alt Hacker NewsLooks very cool. One thing I have been looking for, which this doesn't seem to cover (at least I didn't see any mention of IPA in the model documentation), is a way to transcribe unknown languages phonetically, using the International Phonetic Alphabet to spell them (sound-based spelling rather than meaning-based spelling). I know several linguists doing research on minority languages (fewer than 10,000 speakers in some cases), which are small enough that they will never have enough effort made towards training language-specific models in that language.
Are there models I'm not aware of that are trained for this task? Taking audio in an unknown language, and rather than identifying the language, just transcribing the sounds to IPA? That would not be useful to most people, but it would be a Godsend to many, many linguists working with minority languages around the world.
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This field got reactivated recently, some new models were released (in the order of release, but the last one seems the best at the moment):
1. ZIPA https://github.com/lingjzhu/zipa
2. POWSM https://huggingface.co/espnet/powsm
3. PhoneticXEUS https://github.com/changelinglab/PhoneticXeus
I would be curious to know how to help people to use these models, or what kind of tasks they could be applied to.
I would love such a model. My wife's family is Iu Mien which is a sub group of the Dao/Yao Chinese ethnic minority. Mien is its own language but most speakers are essentially illiterate. I'm good with language but there simply isn't a course or any books for learning the language. Not much in writing to begin with given the high illiteracy rate. I would love to build a translation system - project Hail Mary style :)
Largely this is out of scope for the library, mainly because I’m not aware of many models supporting this. but if there are models which support this would be happy to support
Automatic Phoneme Recognition (APR). There are some models that do this, but they're only so-so.
It sounds like the only way this would make sense is if such model knew the range of sounds it expects to "hear". There's a lot of possible sounds that IPA knows about, but world languages only use a fraction of them at once. Think English dark and light "l" (ball/light) or aspirated "p" (pin/spin) - some languages contrast them, while in english the difference is not meaningful.
Or maybe linguists are actually interested in having maximally faithful IPA representation and manually normalizing it? You are clearly way more knowledgeable about that topic than I, so I'm curious what you think.
I learned IPA for Thai, and as part of that, I also read that a lot of professional linguists still find IPA too limited.
IPA seems very comprehensive from my amateur perspective, but apparently a lot of modern linguists still extend it or roll their own.
We take a lot of shortcuts when speaking, it's actually much harder to transcribe phonemes than to transcribe words, even when aware of the language being spoken. Some models have been trained for the task (e.g. look at https://huggingface.co/spaces/KoelLabs/IPA-Transcription-EN ), but the error rate is really high.