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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc0d0e10f61bbd6275bfc7204c6b176490f81d948fcbae0f4b3b8354610b270d
- Size of remote file:
- 221 kB
- SHA256:
- c468cfb7b40fbd958901096d6d4c581b7ca2f97aa6f990d0c6edec221aa6c6ed
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