Feature Extraction
Transformers
Safetensors
English
speech_jepa
speech
audio
self-supervised-learning
jepa
speech-jepa
custom_code
Instructions to use labhamlet/speech-jepa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use labhamlet/speech-jepa-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="labhamlet/speech-jepa-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("labhamlet/speech-jepa-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,336 Bytes
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"activation_dropout": 0.1,
"apply_spec_augment": true,
"architectures": [
"SpeechJEPAModel"
],
"attention_dropout": 0.1,
"auto_map": {
"AutoConfig": "configuration_speech_jepa.SpeechJEPAConfig",
"AutoModel": "modeling_speech_jepa.SpeechJEPAModel",
"AutoModelForCTC": "modeling_speech_jepa.SpeechJEPAForCTC"
},
"conv_bias": false,
"conv_dim": [
512,
512,
512,
512,
512,
512,
512
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
2,
2,
2
],
"ctc_loss_reduction": "mean",
"ctc_zero_infinity": true,
"dtype": "float32",
"feat_extract_norm": "group",
"final_dropout": 0.1,
"hidden_act": "gelu",
"hidden_dropout": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-06,
"layerdrop": 0.05,
"mask_feature_length": 64,
"mask_feature_min_masks": 0,
"mask_feature_prob": 0.0,
"mask_time_length": 10,
"mask_time_min_masks": 2,
"mask_time_prob": 0.065,
"max_position_embeddings": 8192,
"model_type": "speech_jepa",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"rope_theta": 10000.0,
"transformers_version": "5.2.0",
"use_weighted_layer_sum": false,
"vocab_size": 29
}
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