--- description: Niagara is ABR's family of streaming on-device speech recognition models, 8M to 38M parameters, verified on the HuggingFace Open ASR Leaderboard. --- # Niagara Automatic Speech Recognition Niagara is a family of streaming ASR models optimized for local, real-time transcription. All processing runs on-device, with no cloud dependency. Built on a State Space Model (SSM) architecture with attention, Niagara achieves 150ms average latency from first audio to first token. Models range from 8M to 38M parameters, enabling deployment on resource-constrained edge hardware while maintaining accuracy competitive with much larger models. ## Key Features - **Real-time streaming:** Processing begins as soon as the first audio is received, minimizing latency - **Private:** All voice processing runs locally - **Small footprint, high accuracy:** 4 to 6× smaller than transformers at equivalent accuracy, verified independently on the [HuggingFace Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard) - **Cascaded output:** Fast path (~150ms latency) for real-time display, refined path (~1s) for higher accuracy - **Multi-language:** English, Spanish, Chinese (Mandarin), Japanese, and Korean; additional languages available upon request Premium Features - **Custom vocabulary (coming soon):** Accurate identification of brand names, technical jargon, and domain- or application-specific words can be added without retraining the core ASR model ## Models Select a model based on your resource constraints. Larger models provide higher accuracy; smaller models run on more constrained hardware. All models accept raw 16 kHz mono PCM audio with no file headers and output punctuated, capitalized text.
| 38M | 19M | 9M | 8M | ||
|---|---|---|---|---|---|
| Languages | English | ✓ | ✓ | ✓ | ✓ |
| Spanish | ✓ | — | — | ✓ | |
| Chinese (Mandarin) | ✓ | — | — | ✓ | |
| Japanese | ✓ | — | — | — | |
| Korean | ✓ | — | — | — | |
| Sizes | 32-bit | 147MB | 80MB | 38MB | 32MB |
| 8-bit | 39MB | — | 10MB | 9MB | |