@runboubou
Your laptop is now the voice of your whole house.
Boubou turns open-source TTS models into local runtimes with one command. Your phone, robot or smart home on the same Wi-Fi just talks to it.
100% local. Built on @LiquidAI_ + @huggingface 🤗
github.com/runboubou/boub…View quoted post on X
You can transcribe every word correctly and still end up with bad TTS training data.
Knowing what was said isn’t enough. You also need to know who said it, even when speakers keep switching back and forth.
Here’s a quick look at speaker diarization in our data pipeline.View quoted post on X
A step-by-step guide to building a TTS training dataset at the scale of tens of millions of samples.
Over the past few months, we've been working on ways to collect and build high-quality TTS training datasets at scale. Compared to collecting text data for LLMs, working with speech data is significantly more complicated, with plenty of engineering challenges along the way.
There's surprisingly little public discussion about this, especially when it comes to the practical challenges of collecting speech data at scale.
So I wanted to share what we've learned over the past few months: the problems we've run into, the lessons learned, and how we designed and built our data engine from scratch.
Hopefully, this will be useful to others working on TTS and large-scale speech datasets. We're also hoping to open-source the full codebase down the road so others can build on it.
I also put together a video to make the article easier to follow.View quoted post on X
10 hours of data. One afternoon.
Your TTS model can sound like a whole different beast.View quoted post on X
