Audio Classification
Transformers
Joblib
Safetensors
English
pronunciation
audio-quality
whisper
speech
Instructions to use jecallora/readai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jecallora/readai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="jecallora/readai")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jecallora/readai", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a06a4aa78425e4fb5e9848e10aece1be59d13a796650356d47fce70d9a2d1ab
- Size of remote file:
- 471 Bytes
- SHA256:
- 0ee6a57b0538e791d27c0fa268932e1f00ab23ec48d4de11a38e1b1f595d65a7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.