Audio Classification
Transformers
Safetensors
English
audio-spectrogram-transformer
audio
ast
music
mood
Instructions to use guyPerry/audio-mood-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guyPerry/audio-mood-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="guyPerry/audio-mood-classifier")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("guyPerry/audio-mood-classifier") model = AutoModelForAudioClassification.from_pretrained("guyPerry/audio-mood-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 868 Bytes
7ae5115 5331fdb 7ae5115 5331fdb 7ae5115 5331fdb | 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 | {
"architectures": [
"ASTForAudioClassification"
],
"attention_probs_dropout_prob": 0.0,
"dtype": "float32",
"frequency_stride": 10,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "calm_melancholic",
"1": "energetic_upbeat",
"2": "moderate_neutral"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"calm_melancholic": 0,
"energetic_upbeat": 1,
"moderate_neutral": 2
},
"layer_norm_eps": 1e-12,
"max_length": 1024,
"model_type": "audio-spectrogram-transformer",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"num_mel_bins": 128,
"patch_size": 16,
"problem_type": "single_label_classification",
"qkv_bias": true,
"time_stride": 10,
"transformers_version": "5.13.1",
"use_cache": false
} |