WhisperSpeech – An open source text-to-speech system built by inverting Whisper (github.com)

Article summary

WhisperSpeech is an open-source text-to-speech system built by inverting OpenAI Whisper, with the goal of being a powerful and commercially safe speech synthesis tool. The system uses a two-stage, token-based pipeline and has been trained on English and Polish datasets. The developers plan to add more languages and improve the model's performance. WhisperSpeech is available on GitHub and can be tested on Colab.

Main themes

• text-to-speech synthesis
• open-source models
• language learning
• speech recognition
• AI development
• computational resources
• language support
• error reduction

What commenters say

• Some commenters are impressed by the quality of WhisperSpeech and its potential for language learning and speech synthesis.
• Others note that WhisperSpeech requires significant computational resources and may not be suitable for all use cases.
• There is a need for high-quality speech recordings in various languages to improve the model's performance.
• Some commenters are concerned about the potential for hallucination and errors in speech synthesis, particularly in languages like Chinese.
• The development of WhisperSpeech and similar models is seen as part of a rapid advancement in AI and language technology.
• Some commenters are disappointed by the slow pace of progress in getting these technologies into production.
• The use of open-source models like WhisperSpeech and EmotiVoice is seen as a way to promote accessibility and innovation in speech synthesis.
• There is a discussion about the potential for WhisperSpeech to be run on lower-end hardware, such as Mac M1 or Raspberry Pi.