M ana TTS P ersian: a recipe for creating TTS datasets for lower resource languages (2025)

Abstract: In this study, we introduce ManaTTS, the most extensive publicly accessible single-speaker Persian corpus, and a comprehensive framework for collecting transcribed speech datasets for the Persian language. ManaTTS, released under the open CC-0 license, comprises approximately 86 hours of audio with a sampling rate of 44.1 kHz. The dataset is supported by a fully transparent, MIT-licensed pipeline, a testament to innovation in the field. It includes unique tools for sentence tokenization, bounded audio segmentation, and a novel forced alignment method. This alignment technique is specifically designed for low-resource languages, addressing a crucial need in the field. With this dataset, we trained a Tacotron2-based TTS model, achieving a Mean Opinion Score (MOS) of 3.76, which is remarkably close to the MOS of 3.86 for the utterances generated by the same vocoder and natural spectrogram, and the MOS of 4.01 for the natural waveform, demonstrating the exceptional quality and effectiveness of the corpus.

## introduction text-to-speech conversion has long been an essential task. it is integrated with everyday life, including navigation systems, e-learning, content providing, and much more (maps; speechify; murfai). but one of the most vital applications of text-tospeech systems is providing accessibility for people with visual impairments, enabling written materials such as electronic device screens to be converted to speech that can be heard rather than read (nv). the reason for emphasizing the latter application is the lack of open-access, high-quality systems. some persian text-to-speech models are embedded into applications like the balad map (balad) many commercial tools like narakeet @xcite . however, no high-quality, freely available tts models can be used by the more limited audience, including the visually impaired and speech domain re-searchers. to address these challenges, it is crucial to develop open-access text-to-speech tools, which primarily require a proper text-to-speech dataset. an ideal text-to-speech dataset must meet several criteria @xcite . first, it must exhibit minimal to no mismatches in transcripts. second, it should have no background sound, including noise or background music. third, it should have a high sampling rate (at least 24 khz) to be useful for modern tts models. it is also beneficial if the transcripts include exact punctuation to help detect stops and intonations. additionally, the dataset should be large in terms of both total time duration and word coverage. therefore, the data source must be diverse and not limited to a specific domain. our investigations show that many existing textto-speech datasets for the persian language are not publicly available. on the other hand, there are serious challenges with the available data, the most important being non-open licenses, along with issues such as small size, low quality, and limited domain, which will be discussed in the related works review. hence, the first step toward open-source and open-access text-to-speech models for the persian language, and the main focus of the current study, is to prepare such a clean, large-scale and open-source dataset. in this work, we introduce a new dataset called "manatts." the word mana means "enduring" and is derived from the name of a monthly magazine devoted to the blind community, called nasle-mana (naslemana), which has been the source of our dataset. the magazine is publicly available, and the content providers were receptive to publishing the dataset with an open cc-0 license. 1 the manatts corpus has the following characteristics: we have also collected and processed persian-informal, a smaller dataset comprising informal persian text and speech. this dataset is suitable for evaluating asr models based on character error rate (cer) and is used to prioritize the asr models in the alignment tool for this work. for more details, refer to appendix b. the rest of the paper is organized as follows. the next section provides a comprehensive review of the available persian speech datasets. section 3 includes a detailed explanation of the data collection and processing methods. section 4 describes the statistics of the dataset. section 5 presents the experimental results. the final sections 6 and 7 summarize the achievements and limitations of this study. ## related works our analysis encompasses various persian datasets, including text-to-speech (tts) datasets ( ## dataset preparation as mentioned earlier, the raw material of our dataset is crawled foot_0 from the website of the nasle-mana magazine (naslemana) and is published under the cc-0 1.0 license with the consent of its owners. the majority of the audio files were recorded by a female speaker, and we manually removed any files not associated with her to ensure the dataset remained single-speaker. this data was processed through a pipeline to obtain speech and transcripts as output pairs. an overview of the entire pipeline is provided in figure 1a . the hardware utilized for the entire processing pipeline and model training consisted of a 12th gen intel core i9-12900k cpu with 24 cores and an nvidia geforce rtx 4090 gpu with 24,564 mib of memory, supporting cuda version 12.2. ## statistics raw files: nasl-e-mana maintains an archive of over 41 magazines spanning over three years. the archive comprises a total of over 568 audio-text files. the audio files underwent manual inspection to ensure the dataset consisted of single-speaker recordings. following this review, four files were excluded from the raw material, resulting in 564 files for automated processing. the duration of the audio files ranged from approximately 0.5 to 34 minutes, with an average duration of about 10 minutes. similarly, the lengths of the text files varied, with word counts ranging from 44 to 3951 and an average length of 1234 words. processed chunks count: executing the pipeline on the raw material yielded a minimum, maximum, and average of approximately 4, 398, and 118 chunks per file, respectively, totaling 66172 chunks overall. roughly 97.98% of these chunks were automatically accepted as having good quality, while 1338 (about 2.02%) were rejected due to an unacceptable cer between the hypothesis transcript and the matching text. consequently, the final dataset comprises 64834 accepted audio-text chunks. accepted chunks duration: as previously mentioned, our pipeline's chunking method guarantees that audio chunks have durations ranging from a minimum of 2 seconds to a maximum of 12 seconds. the histogram depicting the duration distribution of the audio chunks is illustrated in figure 3 . accepted chunks search type: as outlined in previous sections, two search methods matched a hypothesis transcript with the ground truth text: interval search and gapped search. it should be pointed out that the interval search method is preferred because of its lower computational cost. consequently, if a match meeting the acceptance criteria is found through interval search, further searching with the gapped search is unnecessary. gapped search is primarily utilized when the ground truth text does not perfectly align with the hypothesis. analysis of chunk information reveals that approximately 99.39% of matching text chunks are identified through interval search, while the remaining 0.61% (397 chunks) are the result of gapped search. accepted chunks match quality: as mentioned, there are two threshold levels for cer of audio chunks. the first, labeled high, signifies a match between the hypothesis and ground truth text with a cer less than 0.05. the second, labeled mid-dle, denotes an acceptable cer of 0.05 to 0.2. attaining the high threshold during the search prompts an immediate acceptance of the chunk, whereas achieving the middle threshold would only accept the chunk at the end of each search type. approximately 71.46% of the chunks (46330 in total) have the high and about 28.54% of the chunks (18504 in total) have the middle match quality. figure 4 illustrates the joint distribution of the search type and match quality of the audio-text chunks, as they are correlated. it's also intriguing to visualize the distribution of cer values for all the chunks that passed through the dataset creation pipeline. as illustrated in figure 5 , there is only a small number of rejected chunks compared to the matched chunks in the other groups. manual investigations indicate that these rejected chunks are primarily associated with an underlying discrepancy between the raw audio and text files. other reasons for rejection include utterances that differ in their written and spoken forms, which will be further discussed in the section 8. word count: the accepted chunks exhibit a range of 1 to 38 words, with an average of approximately 11 words per chunk. the histogram illustrating the distribution of word counts can be found in figure 6 . overall, the dataset contains a total of approximately 24,113 unique words. ## experiments in this section, we present the experiments conducted to evaluate the quality and accuracy of the manatts dataset and the tts model trained on it. the experiments are divided into two main parts: (1) assessing the quality of the trained tts model and (2) measuring the transcription accuracy of the dataset. ## discussion the absence of high-quality, open-source/openaccess text-to-speech models and datasets for the persian language has been highlighted in section a. 4 for other objective methods, please refer to appendix d. below are some of the critical challenges associated with the available corpora. the manatts dataset introduced in this work is the first persian language text-to-speech corpus that addresses all the above challenges. this dataset is publicly available under an open cc-0 1.0 license. it includes utterances from a monthly magazine over three years and covers various persian language utterances. the speech and ground truth text in this dataset are collected by human agents, not synthesized. most notably, it is the largest singlespeaker text-to-speech dataset available to date. another notable contribution of this work is that it represents the first fully open-source textto-speech data collection project for the persian language. due to the open code base, all steps, including data crawling, processing, and model training, are reproducible. this approach helps develop additional persian speech datasets and offers two fundamental benefits. first, in addition to the standard tools used in typical speech data processing, this project introduces a new sentence tokenization method and a new start-end alignment and forced alignment tool capable of aligning speech and text pairs that are not exact matches but are slightly different. our work demonstrates that this tool can be effectively used with publicly available persian asr models of moderate accuracy, thus contributing yet another open-license tool to the community. second, the ever-growing monthly magazine of nasl-e-mana and the fully available data collection and processing pipeline make this dataset easily extendable. future work can obtain an even larger dataset by executing a few scripts. the experiments reveal that the tts model, trained on the manatts dataset, achieved a mos of 3.76, slightly lower than natural spectrograms ## conclusion in this work, we proposed a processing pipeline to create a tts dataset from raw speech and text files. applying the pipeline to the archive of the nasl-e-mana magazine, we published manatts, the largest single-speaker persian tts dataset, under the open cc-0 1.0 license. additionally, we released a smaller transcribed speech dataset, persian-informal, which serves as a valuable test dataset for evaluating asr models and is utilized in our novel forced alignment method. evaluating manatts involved training a tacotron2-based tts model. the samples synthesized by this model exhibited remarkable naturalness, comparing favorably to both the utterances generated from gold speech spectrograms and natural speech waveforms.