Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ForSequenceClassification 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-Wav2Vec2ForSequenceClassification 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-Wav2Vec2ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5b10a9ef03f0ff6d7dabaa2fb816e24a20d48ebac3dac150818593bcb9b5854
- Size of remote file:
- 136 kB
- SHA256:
- 3eb29c1e98cbfa578ff9af974af1841b422da677dd9296efeaf0cf73e6cec57a
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