FreyaTTS Technical Report

• URL: http://arxiv.org/abs/2607.09530v1
• Date: Fri, 10 Jul 2026 15:36:30 GMT
• Title: FreyaTTS Technical Report
• Abstract summary: We introduce Freya-TTS, a compact, tokenizer-free, Turkish-first text-to-speaker model.<n>It 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.<n>It 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• TontaubeV1: Streaming Text-to-Speech with Hierarchical Codec Modeling and Bounded Context [0.04583541422554719]
  TontaubeV1 is a model that preserves natural prosody while enabling streaming from a single consumer GPU.<n>Our design assumes that prosodic structure is largely established when the semantic stream is generated.<n>The model accepts up to one minute of reference audio for voice conditioning and is designed primarily for English and German.
  arXiv  Detail & Related papers  (2026-09-08T13:03:46Z)
• CuteTTS: Efficient and High-Quality Speech Synthesis via Autoregressive Modeling of Continuous Latents [53.73961877379327]
  CuteTTS is a compact continuous-autoregressive TTS system.<n>It combines semantically aligned causal VAE latents with patch-level autoregression, explicit speaker conditioning, and a bidirectional flow-matching head.
  arXiv  Detail & Related papers  (2026-08-09T11:10:20Z)
• dots.tts Technical Report [21.076643922579517]
  dots.tts is a continuous autoregressive text-to-speech foundation model.<n>We train an AudioVAE with multiple objectives to build a semantically structured and prediction-friendly continuous speech space.<n>We apply reward-free self-corrective post-training to the flow-matching head to further improve robustness and acoustic quality.
  arXiv  Detail & Related papers  (2026-06-05T09:19:24Z)
• MOSS-TTS Technical Report [62.0595749527453]
  MOSS-TTS is a speech generation foundation model built on a scalable recipe.<n>It compresses 24 kHz audio to 12.5 fps with variable-bitrate RVQ and unified semantic-acoustic representations.<n>It supports zero-shot voice cloning, token-level duration control, phoneme-/pinyin-level pronunciation control, smooth code-switching, and stable long-form generation.
  arXiv  Detail & Related papers  (2026-03-18T09:08:06Z)
• Qwen3-ASR Technical Report [71.87071808763484]
  We introduce Qwen3-ASR family, which includes two powerful all-in-one speech recognition models and a novel non-autoregressive speech forced alignment model.<n>Qwen3-ASR-1.7B and Qwen3-ASR-0.6B are ASR models that support language identification and ASR for 52 languages and dialects.
  arXiv  Detail & Related papers  (2026-01-29T06:58:13Z)
• Qwen3-TTS Technical Report [64.94647392030824]
  We present the Qwen3-TTS series, a family of advanced multilingual, controllable, robust, and streaming text-to-speech models.<n>Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control.<n>Qwen3-TTS adopts a dual-track LM architecture for real-time synthesis, coupled with two speech tokenizers.
  arXiv  Detail & Related papers  (2026-01-22T03:51:43Z)
• Qwen3-Omni Technical Report [105.11829337290249]
  We present Qwen3- Omni, a single multimodal model that maintains state-of-the-art performance across text, image, audio, and video.<n>Qwen3- Omni matches the performance of same-sized single-modal models within the Qwen series and excels particularly on audio tasks.<n>It supports text interaction in 119 languages, speech understanding in 19 languages, and speech generation in 10 languages.
  arXiv  Detail & Related papers  (2025-09-22T13:26:24Z)
• Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis [64.12708207721276]
  We introduce a novel pseudo-autoregressive (PAR) language modeling approach that unifies AR and NAR modeling.<n>Building on PAR, we propose PALLE, a two-stage TTS system that leverages PAR for initial generation followed by NAR refinement.<n>Experiments demonstrate that PALLE, trained on LibriTTS, outperforms state-of-the-art systems trained on large-scale data.
  arXiv  Detail & Related papers  (2025-04-14T16:03:21Z)
• SimpleSpeech 2: Towards Simple and Efficient Text-to-Speech with Flow-based Scalar Latent Transformer Diffusion Models [64.40250409933752]
  We build upon our previous publication by implementing a simple and efficient non-autoregressive (NAR) TTS framework, termed SimpleSpeech 2. SimpleSpeech 2 effectively combines the strengths of both autoregressive (AR) and non-autoregressive (NAR) methods. We show a significant improvement in generation performance and generation speed compared to our previous work and other state-of-the-art (SOTA) large-scale TTS models.
  arXiv  Detail & Related papers  (2024-08-25T17:07:39Z)
• Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech [5.521191428642322]
  We present a method for building highly expressive TTS voices with as little as 15 minutes of speech data from the target speaker. Compared to the current state-of-the-art approach, our proposed improvements close the gap to recordings by 23.3% for naturalness of speech.
  arXiv  Detail & Related papers  (2021-06-24T10:52:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.