Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech
Abstract
Recent advances in text-to-speech (TTS) models show impressive speech naturalness and quality, yet the role of large-scale open data in driving this progress remains underexplored. In this work, we introduce Raon-OpenTTS, an open TTS model that performs competitively with state-of-the-art closed-data TTS models, and Raon-OpenTTS-Pool, a large-scale open dataset for reproducible TTS training. Raon-OpenTTS-Pool consists of 615K hours of 240M speech segments aggregated from publicly available English speech corpora and web-sourced recordings. With a model-based filtering pipeline applied to Raon-OpenTTS-Pool, we derive Raon-OpenTTS-Core, a curated, high-quality subset of 510K hours and 194M speech segments. Using Raon-OpenTTS-Core, we train Raon-OpenTTS, a series of diffusion transformer (DiT)-based TTS models from 0.3B to 1B parameters. On multiple benchmarks, Raon-OpenTTS-1B shows comparable performance to state-of-the-art models such as Qwen3-TTS and CosyVoice 3, which are trained on several million hours of proprietary speech data. Notably, on Seed-TTS-Eval, Raon-OpenTTS-1B achieves a word error rate (WER) of 1.78% and a speaker similarity (SIM) of 0.749, ranking second on WER and first on SIM among recent open-weight TTS baselines. On CV3-Hard-EN, Raon-OpenTTS-1B achieves a WER of 6.15% and a SIM of 0.775, ranking first on both metrics. Furthermore, to support robust evaluation, we introduce Raon-OpenTTS-Eval, a structured benchmark for assessing TTS robustness across diverse acoustic conditions including clean, noisy, in-the-wild, and expressive speech. On Raon-OpenTTS-Eval, Raon-OpenTTS-1B achieves the best average WER and SIM among all evaluated models, and the second-best human preference, as measured by comparative mean opinion score (CMOS). Our data pool, filtering pipeline, training code, and checkpoints are publicly available at https://github.com/krafton-ai/RAON-OpenTTS.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of Raon-OpenTTS, a competitive open TTS model trained on a large-scale, high-quality dataset, along with a structured evaluation benchmark for assessing TTS robustness. This work addresses the critical need for open data and models in TTS research, facilitating reproducibility and advancing the state of the art in the field.
Methodology
The paper presents a robust methodology for constructing a large-scale open TTS dataset, Raon-OpenTTS-Pool, and a high-quality subset, Raon-OpenTTS-Core, which are critical for training the Raon-OpenTTS models. The authors utilize a model-based filtering pipeline to ensure data quality, which is a significant improvement over existing methods that often rely on proprietary datasets. The use of diffusion transformers (DiT) for TTS synthesis is an innovative approach that enhances the model's performance across various benchmarks. The introduction of a structured evaluation benchmark, Raon-OpenTTS-Eval, further strengthens the methodology by enabling comprehensive assessments across diverse acoustic conditions.
Results & evaluation
The experimental evaluation is thorough, with multiple benchmarks used to assess the performance of Raon-OpenTTS-1B against state-of-the-art models. The results demonstrate competitive performance in terms of word error rate (WER) and speaker similarity (SIM), indicating that the model can effectively generalize across different acoustic environments. The inclusion of human preference evaluations adds a valuable subjective dimension to the assessment, reinforcing the model's practical applicability.
Reproducibility
The paper emphasizes reproducibility by providing access to the dataset, model weights, and training code. This transparency is crucial for the research community, as it allows others to replicate the study and build upon the findings. The detailed description of the data collection and filtering process enhances the reproducibility of the results.
Limitations
One limitation is the focus on English speech data, which restricts the applicability of the findings to other languages. Additionally, while the filtering process improves data quality, it may also lead to the exclusion of potentially useful data. The authors acknowledge the need for future work to explore multilingual settings and more effective data mixing strategies.
Broader impact
The development of Raon-OpenTTS and its associated datasets has the potential to significantly impact the field of TTS by providing a high-quality, open-source alternative to proprietary models. This could democratize access to advanced TTS technology, enabling researchers and developers to create more inclusive and diverse applications. The structured evaluation benchmark also sets a precedent for future TTS research, promoting more rigorous assessments of model performance. The main contribution of this paper is the introduction of Raon-OpenTTS, a competitive open TTS model trained on a large-scale, high-quality dataset, along with a structured evaluation benchmark for assessing TTS robustness. This work addresses the critical need for open data and models in TTS research, facilitating reproducibility and advancing the state of the art in the field.
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