Instructions to use mispeech/ced-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/ced-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mispeech/ced-tiny", trust_remote_code=True)# Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("mispeech/ced-tiny", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfc728bc9459af977aa9c1b02a353c27bdad18ad6d6b5fd8a83a097147751599
- Size of remote file:
- 22 MB
- SHA256:
- 0e086f0cd62814c6def89001f3f25193f75955696f6975ef6800af31d00d6dd7
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