ArmanTTS single-speaker Persian dataset
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Abstract (arXiv)
TTS, or text-to-speech, is a complicated process that can be accomplished through appropriate modeling using deep learning methods. In order to implement deep learning models, a suitable dataset is required. Since there is a scarce amount of work done in this field for the Persian language, this paper will introduce the single speaker dataset: ArmanTTS. We compared the characteristics of this dataset with those of various prevalent datasets to prove that ArmanTTS meets the necessary standards for teaching a Persian text-to-speech conversion model. We also combined the Tacotron 2 and HiFi GAN to design a model that can receive phonemes as input, with the output being the corresponding speech. 4.0 value of MOS was obtained from real speech, 3.87 value was obtained by the vocoder prediction and 2.98 value was reached with the synthetic speech generated by the TTS model.
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References · 14
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2022Generative Adversarial Networkscited by 5,603
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
- 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
- 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 639
- 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
- 2019MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesiscited by 351
- 2018Adversarial Audio Synthesiscited by 222
- 2021A Survey on Neural Speech Synthesiscited by 182
- 2019High Fidelity Speech Synthesis with Adversarial Networkscited by 90
2 more
- 2021Hi-Fi Multi-Speaker English TTS Datasetcited by 60
- 2021HUI-Audio-Corpus-German: A high quality TTS datasetcited by 14
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