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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 384 Bytes
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"center": true,
"f_max": null,
"f_min": 0,
"feature_extractor_type": "CedFeatureExtractor",
"auto_map": {
"AutoFeatureExtractor": "feature_extraction_ced.CedFeatureExtractor"
},
"feature_size": 64,
"hop_size": 160,
"n_fft": 512,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 16000,
"win_size": 512
}
|