ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis
Abstract
Existing Persian speech datasets are typically smaller than their English counterparts, which creates a key limitation for developing Persian speech technologies. We address this gap by introducing ParsVoice, the largest Persian speech corpus designed specifically for text-to-speech(TTS) applications. We created an automated pipeline that transforms raw audiobook content into TTS-ready data, incorporating components such as a BERT-based sentence completion detector, a binary search boundary optimization method for precise audio-text alignment, and audio-text quality assessment frameworks tailored to Persian. The pipeline processes 2,000 audiobooks, yielding 3,526 hours of clean speech, which was further filtered into a 1,804-hour high-quality subset suitable for TTS, featuring more than 470 speakers. To validate the dataset, we fine-tuned XTTS for Persian, achieving a naturalness Mean Opinion Score (MOS) of 3.6/5 and a Speaker Similarity Mean Opinion Score (SMOS) of 4.0/5 demonstrating ParsVoice's effectiveness for training multi-speaker TTS systems. ParsVoice is the largest high-quality Persian speech dataset, offering speaker diversity and audio quality comparable to major English corpora. The complete dataset has been made publicly available to accelerate the development of Persian speech technologies. The ParsVoice dataset is publicly available at: https://huggingface.co/datasets/MohammadJRanjbar/ParsVoice.
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 ParsVoice, the largest high-quality Persian speech corpus for TTS applications, along with a comprehensive automated pipeline for its creation. This work significantly enhances the resources available for Persian language technology, addressing a critical gap in the field and paving the way for future advancements in TTS systems for low-resource languages.
Methodology
The paper presents a well-structured methodology for creating a large-scale Persian speech corpus, addressing the unique challenges of TTS data generation in low-resource languages. The automated pipeline includes innovative components such as a BERT-based sentence completion model, a binary search boundary optimization method, and a comprehensive quality assessment framework tailored to Persian. The segmentation and alignment techniques are particularly noteworthy, as they ensure high-quality audio-text pairs essential for TTS applications. However, the methodology could benefit from a clearer explanation of the integration of these components and their individual contributions to the overall quality of the dataset.
Results & evaluation
The authors conducted a thorough evaluation of the ParsVoice dataset by fine-tuning the XTTS model, achieving competitive MOS scores that demonstrate the dataset's effectiveness for TTS applications. The results indicate that the corpus is capable of producing natural-sounding speech and maintaining speaker similarity, which are critical metrics for TTS systems. However, the evaluation could be strengthened by including more detailed comparisons with existing datasets and models to contextualize the performance of ParsVoice.
Reproducibility
The paper provides a clear description of the dataset creation process and the evaluation methodology, which aids in reproducibility. The availability of the dataset on Hugging Face is a significant step towards ensuring that other researchers can replicate the findings and build upon the work. However, more information on the specific configurations and hyperparameters used during model training would enhance reproducibility.
Limitations
One limitation of the study is the reliance on a single data source (IranSeda), which may introduce biases in speaker representation and content diversity. Additionally, while the dataset is large, the quality assessment metrics may not capture all nuances of speech quality, and further subjective evaluations could provide a more comprehensive understanding of the dataset's strengths and weaknesses.
Broader impact
The introduction of ParsVoice has the potential to significantly advance Persian speech technologies, enabling researchers and developers to create more robust TTS systems. This work not only addresses the data scarcity issue for Persian but also sets a precedent for similar efforts in other low-resource languages. The public availability of the dataset encourages collaboration and innovation in the field of speech synthesis. The main contribution of this paper is the introduction of ParsVoice, the largest high-quality Persian speech corpus for TTS applications, along with a comprehensive automated pipeline for its creation. This work significantly enhances the resources available for Persian language technology, addressing a critical gap in the field and paving the way for future advancements in TTS systems for low-resource languages.
Author of this paper? Send a correction.