Feature Extraction
Transformers
Safetensors
English
voiceclap-small
audio
speech
emotion
clap
contrastive
voice
custom_code
Instructions to use VoiceNet/voiceclap-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VoiceNet/voiceclap-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="VoiceNet/voiceclap-small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VoiceNet/voiceclap-small", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 645 Bytes
f5fcbcd | 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 | {
"architectures": [
"VoiceCLAPSmall"
],
"dtype": "float32",
"embed_dim": 768,
"model_type": "voiceclap-small",
"n_ctx": 1500,
"n_head": 12,
"n_layer": 12,
"n_mels": 80,
"n_state": 768,
"text_hidden_dim": 384,
"text_intermediate_size": 1536,
"text_layer_norm_eps": 1e-12,
"text_max_position_embeddings": 512,
"text_num_heads": 12,
"text_num_layers": 6,
"text_pad_token_id": 0,
"text_proj_hidden": 576,
"text_vocab_size": 30522,
"transformers_version": "5.7.0",
"auto_map": {
"AutoConfig": "configuration_voiceclap.VoiceCLAPSmallConfig",
"AutoModel": "modeling_voiceclap.VoiceCLAPSmall"
}
} |