Instructions to use hf-internal-testing/tiny-random-Wav2Vec2ConformerForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Wav2Vec2ConformerForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-Wav2Vec2ConformerForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ConformerForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ConformerForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5aa62ac465491405ecb2fbbee12dc1be4fd8e3be9c62c8daad799295aa869cba
- Size of remote file:
- 185 kB
- SHA256:
- d1efeb40c54529657b027a158868a80f3649700df99b5af53035ba76ba5664db
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