We can! At Kyutai, we released a real-time, on-device speech translation demo last week. For now, it is working only for French to English translation, on an iPhone 16 Pro: https://x.com/neilzegh/status/1887498102455869775
We decided to keep the casing, as it is useful for some applications such as named entity recognition.
Regarding the punctuation, as pointed out in another comment, these tokens might also be useful for some applications (and they are easy to filter out if you don't need them).
Hi, because we trained these vectors on Wikipedia, we released models corresponding to the 90 largest Wikipedia first (in term of training data size). More models are on the way, including Irish.
Models are trained independently for each language. So unfortunately, you cannot directly compare words from different languages using these vectors.
If you have a bilingual dictionary, you might try to learn a linear mapping from one language to the other (e.g. see https://arxiv.org/abs/1309.4168 for this approach).
The graph algorithm described in the blogpost is more related to label propagation (which is more than 10 years old), than to "retrofitting". And the Google paper linked in the blogpost is citing the relevant literature correctly.
Thanks for pointing this out. We design this library on large datasets and some static variables may not be well tuned for smaller ones. For example the learning rate is only updated every 10k words. We are fixing that now, could you please send us on which dataset you were testing? We would like to see if we have solved this.
At train time, the code supports multiple labels by sampling one of the k label at random. At test time, it only predicts the most probable label for each example.
We will add more functionalities for multi label classification in the future (predict the top k labels, etc...).
A lot of people play the lottery. Only one person did this experiment. When you test multiple hypothesis (as in your lottery example), you need to perform a correction[2,3].
Members of the editorial board of scientific journals are not paid. So nothing prevents having a completely free and open access journal, which is peer reviewed. As a matter of fact, such journals already exist in some fields (e.g. the Journal of Machine Learning Research[1], which is the best journal in machine learning).
We released inference code and weights, you can check our github here: https://github.com/kyutai-labs/hibiki