Instructions to use IAMCB/laya-voice-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IAMCB/laya-voice-router with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IAMCB/laya-voice-router", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Laya voice router
convaiinnovations/laya (multilingual, mmBERT-base) fine-tuned to decide, from a caller's partial speech-to-text and the
conversation so far, which read-only lookups a voice agent is about to need and with which closed-set arguments.
English, Hinglish, Hindi. Load with laya.load("this-repo").
Training mix: template-generated voice-agent turns over six business domains (complete, two-in-one, mid-sentence, split-across-turns, small talk; ASR-style noise), MASSIVE intents (Hindi, English, six Indic languages), LocalLLaMA/typed-decisions, no real call data yet.
| held-out domain, per question | held-out domain, all questions right | 7 production routing cases | latency (median) | |
|---|---|---|---|---|
| base | 0.636 | 0.107 | 3/7 | 32.9 ms |
| fine-tuned | 0.962 | 0.817 | 7/7 | 33.9 ms |
The held-out domain shares its sentence templates' style with the training domains, so treat that column as an upper bound; performance on real callers is only established by shadow-testing against live traffic.
Model tree for IAMCB/laya-voice-router
Base model
convaiinnovations/laya