Instructions to use hf-internal-testing/tiny-random-UniSpeechForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UniSpeechForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-UniSpeechForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-UniSpeechForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-UniSpeechForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df6630fab8a51fc1642326d518e0398c6666d48fe29922f47bd8eb7a7db810c0
- Size of remote file:
- 153 kB
- SHA256:
- 3afe4e2ef0f6db7561728085e0033d7921185cf481dd22ebb64d47ae55dfe59e
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