Implementing a Text-to-Speech synthesis model on a Raspberry Pi for Industrial Applications

Mandeel, Ali Raheem; Aggar, Ammar Abdullah; Al-Radhi, Mohammed Salah; Csapó, Tamás Gábor

doi:10.3311/wins2023-014

Cited by 1 publication

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With p > 0.05, the two systems were significantly different. Moreover, it outperforms our earlier study results on RPi with the TTS model of FastSpeech 2 and HiFi-GAN V1, which obtained RTF 8.93 [21]. After that, we tested the cache mechanism in the system with the same ten sentences.…”

Section: Real-time System Evaluation and Runtime Analysismentioning

confidence: 75%

See 1 more Smart Citation

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

et al. 2023

Self Cite

View full text Add to dashboard Cite

Oil refineries have high operating expenses and are often exposed to increased asset integrity risks and functional failure. Real-time monitoring of their operations has always been critical to ensuring safety and efficiency. We proposed a novel Industrial Internet of Things (IIoT) design that employs a text-to-speech synthesizer (TTS) based on neural networks to build an intelligent extension control system. We enhanced a TTS model to achieve high inference speed by employing HiFi-GAN V3 vocoder in the acoustic model FastSpeech 2. We experimented with our system on a low resources-embedded system in a real-time environment. Moreover, we customized the TTS model to generate two target speakers (female and male) using a small dataset. We performed an ablation analysis by conducting experiments to evaluate the performance of our design (IoT connectivity, memory usage, inference speed, and output speech quality). The results demonstrated that our system Real-Time Factor (RTF) is 6.4 (without deploying the cache mechanism, which is a technique to call the previously synthesized speech sentences in our system memory). Using the cache mechanism, our proposed model successfully runs on a low-resource computational device with real-time speed (RTF equals 0.16, 0.19, and 0.29 when the memory has 250, 500, and 1000 WAV files, respectively). Additionally, applying the cache mechanism has reduced memory usage percentage from 16.3% (for synthesizing a sentence of ten seconds) to 6.3%. Furthermore, according to the objective speech quality evaluation, our TTS model is superior to the baseline TTS model.

show abstract

Section: Real-time System Evaluation and Runtime Analysismentioning

confidence: 75%

“…Following our previous study [21], we built an intelligent alarm model using a modified TTS of an acoustic model (FastSpeech 2) with a neural vocoder (HiFi-GAN V3). We compared our TTS model inference speech speed with a baseline TTS model (FastSpeech 2 and MelGAN vocoder).…”

Section: Introductionmentioning

confidence: 99%