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
README: list all 9 training datasets (expresso/vox1/vox2 were missing)
Browse files
README.md
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## Training data
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Trained for **1 epoch** on the open `
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VoiceNet paper:
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- `emolia-balanced-5M-subset` (annotated subset of [Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset))
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- `laions_got_talent_clean_with_captions`
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- `synthetic_vocal_bursts`
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- `improved_synthetic_vocal_bursts`
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- `ears`
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All clips are captioned with `MOSS-Audio-8B-Thinking`-derived dense
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vocal-style captions covering emotions, talking-style attributes, and
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## Training data
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Trained for **1 epoch** on the open `voiceclap_10_safe` mixture (9 datasets)
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used in the VoiceNet paper:
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- `emolia-balanced-5M-subset` (annotated subset of [Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset))
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- `laions_got_talent_clean_with_captions`
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- `synthetic_vocal_bursts`
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- `improved_synthetic_vocal_bursts`
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- `ears`
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- `expresso`
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- `voxceleb1`
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- `voxceleb2`
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All clips are captioned with `MOSS-Audio-8B-Thinking`-derived dense
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vocal-style captions covering emotions, talking-style attributes, and
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