Instructions to use godzilla1215/dsn2026-lid-probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use godzilla1215/dsn2026-lid-probe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="godzilla1215/dsn2026-lid-probe")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("godzilla1215/dsn2026-lid-probe") model = AutoModelForAudioClassification.from_pretrained("godzilla1215/dsn2026-lid-probe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Runner diagnostic (team godzilla, DSN 2026)
A tiny randomly initialised HubertForSequenceClassification (36k parameters, 1 layer). It is not a real
model: it only checks whether the evaluation runner processes clips at all. It runs 6,659 clips in about
3 minutes on a single CPU thread. No training data was used.
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