voice AI Agents and STT systems, and may not always produce the most natural-sounding audio for human listeners.It is expected that some noise and reverberation may remain in the output, as these can actually help improve STT accuracy by providing additional acoustic context.If your primary goal is to improve the listening experience for humans, we recommend using the Rook models instead.
Take a look at our ASR optimization guide
  • ID: quail-vf-2.0-l-16khz
  • File size: 35 MB
  • Window length: 10 ms
  • Optimal sample rate: 16 kHz
  • Optimal num frames: 160
  • Minimal algorithmic delay: 30 ms
  • ID: quail-l-16khz
  • File size: 35 MB
  • Window length: 10 ms
  • Optimal sample rate: 16 kHz
  • Optimal num frames: 160
  • Minimal algorithmic delay: 30 ms
  • ID: quail-l-8khz
  • File size: 33.4 MB
  • Window length: 10 ms
  • Native sample rate: 8 kHz
  • Native num frames: 80
  • Minimal algorithmic delay: 30 ms
  • ID: quail-s-16khz
  • File size: 8.88 MB
  • Window length: 10 ms
  • Native sample rate: 16 kHz
  • Native num frames: 160
  • Minimal algorithmic delay: 30 ms
  • ID: quail-s-8khz
  • File size: 8.43 MB
  • Window length: 10 ms
  • Native sample rate: 8 kHz
  • Native num frames: 80
  • Minimal algorithmic delay: 30 ms
Model files available for download here.
Learn more about the models’ sample rate.

​
Rook

SDK, Real-time, Human-to-Human The Rook models are specifically optimized for human-to-human interaction in real-time constrained systems (e.g. voice calls). They reduce background noise and reverberation while preserving speech naturalness and intelligibility for human perception.
  • ID: rook-l-48khz
  • File size: 35.1 MB
  • Window length: 10 ms
  • Native sample rate: 48 kHz
  • Native num frames: 480
  • Minimal algorithmic delay: 30 ms
  • ID: rook-l-16khz
  • File size: 35 MB
  • Window length: 10 ms
  • Native sample rate: 16 kHz
  • Native num frames: 160
  • Minimal algorithmic delay: 30 ms
  • ID: rook-l-8khz
  • File size: 33.4 MB
  • Window length: 10 ms
  • Native sample rate: 8 kHz
  • Native num frames: 80
  • Minimal algorithmic delay: 30 ms
  • ID: rook-s-48khz
  • File size: 8.96 MB
  • Window length: 10 ms
  • Native sample rate: 48 kHz
  • Native num frames: 480
  • Minimal algorithmic delay: 30 ms
  • ID: rook-s-16khz
  • File size: 8.88 MB
  • Window length: 10 ms
  • Native sample rate: 16 kHz
  • Native num frames: 160
  • Minimal algorithmic delay: 30 ms
  • ID: rook-s-8khz
  • File size: 8.43 MB
  • Window length: 10 ms
  • Native sample rate: 8 kHz
  • Native num frames: 80
  • Minimal algorithmic delay: 30 ms
Model files available for download here.
Learn more about the models’ sample rate.

​
Using models with non-native sample rates

Our models are trained for specific sample rates (8 kHz, 16 kHz, and 48 kHz). However, the ai-coustics SDK allows you to use any model with audio at non-native sample rates by internally resampling the input. The model always processes the audio at its native sample rate. You can choose any sample rate between 8 kHz and 192 kHz when calling the processor’s initialize function in the SDK, regardless of the model being used. Higher-than-native sample rate (e.g. 48 kHz audio with a 16 kHz model): In this case, the SDK cuts away the frequency content above the model’s native Nyquist frequency (everything above half the sample rate) before feeding it to the model. The SDK output is then upsampled back to the original sample rate. The mixback (enhancement_level) stays at the higher sample rate, so the output will contain the full frequency range of the original audio, but the model’s enhancement will only be applied to the frequencies within the model’s native Nyquist frequency. Lower-than-native sample rate (e.g. 8 kHz audio with a 16 kHz model): When the input audio sample rate is lower than the model’s native sample rate, compute resources are effectively “wasted” processing higher frequencies where no signal is contained (the model is just processing zeros). Therefore, if there is a model available matching your audio’s sample rate, we recommend using that model to avoid unnecessary compute and ensure optimal performance. In both cases, delay and CPU consumption are not affected by the input sample rate.
Learn more about performance here.