In this article, we explore the development of a new speech synthesis model called PHEME, which offers significant improvements in inference speed while maintaining high-quality synthesis. The authors combine pre-processed GigaSpeech data with the full LibriTTS dataset and randomly subsampled English from Multilingual LibriSpeech to train the 300M PHEME variant. This model requires only 1.4 seconds of processing time, while a sentence with a duration of 3 seconds takes almost 6 seconds to process with MQTTS. PHEME’s RTF scores are not impacted by expected output duration, and the reported RTFs indicate that the larger model maintains competitive production-friendly inference speed.
Key Takeaways
- PHEME is a new speech synthesis model that offers significant improvements in inference speed while maintaining high-quality synthesis.
- The authors combined pre-processed GigaSpeech data with other datasets to train the 300M PHEME variant, which requires only 1.4 seconds of processing time.
- PHEME’s RTF scores are not impacted by expected output duration, and the reported RTFs indicate that the larger model maintains competitive production-friendly inference speed.
Analogy: Imagine a race between two runners, one with a small engine (MQTTS) and the other with a high-performance electric car (PHEME). While both runners start at the same time, the electric car quickly takes the lead due to its powerful motor, and maintains it throughout the race. In this analogy, the output duration represents the distance covered during the race, and the processing time represents the time taken by each runner to complete the race.
Conclusion: PHEME is a game-changer in speech synthesis technology, offering faster inference speed without compromising on quality. This innovation opens up new possibilities for real-world applications, such as voice assistants, language learning tools, and accessibility devices, where rapid and natural-sounding speech synthesis is crucial. As the field of AI continues to advance, models like PHEME will play a vital role in shaping the future of conversational AI.
