RNNT head revived. v1.0's RNNT head was broken (a tokenizer/blank misalignment made greedy decoding loop forever, ~4398% WER). v1.5 surgically re-initializes the RNNT prednet+joint under the ve_tok_v4 tokenizer and trains it up via a 2-phase curriculum -> a working transducer head.
CTC head preserved. The deployed, record-setting CTC head is kept identical to v1.0 (encoder frozen during the corrective RNNT finetune) -> zero CTC regression.

Benchmarks (golden-6669, strict fair_text normalizer, att_context [70,13])

head	v1.0	v1.5
CTC	8.12%	8.12% (identical)
RNNT	4398% (broken)	9.50%

CTC remains the recommended/deployed head; RNNT is newly functional. Character error rate ~2.6%.

Usage

python

from nemo.collections.asr.models import ASRModel
m = ASRModel.from_pretrained("Reza2kn/Shenava-Koochik-v1.5")
m.change_decoding_strategy(decoder_type="ctc")   # or "rnnt"
print(m.transcribe(["audio.wav"]))

Notes

RNNT output leans colloquial (trained on audio-faithful ASR labels).
Trained on a cleaned mix (pseudo corpus + synthetic hardwords + human/article gold) with benchmark de-duplication and label-quality filtering (over-extension / repetition-loop removal).