RenikudPlus — Hebrew grapheme-to-phoneme

Hebrew text in, IPA out, with stress marks. Trained without any benchmark-derived data; every number below reproduces from these files. On 126k words of unseen podcast speech it scores 93.3% word accuracy.

word accuracy	RenikudPlus	int8	Gemini	ReNikud	Phonikud
OVERALL	86.2	85.9	84.6	78.2	66.4
Gender	99.3	99.3	77.0	59.9	37.5
Min. Stress Pairs	90.0	91.3	88.0	80.7	78.7
Stress Homographs	91.1	90.6	92.6	79.8	78.3
Names	80.0	82.0	75.3	67.3	68.0
Acronyms	79.6	76.3	79.0	56.6	37.5
Slang	75.6	75.0	71.2	59.0	41.7
Penultimate Stress	85.4	86.1	89.4	82.1	60.9
Rare Phonemes	58.3	55.0	57.6	41.1	19.9
Foreign	78.1	77.4	71.6	56.8	36.1
Colloquial	51.0	51.0	25.2	54.3	9.3
ILSpeech-test	93.2	93.1	96.0	92.5	85.5

from renikud_onnx import G2P                 # pip install onnxruntime numpy
g2p = G2P("model.onnx")                      # or model_int8.onnx
g2p.phonemize("הלכתי לספר וקראתי ספר בזמן שהוא ספר כמה אצבעות יש לו")
# halˈaχti lesapˈeʁ vekaʁˈati sˈefeʁ bizmˈan ʃehˈu safˈaʁ kˈama ʔetsbaʔˈot jˈeʃ lˈo
g2p.phonemize("המחיר 1250 שקלים")            # digits -> words: hameχˈiʁ ʔˈelef matˈajim…
g2p.phonemize("אני רוצה להגיד לך משהו חשוב", speaker=2, target_speaker=1)  # 0 unknown, 1 male, 2 female

• model.onnx — fp32 weights
• model_int8.onnx — quantized, 4× smaller
• renikud_onnx.py — wrapper (decoding, long inputs, numbers, speaker inputs)