#python #deep_learning #glow_tts #hifigan #melgan #multi_speaker_tts #python #pytorch #speaker_encoder #speaker_encodings #speech #speech_synthesis #tacotron #text_to_speech #tts #tts_model #vocoder #voice_cloning #voice_conversion #voice_synthesis

The new version of TTS (Text-to-Speech) from Coqui.ai, called TTSv2, is now available with several improvements. It supports 16 languages and has better performance overall. You can fine-tune the models using the provided code and examples. The TTS system can now stream audio with less than 200ms latency, making it very responsive. Additionally, you can use over 1,100 Fairseq models and new features like voice cloning and voice conversion. This update also includes faster inference with the Tortoise model and support for multiple speakers and languages. These enhancements make it easier and more efficient to generate high-quality speech from text.

https://github.com/coqui-ai/TTS

#python #fno #fourier_neural_operator #neural_operator #neural_operators #partial_differential_equations #pde #pytorch #tensor_methods #tensorization #tensorly #uno

The `neuraloperator` library is a powerful tool for learning neural operators in PyTorch. It allows you to learn mappings between function spaces, which is different from regular neural networks. This library is useful because it makes your trained models work with data of any resolution, meaning you don't have to worry about the size of your data. You can easily install it using `pip install neuraloperator` and start training operators right away. The library also offers efficient models like the Tucker Tensorized FNO, which reduces the number of parameters needed, making it faster and more efficient. This helps you train and use complex models more effectively.

https://github.com/neuraloperator/neuraloperator