Instructions to use mispeech/ced-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/ced-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mispeech/ced-mini", trust_remote_code=True)# Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("mispeech/ced-mini", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2acb1295a52636a7f68a538e4392568c42f41fd99b2a9c329cef8ff095d97c9e
- Size of remote file:
- 10.9 MB
- SHA256:
- 2a86374dbe1fa03e96b0acf6a02618ac71852d12aa3d72a030b062ff1e5c5434
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