Instructions to use Ericwang/tiny-random-ast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ericwang/tiny-random-ast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Ericwang/tiny-random-ast")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("Ericwang/tiny-random-ast") model = AutoModel.from_pretrained("Ericwang/tiny-random-ast") - Notebooks
- Google Colab
- Kaggle
File size: 539 Bytes
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"architectures": [
"ASTModel"
],
"attention_probs_dropout_prob": 0.0,
"frequency_stride": 10,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 32,
"initializer_range": 0.02,
"intermediate_size": 37,
"layer_norm_eps": 1e-12,
"max_length": 1024,
"model_type": "audio-spectrogram-transformer",
"num_attention_heads": 4,
"num_hidden_layers": 5,
"num_mel_bins": 128,
"patch_size": 1,
"qkv_bias": true,
"time_stride": 10,
"torch_dtype": "float32",
"transformers_version": "4.25.1"
}
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