Text Classification
Transformers
ONNX
Safetensors
llama
text-generation
voice-ai
turn-detection
end-of-utterance
end-of-turn
conversational-ai
livekit
quantized
knowledge-distillation
text-embeddings-inference
Instructions to use livekit/turn-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use livekit/turn-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="livekit/turn-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("livekit/turn-detector") model = AutoModelForCausalLM.from_pretrained("livekit/turn-detector", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 793d4f5a57cbb97bdbb2b7fb52d58c1f01bf556a71745f0e6e53912518d7c267
- Size of remote file:
- 538 MB
- SHA256:
- 2f7b4c93c1cdb6d1e858b01f63e5f0f15bdb979dd0d177e8996a244c80e03925
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