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
| { | |
| "window": { | |
| "window_seconds": 8.0, | |
| "do_normalize": false, | |
| "num_mel_bins": 80, | |
| "sample_rate": 16000, | |
| "pooling": "last5" | |
| }, | |
| "params": 20160001, | |
| "fp32": { | |
| "bytes": 80718310, | |
| "max_abs_dp_vs_torch": 1.3113021850585938e-06, | |
| "latency": { | |
| "p50_ms": 163.6397559195757, | |
| "p90_ms": 165.72624072432518, | |
| "min_ms": 146.37318346649408 | |
| } | |
| }, | |
| "int8": { | |
| "bytes": 23776856, | |
| "max_abs_dp_vs_torch": 0.14714109897613525, | |
| "mean_abs_dp_vs_torch": 0.047445375472307205, | |
| "latency": { | |
| "p50_ms": 68.82935343310237, | |
| "p90_ms": 69.49121318757534, | |
| "min_ms": 57.50598106533289 | |
| } | |
| } | |
| } |