Training Tacotron 2 for Text-to-Speech Synthesis in Kreole Language: Methodology and Audio Processing

This paper presents a methodology for training Tacotron 2 for text-to-speech synthesis in the Kreole language, aiming to improve access to textual information for visually impaired individuals. This paper discusses Tacotron 2’s architecture, reviews relevant literature, and presents experimental implementations. By synthesizing current research trends serves as a valuable resource for TTS advancement. We trained the Tacotron 2 model to map input text sequences to Mel-spectrograms representing speech segments using a dataset of 25 audio recordings matched to their corresponding text transcripts. Segmenting audio data, using Google Colab for model training, and using HiFi-GAN as a vocoder for audio synthesis were all parts of the training process. The completeness, repeatability, and clarity of the synthesized speech were assessed in the listening assessments. Tacotron 2 performed admirably, generating intelligible sounds for 13 of 25 words despite the small sample. This study addresses the future and focuses Tacotron 2’s promise in low-resource language environments.