Automatic Speech Recognition
NeMo
ONNX
Arabic
audio
arabic
quran
quranic-recitation
tajweed
fastconformer
mispronunciation-detection
Eval Results (legacy)
Instructions to use Muno459/fastconformer-quran with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use Muno459/fastconformer-quran with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("Muno459/fastconformer-quran") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Fix numpy 2 OverflowError in ctc_forced_align backtracking
#7
by mr-taha - opened
Under numpy >= 2, ctc_forced_align crashes with OverflowError: Python integer N out of bounds for int8 on any text longer than ~127 CTC states — i.e. anything beyond one short ayah. The backtracking loop adds a Python int state index to an element of the int8 back array, which numpy 2 now treats as an unsafe cast error instead of silently wrapping. I tested on multi-ayah passages (Āl ʿImrān 3:4–6, 161 letters) with numpy 2.2.6 and onnxruntime 1.23 on CPU — forced alignment and the full scorer both work correctly after the change.
Thanks for publishing this model — the tajweed rule engine on top of the CTC alignment is genuinely excellent work. جزاكم الله خيرا