whisper-base-ar-quran Open-source Speech Recognition Model - Free Transcription of Arabic Quran Content

Whisper Base Ar Quran

Developed by tarteel-ai

This model is a fine-tuned version of openai/whisper-base on an Arabic Quran dataset, specializing in transcribing Quranic content in Arabic.

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Arabic Speech Recognition #Quran Audio Processing #Low Word Error Rate

Downloads 4,779

Release Time : 12/8/2022

Model Overview

whisper-base-ar-quran is a speech recognition model based on the Whisper architecture, specifically optimized for Arabic Quranic content. It accurately converts Arabic Quranic speech into text.

Model Features

Quran-Specific Optimization

Specially optimized for Arabic Quranic content, improving recognition accuracy for religious terms and Quran-specific pronunciations

Low Word Error Rate

Achieved a word error rate (WER) of 5.7544 on the evaluation set, demonstrating excellent performance

Multi-GPU Training

Utilized 8 GPUs for distributed training, enhancing training efficiency

Model Capabilities

Arabic Speech Recognition

Quranic Content Transcription

Real-time Speech-to-Text

Use Cases

Religious Applications

Quran Learning Aid

Assists learners in quickly obtaining Quranic text content through voice input

A 5.75% word error rate ensures high transcription accuracy

Religious Content Digitization

Automatically converts Quranic recitation recordings into digital text

Educational Applications

Arabic Learning Tool

Helps Arabic learners check pronunciation accuracy

🚀 whisper-base-ar-quran

This model is a fine - tuned version of openai/whisper-base on the None dataset. It offers a practical solution for speech - related tasks by leveraging the pre - trained capabilities of the base model and fine - tuning them for specific needs.

🚀 Quick Start

This model is a fine - tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0839
Wer: 5.7544

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi - GPU
num_devices: 8
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1092	0.05	250	0.1969	13.3890
0.0361	0.1	500	0.1583	10.6375
0.0192	0.15	750	0.1109	8.8468
0.0144	0.2	1000	0.1157	7.9754
0.008	0.25	1250	0.1000	7.5360
0.0048	1.03	1500	0.0933	6.8227
0.0113	1.08	1750	0.0955	6.9638
0.0209	1.13	2000	0.0824	6.3586
0.0043	1.18	2250	0.0830	6.3444
0.002	1.23	2500	0.1015	6.3025
0.0013	2.01	2750	0.0863	6.0639
0.0014	2.06	3000	0.0905	6.0213
0.0018	2.11	3250	0.0864	6.0293
0.0008	2.16	3500	0.0887	5.9308
0.0029	2.21	3750	0.0777	5.9159
0.0022	2.26	4000	0.0847	5.8749
0.0005	3.05	4250	0.0827	5.8352
0.0003	3.1	4500	0.0826	5.7800
0.0006	3.15	4750	0.0833	5.7625
0.0003	3.2	5000	0.0839	5.7544

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2

📄 License

This model is released under the apache - 2.0 license.

Property	Details
Model Type	whisper - base - ar - quran
Metrics	wer
License	apache - 2.0
Generated From	Trainer

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