How We Used the LibriTTS Dataset to Train the Hierarchical Speech Synthesizer | HackerNoonThe paper discusses training a hierarchical speech synthesizer using the LibriTTS dataset, emphasizing the importance of data diversity for robust voice style transfer.
The 7 Objective Metrics We Conducted for the Reconstruction and Resynthesis Tasks | HackerNoonThe article explores advanced speech synthesis tasks using various metrics for evaluation, focusing on voice conversion and text-to-speech models.It details the experimentation and methodologies applied in evaluating speech synthesis quality.
HierSpeech++: How Does It Compare to Vall-E, Natural Speech 2, and StyleTTS2? | HackerNoonThe Hierspeech++ model outperforms existing models in naturalness and prompt similarity for zero-shot speech synthesis.The evaluation revealed important limitations in similarity with ground truth versus prompt-generated speech.
Style Prompt Replication: A Simple Trick That Helped Us In Our Journey | HackerNoonStyle Prompt Replication (SPR) enables effective synthesis from short speech prompts, enhancing style transfer in speech generation.
Zero-shot Voice Conversion: Comparing HierSpeech++ to Other Basemodels | HackerNoonHierSpeech++ demonstrates superior performance in voice style transfer compared to traditional models, significantly enhancing naturalness in speech synthesis.
A Deeper Look at Speech Super-Resolution | HackerNoonSpeechSR improves speech super-resolution by upsampling from 16 kHz to 48 kHz with superior performance and efficiency over existing models.
How We Used the LibriTTS Dataset to Train the Hierarchical Speech Synthesizer | HackerNoonThe paper discusses training a hierarchical speech synthesizer using the LibriTTS dataset, emphasizing the importance of data diversity for robust voice style transfer.
The 7 Objective Metrics We Conducted for the Reconstruction and Resynthesis Tasks | HackerNoonThe article explores advanced speech synthesis tasks using various metrics for evaluation, focusing on voice conversion and text-to-speech models.It details the experimentation and methodologies applied in evaluating speech synthesis quality.
HierSpeech++: How Does It Compare to Vall-E, Natural Speech 2, and StyleTTS2? | HackerNoonThe Hierspeech++ model outperforms existing models in naturalness and prompt similarity for zero-shot speech synthesis.The evaluation revealed important limitations in similarity with ground truth versus prompt-generated speech.
Style Prompt Replication: A Simple Trick That Helped Us In Our Journey | HackerNoonStyle Prompt Replication (SPR) enables effective synthesis from short speech prompts, enhancing style transfer in speech generation.
Zero-shot Voice Conversion: Comparing HierSpeech++ to Other Basemodels | HackerNoonHierSpeech++ demonstrates superior performance in voice style transfer compared to traditional models, significantly enhancing naturalness in speech synthesis.
A Deeper Look at Speech Super-Resolution | HackerNoonSpeechSR improves speech super-resolution by upsampling from 16 kHz to 48 kHz with superior performance and efficiency over existing models.