Feature Extraction
sentence-transformers
Safetensors
English
qwen2_5_omni_thinker
audio
speech
emotion
clap
contrastive
voice
Instructions to use VoiceNet/voiceclap-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use VoiceNet/voiceclap-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("VoiceNet/voiceclap-large") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 1,146 Bytes
f6fc423 | 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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | {
"transformer_task": "any-to-any",
"modality_config": {
"text": {
"method": "forward",
"method_output_name": [
"hidden_states",
-1
]
},
"image": {
"method": "forward",
"method_output_name": [
"hidden_states",
-1
]
},
"audio": {
"method": "forward",
"method_output_name": [
"hidden_states",
-1
]
},
"video": {
"method": "forward",
"method_output_name": [
"hidden_states",
-1
]
},
"message": {
"method": "forward",
"method_output_name": [
"hidden_states",
-1
],
"format": "structured"
}
},
"module_output_name": "token_embeddings",
"processing_kwargs": {
"chat_template": {
"chat_template": "sentence_transformers",
"add_generation_prompt": true
}
}
} |