Instructions to use Felguk/Felguk-suno-or-people with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Felguk/Felguk-suno-or-people with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Felguk/Felguk-suno-or-people")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Felguk/Felguk-suno-or-people") model = AutoModelForAudioClassification.from_pretrained("Felguk/Felguk-suno-or-people", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create config.json
Browse files- config.json +67 -0
config.json
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{
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"_name_or_path": "Felguk/Felguk-suno-or-people",
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"architectures": [
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"Wav2Vec2ForSequenceClassification"
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],
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"model_type": "wav2vec2",
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"num_labels": 2,
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"label2id": {
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"suno": 0,
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"people": 1
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},
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"id2label": {
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"0": "suno",
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"1": "people"
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},
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"hidden_size": 768,
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"num_hidden_layers": 12,
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"num_attention_heads": 12,
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"intermediate_size": 3072,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"attention_probs_dropout_prob": 0.1,
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"initializer_range": 0.02,
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"layer_norm_eps": 1e-5,
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"feat_extract_norm": "group",
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"feat_extract_activation": "gelu",
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2,
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2
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_bias": false,
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"num_conv_pos_embeddings": 128,
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"num_conv_pos_embedding_groups": 16,
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"do_stable_layer_norm": false,
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"apply_spec_augment": true,
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"mask_time_prob": 0.05,
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"mask_time_length": 10,
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"mask_feature_prob": 0.0,
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"mask_feature_length": 10,
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"classifier_proj_size": 256,
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"torch_dtype": "float32",
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"transformers_version": "4.36.0"
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}
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