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ghananlpcommunity/ghana-english-ipa2text-tokenizer
ghana-english-ipa2text-tokenizer is a machine learning model from ghananlpcommunity. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-4.0.
google-t5/t5-small's tokenizer, extended to also read Ghanaian-English IPA phoneme sequences — 80 new tokens added for phones the base vocabulary doesn't have (ɡ, tʃ, ŋ, etc.), taking it from 32,100 to 32,180 tokens.…
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Updated Aug 17, 2026
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From the Hugging Face model README
google-t5/t5-small's tokenizer, extended to also read Ghanaian-English IPA phoneme sequences —
80 new tokens added for phones the base vocabulary doesn't have (ɡ, tʃ, ŋ, etc.), taking it
from 32,100 to 32,180 tokens. Ordinary English text still tokenizes identically to stock
google-t5/t5-small (verified: byte-identical ids on English input).
from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("ghananlpcommunity/ghana-english-ipa2text-tokenizer")
tok("The Governor raised the policy rate.").input_ids # same ids as stock t5-small
tok("ɡ ʊ d m ɔː n ɪ ŋ").input_ids # phones tokenize cleanly, no <unk>
IPA input should be space-separated phones with punctuation kept as its own tokens (the format
ghana-english-g2p and
ghananlpcommunity/ghana-english-phoneme-asr
both produce). Numbers should be spelled out as words before phonemising
("1,000" → "one thousand"), not phonemised digit-by-digit.
ghananlpcommunity/t5-small-ipa2text-v2 —
fine-tuned with this tokenizer to convert Ghanaian-English IPA into text. Use that model's
tokenizer copy (same repo either way) rather than mixing this with a different checkpoint.