A unified speech-language model that synchronizes speech and text into a single, cohesive stream via 1:1 alignment. TADA is a unified speech-language model that synchronizes speech and text into a single, cohesive stream via 1:1 alignment. By leveraging a novel tokenizer and architectural design, TADA achieves high-fidelity synthesis and generation with a fraction of the computational overhead required by traditional models. ⭐️ arxiv: https://arxiv.org/abs/2602.23068 \ ⭐️ demo1: https://huggingface.co/spaces/fffiloni/tada-dual-alignment-tts-demo \ ⭐️ demo2: https://huggingface.co/spaces/HumeAI/tada \ ⭐️ github: https://github.com/HumeAI/tada \ ⭐️ blog post: https://www.hume.ai/blog/opensource-tada \
A unified speech-language model that synchronizes speech and text into a single, cohesive stream via 1:1 alignment. TADA is a unified speech-language model that synchronizes speech and text into a single, cohesive stream via 1:1 alignment. By leveraging a novel tokenizer and architectural design, TADA achieves high-fidelity synthesis and generation with a fraction of the computational overhead required by traditional models. ⭐️ arxiv: https://arxiv.org/abs/2602.23068 \ ⭐️ demo1: https://huggingface.co/spaces/fffiloni/tada-dual-alignment-tts-demo \ ⭐️ demo2: https://huggingface.co/spaces/HumeAI/tada \ ⭐️ github: https://github.com/HumeAI/tada \ ⭐️ blog post: https://www.hume.ai/blog/opensource-tada
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
🖥️ Official Website | 🕖 HunyuanAPI | 🕹️ Demo Hunyuan is Tencent's open-source efficient large language model series, designed for versatile deployment across diverse computational environments. From edge devices to high-concurrency production systems, these models deliver optimal performance with advanced quantization support and ultra-long context capabilities. We have released a series of Hunyuan dense models, comprising both pre-trained and instruction-tuned variants, with parameter scales of 0.5B, 1.8B, 4B, and 7B. These models adopt training strategies similar to the Hunyuan-A13B, thereby inheriting its robust performance characteristics. This comprehensive model family enables flexible deployment optimization - from resource-constrained edge computing with smaller variants to high-throughput production environments with larger models, all while maintaining strong capabilities across diverse scenarios.
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