WhisperSpeech VS llama.cpp

I haven't tried openvoice, but I did try whisperspeech and it will do the same thing. You can optionally pass in a file with a reference voice, and the tts uses it.

https://github.com/collabora/whisperspeech

I found it to be kind of creepy hearing it in my own voice. I also tried a friend of mine who had a french canadian accent and strangely the output didn't have his accent.

- WhisperSpeech for the text-to-speech - https://github.com/collabora/WhisperSpeech

and an LLM (phi-2, Mistral, etc.) in between

Hi, I used the [WhisperSpeech](https://github.com/collabora/WhisperSpeech) model for the TTS part after I did some serious torch.compile optimizations to bring the latency down. The Whisper speech recognition and the LLM were optimized through TensorRT-LLM by Marcus and Vineet.

It's not perfect but I am still extremely proud of how it came out. :)

I think you’re talking about just using Whisper to annotate audio for a TTS pipeline but someone from Collabora actually created a TTS model directly from Whisper embeddings https://github.com/collabora/WhisperSpeech

if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp

the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...

And you can search for any GGUF on huggingface

Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182

For anyone interested in exploring this, llama.cpp has an example implementation here:

https://github.com/ggerganov/llama.cpp/tree/master/examples/...

Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.

well https://github.com/ggerganov/llama.cpp/issues/6849