meetpateltechVoxtral Transcribe 2
Is there an open source Android keyboard that would support it? Everything I find is based on Whisper, which is from 2022. Ages ago given how fast AI is evolving.
I wish I had a Google Keyboard that could easily run on Whisper Medium. This is already great. But unfortunately would be too much inference cost, incredibly slow. The problem with Whisper is not the inference quality: medium and large are incredible. Is that the base model is not enough, and the only one with fast inference in mobile devices.
Italian represents, I believe, the most phonetically advanced human language. It has the right compromise among information density, understandability, and ability to speech much faster to compensate the redundancy. It's like if it had error correction built-in. Note that it's not just that it has the lower error rate, but is also underrepresented in most datasets.
I love seeing people from other countries share their own folk tales about what makes their countries special and unique. I've seen it up close in my country and I always cringed when I heard my fellow countrymen came up with these stories. In my adulthood I'm reassured that it happens everywhere and I find it endearing.
On the information density of languages: it is true that some languages have a more information dense textual representation. But all spoken languages convey about the same information in the same time. Which is not all that surprising, it just means that human brains have an optimal range at which they process information.
Further reading: Coupé, Christophe, et al. "Different Languages, Similar Encoding Efficiency: Comparable Information Rates across the Human Communicative Niche." Science Advances. https://doi.org/10.1126/sciadv.aaw2594
Different representations at the same bitrate may have features that make one a lot more resilient to errors. This thing about Italian, you fill find in any benchmark of vastly different AI transcribing models. You can find similar results also on the way LLMs mostly trained on English generalize usually very well with Italian. All this despite Italian accounting for marginal percentage of the training set. How do you explain that? I always cringe when people refute evidence.
Disappointing how this lacks a clear reference implementation, if not mixed at almost yet unreleased VLLM (nightly version) stuff. I'm ok with Open Weights being a form of OSS in the case of models, because frankly I don't believe that, for large LLMs, it is feasible to release the training data, all the orchestration stuff, and so forth. But it can't be: here are the weights, we partnered with VLLM for inference. Come on. Open Weights must mean that you put me in a situation to write an implementation easily for any hardware.
p.s. even the demo uses a remote server via websocket.