FreyaTTS Technical Report

Researchers introduced Freya-TTS, a compact 183.2M-parameter Turkish text-to-speech model using a tokenizer-free diffusion transformer, achieving an 8.0% word error rate and 0.11 real-time factor on consumer GPUs. The model outperforms larger open-source systems and is released under Apache-2.0 for edge deployment.

arXiv:2607.09530v1 Announce Type: new Abstract: We introduce Freya-TTS, a compact, tokenizer-free, Turkish-first text-to-speech model designed for highly reliable and efficient conversational synthesis. Freya-TTS is a 183.2M-parameter non-autoregressive conditional flow-matching Diffusion Transformer DiT that operates in the frozen continuous latent space of AudioVAE2 16 kHz encode, 48 kHz decode , allowing the model to focus its capacity on text-to-latent mapping while inheriting high-quality 48 kHz reconstruction. We advance the framework along three key dimensions: 1 rule-free end-to-end modeling from a 92-symbol Turkish character vocabulary without a phonemizer, grapheme-to-phoneme frontend, or discrete speech tokenizer; 2 non-autoregressive parallel denoising, which predicts the entire latent sequence simultaneously over a predicted duration; and 3 a production-oriented two-stage post-training recipe consisting of single-speaker voice locking and short-utterance coverage, improving speaker consistency and robustness on short inputs. On the Freya-TR-Eval benchmark, Freya-TTS achieves a band-matched word error rate WER of 8.0% and character error rate CER of 3.0%, outperforming substantially larger open-source systems while using a fraction of their parameters. The model achieves a real-time factor of 0.11 on consumer GPUs and runs faster than real time on a laptop CPU, making it well suited for resource-constrained edge deployment. We release the model weights, training and inference code, and evaluation benchmark under the Apache-2.0 license.