Any-to-Any
Transformers
Safetensors
multilingual
spev
feature-extraction
multimodal
text
audio
image
video
decision-model
choice
qwen3-omni
lora
custom-code
custom_code
Instructions to use SpragAI/SPEV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpragAI/SPEV with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SpragAI/SPEV", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download examples/turn_policy.json from SpragAI/SPEV: direct link, hf CLI and curl.
- Browser
- Download file 730 Bytes
-
https://huggingface.co/SpragAI/SPEV/resolve/main/examples/turn_policy.json
- Command line
-
hf download hf://SpragAI/SPEV/examples/turn_policy.json
-
curl -L -o turn_policy.json https://huggingface.co/SpragAI/SPEV/resolve/main/examples/turn_policy.json
730 Bytes
| { | |
| "questions": { | |
| "decision": { | |
| "instructions": "At the end of this conversation audio, has the most recent speaker yielded the floor or are they continuing their turn?", | |
| "criteria": { | |
| "keep_listening": "The most recent speaker is continuing their turn; keep listening.", | |
| "respond": "The most recent speaker has yielded the floor; another speaker can respond." | |
| } | |
| } | |
| }, | |
| "temperature": 1.3234945284875692, | |
| "response_threshold": 0.3856703463365996, | |
| "comparison": "strictly greater than", | |
| "scope": "Fixed English turn-taking schema; not a generic Choice threshold", | |
| "temperature_fitted_on": "AMI designated calibration split", | |
| "threshold_selected_on": "AMI development split" | |
| } | |