Audio Classification
Transformers
Safetensors
audio-spectrogram-transformer
Generated from Trainer
Eval Results (legacy)
Instructions to use Vladimirlv/ast-finetuned-audioset-10-10-0.4593-heart-sounds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vladimirlv/ast-finetuned-audioset-10-10-0.4593-heart-sounds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Vladimirlv/ast-finetuned-audioset-10-10-0.4593-heart-sounds")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("Vladimirlv/ast-finetuned-audioset-10-10-0.4593-heart-sounds") model = AutoModelForAudioClassification.from_pretrained("Vladimirlv/ast-finetuned-audioset-10-10-0.4593-heart-sounds") - Notebooks
- Google Colab
- Kaggle
File size: 833 Bytes
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"_name_or_path": "MIT/ast-finetuned-audioset-10-10-0.4593",
"architectures": [
"ASTForAudioClassification"
],
"attention_probs_dropout_prob": 0.0,
"frequency_stride": 10,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "artifact",
"1": "healthy",
"2": "unhealthy"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"artifact": 0,
"healthy": 1,
"unhealthy": 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,
"torch_dtype": "float32",
"transformers_version": "4.49.0"
}
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