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ghananlpcommunity/
t5-small-ipa2text-v2

Name: t5-small-ipa2text-v2
Availability: InStock

ghananlpcommunity/ghana-gov-speeches-sentences-ipa-robust

google-t5/t5-small

finetune

cc-by-4.0

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T5 IPA-to-Text (Ghanaian English) — v2

Converts a Ghanaian-English IPA phoneme sequence into the English text it represents. Use it to turn phonetic output — from a G2P tool or a phoneme-recognizing ASR model — back into ordinary written English.

Usage

python

from transformers import AutoTokenizer, T5ForConditionalGeneration

tok = AutoTokenizer.from_pretrained("ghananlpcommunity/ghana-english-ipa2text-tokenizer")
model = T5ForConditionalGeneration.from_pretrained("ghananlpcommunity/t5-small-ipa2text-v2")

ipa = "ɡ ʊ d m ɔː n ɪ ŋ , l e d i s æ n d dʒ ɛ n t ə l m ɛ n !"
ids = tok(ipa, return_tensors="pt").input_ids
out = model.generate(ids)
print(tok.decode(out[0], skip_special_tokens=True))
# "Good morning, ladies and gentlemen!"

No task prefix — feed the IPA string directly. Use the paired tokenizer (ghananlpcommunity/ghana-english-ipa2text-tokenizer), not stock T5-small's — this model's vocabulary was extended with phone tokens it needs.

For a full audio → text pipeline (chaining a phoneme-ASR model in front of this one, with chunking for long audio), see scripts/infer.py in this repo, or the poto-asr library.

Training data

ghananlpcommunity/ghana-gov-speeches-sentences-ipa-robust: Ghana government/parliamentary speech, phonemised with ghana-english-g2p. Training input isn't clean G2P output as-is — each batch, most examples are run through one of five random perturbations (vowel-length collapse, weak-vowel deletion, consonant confusion/deletion, phone insertion) that mimic real ASR phoneme-recognizer errors, so the model has seen noisy input, not just perfect phonemes.

Evaluation

Held-out validation, scored with SacreBLEU against the clean reference sentence: 84.94.

Limitations

Numbers and dates are the least reliable part of the output — many spoken-number phrases are phonetically ambiguous, so rare or unseen numbers can come out wrong.
Rare proper nouns (names seen once or twice in training) are less reliable than common vocabulary.
Domain: trained entirely on formal government/parliamentary speech. Expect the best accuracy on similar formal register; casual conversation is out of domain.
Real (ASR-produced) input can occasionally trigger a repetition loop under decoding; this model's generation_config.json already sets no_repeat_ngram_size=3 and repetition_penalty=1.3 to guard against it — don't override those unless you've checked the effect on your inputs.

Model Author

Mich-Seth Owusu