Instructions to use mispeech/ced-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/ced-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mispeech/ced-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("mispeech/ced-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update configuration_ced.py
Verified signaturedb3e14a
-
Update README.md
Verified signature52200e0 -
Uploaded model.onnx ( 8 bit int )
Verified signature2abb379
-
Update modeling_ced.py
Verified signature19505af
-
Update config.json
Verified signature76114a7 -
Update README.md
Verified signature5f8ed5a -
Update preprocessor_config.json
Verified signaturebcc5173 -
Update config.json
Verified signaturee406431 -
Upload 3 files
Verified signaturefb96339
-
Update README.md
Verified signature52527c2
-
Update README.md
Verified signature54b35a4
-
Update README.md
Verified signaturedb12bbd -
Update README.md
Verified signature9cd3efa
-
Update preprocessor_config.json
Verified signature09a7319
-
Update README.md
cf34d65
-
Update README.md
e9c53e4 -
Update README.md
f89d369 -
Remove model codes.
21e84e2
-
Update README.md
73c48c7
-
Upload README.md
6685d58 -
Upload 4 files
68b2ec4 -
Upload feature extractor
3bf8ef6 -
Upload CedForAudioClassification
d4f633f -
initial commit
02d8486