> Running a family was a brutal two-person job -- and the kids had to dive in to help out the second they could lift something heavier than a couple pounds.
Orphanes did struggle but most families were not just two person, families were big and supported by community.
And moreover, you can not tune those models for practical applications. The model is originally trained on very clean data, so lower layers are also not very stable for diverse inputs. To finetune you have to update the whole model, not just upper layers.
This model is actually expected to be bad for popular languages, just like previous MMS it is not accurate at all, it wins by supporting something rare well but never had good ASR accuracy even for Swedish etc. It is more a research thing than a real tool. Unlike Whisper.
No, there are mathematical reasons LLMs are better. They are trained with multiobjective loss (coding skills, translation skills, etc) so they understand the world much better than MLM. Original post discuss that but with more words and points than necessary.
It is actually pretty straightforward why those model "reason" or, to be more exact, can operate on a complex concepts. By processing huge amount of texts they build an internal representation where those concepts are represented as a simple nodes (neurons or groups). So they really distill knowledge. Alternatively you can think about it as a very good principal component analysis that can extract many important aspects. Or like a semantic graph built automatically.
Once knowledge is distilled you can build on top of it easily by merging concepts for example.
Err, I deeply respect Amazon TTS team but this paper and synthesis is..... You publish the paper in 2024 and include YourTTS in your baselines to look better. Come on! There is XTTS2 around!
Voice sounds robotic and plain. Most likely a lot of audiobooks in training data and less conversational speech. And dropping diffusion was not a great idea, voice is not crystal clear anymore, it is more like a telephony recording.
Metavoice is one of a dozen GPT-based TTS systems around starting from Tortoise. And not that great honestly. You can clearly hear "glass scratches" in their sound, it is because they trained on MP3-compressed data.
There are much more clear sounding systems around. You can listen for StyleTTS2 to compare.
So we have numbers on PTB original perplexity 8.79 quantized 9.68, already 10% worse. And PPL reported per token I suppose? Because word PPL for PTB must be around 20, not less than 10.
https://en.bouffalolab.com/product/?type=detail&id=16
voice processing is in hardware unfortunately, but it exposes some things like DOA