Audio Classification
Transformers
Safetensors
English
Vietnamese
smad_crnn
feature-extraction
audio
music
speech
singing-voice-detection
speech-music-discrimination
custom-code
custom_code
Eval Results (legacy)
Instructions to use duclvQ/smad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duclvQ/smad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="duclvQ/smad", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("duclvQ/smad", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Remove caveat from model card
Browse files
README.md
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audio, _ = librosa.load("clip.mp3", sr=16000, mono=True)
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```
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## Caveat
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The reported score is an upper bound on real-world performance: train and test
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clips come from the same synthetic mixer, so they share its biases.
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audio, _ = librosa.load("clip.mp3", sr=16000, mono=True)
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```
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