Audio Classification
Transformers
Safetensors
smad_crnn
feature-extraction
audio
music
speech
custom-code
custom_code
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
File size: 777 Bytes
64ef8a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"architectures": [
"SmadForAudioClassification"
],
"auto_map": {
"AutoConfig": "configuration_smad.SmadConfig",
"AutoModel": "modeling_smad.SmadForAudioClassification",
"AutoModelForAudioClassification": "modeling_smad.SmadForAudioClassification"
},
"channels": [
32,
64,
128,
128
],
"dropout": 0.2,
"id2label": {
"0": "speech_noise",
"1": "speech_music",
"2": "singing_music",
"3": "none"
},
"label2id": {
"speech_noise": 0,
"speech_music": 1,
"singing_music": 2,
"none": 3
},
"model_type": "smad_crnn",
"n_fft": 400,
"num_labels": 4,
"num_mels": 80,
"rnn_hidden": 128,
"rnn_type": "gru",
"sample_rate": 16000,
"segment_seconds": 4.0,
"temperature": 0.709507268312107
}
|