This model is a fine-tuned version of OpenAI’s Whisper Large V3 model, adapted specifically for Arabic Quranic speech recognition using the Tarteel AI Everyayah Dataset. It is optimized to transcribe Quranic recitations with improved accuracy on this specialized dataset.

Training Details

Base model: openai/whisper-large-v3
Dataset: Tarteel AI Everyayah Dataset (language: Arabic, splits: train + validation)
Training steps: 5000
Batch size: 16
Learning rate: 1e-5
Gradient checkpointing: enabled
FP16 mixed precision: enabled

Loss and Metrics

Training loss decreased to near zero
Validation WER (Word Error Rate) improved steadily to ~48%

Known Issues / Notes

The training process showed a warning regarding use_cache=True being incompatible with gradient checkpointing, which was automatically handled by disabling use_cache.
Attention mask warnings appear when the pad token is the same as the EOS token; providing explicit attention masks is recommended for reliable inference.
This model is intended for Arabic Quranic speech only and may not perform well on other Arabic speech domains.

Intended Use

Automatic speech recognition (ASR) of Quranic recitations in Arabic.
Useful for Quranic audio transcription and research related to Islamic studies.
Not intended for general Arabic speech recognition or other languages.

Usage Example

python

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch
import librosa

model_name = "ijyad/whisper-large-v3-Tarteel"

processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)

# Load audio (replace with your audio file)
audio, rate = librosa.load("path_to_quran_audio.wav", sr=16000)
input_features = processor(audio, sampling_rate=rate, return_tensors="pt").input_features

# Generate transcription
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(transcription)

IJyad/whisper-large-v3-Tarteel

whisper-large-v3-Tarteel

Model Description

Training Details

Loss and Metrics

Known Issues / Notes

Intended Use

Usage Example

IJyad/
whisper-large-v3-Tarteel