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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("duclvQ/smad", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from duclvQ/smad: direct link, hf CLI and curl.
- Browser
- Download file 286 Bytes
-
https://huggingface.co/duclvQ/smad/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://duclvQ/smad/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/duclvQ/smad/resolve/main/preprocessor_config.json
286 Bytes
| { | |
| "auto_map": { | |
| "AutoFeatureExtractor": "feature_extraction_smad.SmadFeatureExtractor" | |
| }, | |
| "feature_extractor_type": "SmadFeatureExtractor", | |
| "feature_size": 80, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "padding_value": 0.0, | |
| "sampling_rate": 16000, | |
| "segment_seconds": 4.0 | |
| } | |