Tadabur-Whisper-Small

A Whisper Small model fine-tuned on Tadabur for Qur'anic speech recognition.

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Overview

Tadabur-Whisper-Small is a fine-tuned version of Whisper Small on the Tadabur dataset, as presented in the paper Tadabur: A Large-Scale Quran Audio Dataset.

• GitHub Repository: fherran/tadabur
• Project Page: fherran.github.io/tadabur

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Training Iteration

Step	Epoch	WER ↓
2,500	0.15	13.78%
5,000	0.30	11.20%
7,500	0.44	11.15%
25,000	1.48	7.89% ⭐
32,500	1.93	14.75%

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Usage

from transformers import pipeline

asr = pipeline(
    "automatic-speech-recognition",
    model="FaisaI/tadabur-whisper-small",
    generate_kwargs={"language": "arabic"}
)

result = asr("path/to/audiofile")
print(result["text"])

Or with the full Whisper API:

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import librosa

processor = WhisperProcessor.from_pretrained("FaisaI/tadabur-whisper-small")
model = WhisperForConditionalGeneration.from_pretrained("FaisaI/tadabur-whisper-small")



# Audio must be 16kHz mono
audio_array, sampling_rate = librosa.load("path/to/audiofile", sr=16000,mono=True)
inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")

predicted_ids = model.generate(**inputs, language="arabic")
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription[0])

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Limitations

• Not suitable for speaker identification or diarization.
• May underperform on noisy or low-quality recordings.
• Not fully generalized — transcription errors are expected.

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Ethical Considerations

This model is trained exclusively on Qur'anic recitation data. Users must engage with outputs respectfully and must not use this model for mockery, distortion, or any disrespectful application involving Qur'anic content.

For research and educational use only.

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Citation

@misc{alherran2026tadabur,
  author        = {Alherran, Faisal},
  title         = {Tadabur: A Large-Scale Quran Audio Dataset},
  year          = {2026},
  eprint        = {2604.18932},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SD},
  doi           = {10.48550/arXiv.2604.18932},
  url           = {https://arxiv.org/abs/2604.18932}
}