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
Model card: eot-bench operating points
Browse files
README.md
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Score smoothness along a pause matches the tiny model (local std 0.044 over 200 ms, threshold flips 1.7 %
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per 20 ms step; smart-turn-v3 0.124 / 9.8 %).
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## Files
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| file | what |
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Score smoothness along a pause matches the tiny model (local std 0.044 over 200 ms, threshold flips 1.7 %
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per 20 ms step; smart-turn-v3 0.124 / 9.8 %).
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**Under LiveKit's [eot-bench](https://github.com/livekit/eot-bench) harness** (100 ms causal grid over
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every pause ≥ 0.1 s, `threshold × action_delay × timeout` policy sweep; same adapter for all audio models,
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scored 0.2 s into each pause for the scalar metrics):
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| set | model | cutoff @ 300 ms budget | cutoff @ 600 ms | latency @ 5 % cutoff | latency @ 10 % cutoff | AUC |
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|---|---|---:|---:|---:|---:|---:|
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| telephony test, 1 000 turns, English (510 eot / 260 hold spans) | **this model** | **47.3 %** | **25.0 %** | **1 722 ms** | **1 261 ms** | **0.84** |
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| | tiny variant | 50.8 % | 30.0 % | 2 039 ms | 1 529 ms | 0.80 |
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| | smart-turn-v3 | 69.6 % | 35.4 % | 2 269 ms | 1 756 ms | 0.66 |
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| | VAD baseline | 77.3 % | 41.9 % | 2 020 ms | 1 610 ms | – |
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| telephony test, 1 000 turns, Malay (485 / 169) | **this model** | **49.1 %** | **25.4 %** | **1 903 ms** | 1 494 ms | **0.86** |
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| | tiny variant | 55.0 % | 32.5 % | 2 019 ms | **1 423 ms** | 0.81 |
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| | smart-turn-v3 | 78.1 % | 39.1 % | 2 635 ms | 2 116 ms | 0.63 |
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| | VAD baseline | 79.3 % | 46.2 % | 2 540 ms | 2 060 ms | – |
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| telephony, the 300 benchmark turns, English (188 / 105) | **this model** | **45.7 %** | **21.9 %** | **1 530 ms** | **1 151 ms** | **0.83** |
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| | tiny variant | 58.1 % | 27.6 % | 1 636 ms | 1 198 ms | 0.77 |
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| | smart-turn-v3 | 74.3 % | 30.5 % | 1 649 ms | 1 164 ms | 0.64 |
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| | VAD baseline | 77.1 % | 31.4 % | 1 800 ms | 1 510 ms | – |
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| telephony, the 300 benchmark turns, Malay (112 / 35) | **this model** | **48.6 %** | **25.7 %** | **1 761 ms** | **1 238 ms** | **0.85** |
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| | tiny variant | 57.1 % | 31.4 % | 1 843 ms | 1 482 ms | 0.78 |
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| | smart-turn-v3 | 68.6 % | 34.3 % | 2 357 ms | 1 603 ms | 0.63 |
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| | VAD baseline | 80.0 % | 34.3 % | 2 410 ms | 1 830 ms | – |
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Where the tiny model only ties the VAD timer on latency at a 5 % cutoff budget, this one is ahead of it on
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every operating point of every subset, and ahead of the tiny model everywhere except latency at 10 % on the
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Malay 1 000-turn set. The harness asks within the first 100–300 ms of every pause, before an audio model has
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silence evidence; the extra encoder capacity buys the most exactly there (AUC 0.85 vs 0.80 at the pause
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start). In the LiveKit pipeline, which asks after the VAD's 0.4–0.55 s of silence, the two are one cut-off
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turn apart.
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## Files
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| file | what |
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