Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51b22c556b7f28ac177992b5d7b12e1df7f8b09ff02b8c6ec9bd23944abd1f22
- Size of remote file:
- 185 kB
- SHA256:
- f0717ffecd000215d1417b7ad88d47be3603eed886c9b9df010540b034d98924
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