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)# 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 ·
3bf8ef6
1
Parent(s): d4f633f
Upload feature extractor
Browse files- preprocessor_config.json +14 -0
preprocessor_config.json
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{
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"center": true,
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"f_max": null,
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"f_min": 0,
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"feature_extractor_type": "CedFeatureExtractor",
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"feature_size": 64,
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"hop_size": 160,
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"n_fft": 512,
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"padding_side": "right",
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"padding_value": 0.0,
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"return_attention_mask": true,
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"sampling_rate": 16000,
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"win_size": 512
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}
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