Audio Classification
Transformers
ONNX
Safetensors
Malay
English
end-of-turn-detection
turn-detection
semantic-vad
endpointing
voice-agent
livekit
whisper
telephony
Instructions to use Scicom-intl/semantic-vad-eot-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scicom-intl/semantic-vad-eot-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Scicom-intl/semantic-vad-eot-whisper-base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Scicom-intl/semantic-vad-eot-whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43c8d96238fddbe4c266d2714d5961661a858be5bc00d44e9c3811845d01ddfb
- Size of remote file:
- 80.7 MB
- SHA256:
- ae327622a3d579a27aa2c51780c9349261920e12018b1642825ece1185876c7a
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