Models Overview - Sanas Developer Hub

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Explore Sanas’s Human ↔ Human and Human ↔ Machine models and their capabilities. Sanas delivers world-class AI models across two categories: Human ↔ Human and Human ↔ Machine. Sanas currently offers Noise Cancellation and Speech Enhancement capabilities. Accent Translation, Language Translation, and Speech Intelligence coming soon.

Agentic Speech Enhancement Models

Model Use Case Description Best For:
AGENTIC_VI_G_SE Agentic — Voice Isolation (General) — Human ↔ Machine Removes all background noise and non-primary voices for complete speaker isolation Voice agents, IVR, and phone bots that need a single, noise-free speaker isolated from everything else on the line
AGENTIC_VI_GT_SE Agentic — Voice Isolation (Telephony) — Human ↔ Machine Telephony-optimized variant of VI_G for 8kHz narrowband audio Telephony-based voice agents and IVR in contact centers running on narrowband (8kHz) call audio
AGENTIC_ST_SE Agentic — Standard — Human ↔ Machine Removes background noise while keeping all human speech audible Multi-party calls or meeting/triage agents where overheard background speech still adds useful context
VI_G_SE Voice Isolation (General) — Human ↔ Human Isolates intended speech by removing background noise and voices Contact center agents, conferencing, and live/gaming voice chat where listeners need clean, isolated speech

Speech Enhancement Models

Model Use Case Description Best For:
SE2.1 SE Standard — Human ↔ Human Restores and enhances voice quality for telephony audio, outputting 8kHz High-volume contact centers and IVR systems needing lightweight, low-latency voice cleanup on telephony audio
SE2.2 SE Ultra — Human ↔ Human Full-fidelity speech enhancement with bandwidth extension to 24kHz Premium contact centers, conferencing, and telemedicine where full-fidelity audio quality is essential
VI_G_SE Voice Isolation (General) — Human ↔ Human Isolates intended speech by removing background noise and voices Contact center agents, conferencing, and live/gaming voice chat where listeners need clean, isolated speech

Accent Translation Models

Model Use Case Description Best For:
AT5.2 Accent Translation — Human ↔ Human Accent Translation modifies global accents in real-time, allowing your teams to be instantly understood while preserving what makes every voice unique. Contact centers and global teams who need instant, natural-sounding accent translation in meetings and daily calls

Language Translation

API Use Case Description Best For:
LT Language Translation— Human ↔ Human Real-time language translation that preserves your speakers’ voices, tone, and intent. Cross-language customer support and international meetings that need real-time translation while preserving the speaker’s voice and tone

Hear samples and learn more about each model’s specifications, use cases, and code examples below.

Human ↔ Human

Speech Enhancement · Standard

SE2.1 — Restores and enhances voice quality for telephony audio. Low CPU footprint.

Speech Enhancement · with full-fidelity

SE2.2 — Full-fidelity speech enhancement with bandwidth extension to ultra-fidelity 24kHz.

Speech Enhancement · Voice Isolation (General)

VI_G_SE — Isolates intended speech by removing background noise and voices. Optimized for human listeners.

Accent Translation

AT5.2 — Accent Translation modifies global accents in real-time, allowing your teams to be instantly understood while preserving what makes every voice unique.

Language Translation

LT — Real-time language translation that preserves your speakers’ voices, tone, and intent.

Human ↔ Machine

Agentic Speech Enhancement · Voice Isolation (General)

AGENTIC_VI_G_SE — Removes background noise and distant voices for complete voice isolation of the primary speaker’s audio stream.

Agentic Speech Enhancement · Voice Isolation (Telephony)

AGENTIC_VI_GT_SE— Telephony-optimized variant of Voice Isolation for 8kHz narrowband audio.

Agentic Speech Enhancement · Standard

AGENTIC_ST_SE — Removes background noise while preserving all human speech for multi-speaker environments.