TADA is an open-source, LLM-based text-to-speech (TTS) model released by Hume AI in March 2026. As its name, Text-Acoustic Dual Alignment, suggests, it centers around an architecture that synchronizes text tokens and acoustic vectors in a 1:1 ratio. It adopts Meta's Llama 3.2 as its base language model and uses a self-developed Encoder-Aligner to extract acoustic features corresponding to each text token from the input audio. Subsequently, a Flow Matching head, conditioned on the LLM's hidden state, generates a sequence of acoustic vectors, and a decoder restores them into the final waveform. Two models are available: a 1B parameter model (English only) and a 3B parameter model (multilingual, supporting 10 languages), both of which were simultaneously released on Hugging Face and GitHub. The code is freely modifiable and distributable under the MIT license. Existing LLM-based TTS systems suffer from a structural mismatch, generating 12.5 to 75 fixed-frame audio tokens for each text token. This makes content hallucination (skipping or repeatedly inserting words) inevitable during the autoregressive decoding of long audio sequences. TADA solves this problem not through training or post-processing, but through its architecture. Just as a GPS navigation system prevents deviations by mapping each point on the route to the actual road coordinates in a 1:1 ratio, TADA processes exactly one text token and one audio frame at each LLM step, making it physically impossible for words to be omitted or inserted. In tests on over 1,000 LibriTTSR samples, it recorded zero instances of content hallucination and achieved a Real-Time Factor (RTF) of 0.09, which is approximately 5 times faster than comparable LLM-TTS systems and about 11 times faster than real-time playback. With the same 2,048 token context window, while existing systems can handle approximately 70 seconds, TADA can handle approximately 700 seconds (about 11.7 minutes), ensuring that the context does not break down even in long-form narration or extended dialogue synthesis. In the life science research environment, TADA's zero-hallucination feature is particularly valuable. For example, when converting clinical trial patient education materials into 10 languages using the 3B-ML model, quantitative information such as drug names or dosages is spoken accurately without distortion. In audiobook or academic paper narration pipelines, a reference voice can be used to prompt the speaker style (Speaker Similarity 4.18/5.0), and prompt caching (EncoderOutput.save/load) can be used to reuse encoding between chunks, maintaining a naturalness level of 3.78/5.0. Because the model weights and code are fully open-source, sensitive medical and research data can be processed on-premises without being transmitted to external APIs, which is a practical benefit for research institutions.