Aradia-CTC-V1 Open-source Automatic Speech Recognition Model - Supports Accurate Recognition of Arabic Speech

Home

Aradia Ctc V1

Developed by abdusah

Automatic speech recognition model trained on a large-scale Arabic speech dataset

Speech Recognition

Transformers

#Arabic speech recognition #Large-scale dataset training #Low word error rate

Downloads 16

Release Time : 3/23/2022

Model Overview

This model is a CTC architecture automatic speech recognition model trained on a 300-hour large-scale Arabic speech dataset, primarily used for Arabic speech-to-text tasks.

Model Features

Arabic optimization

Specially optimized for Arabic speech characteristics

Large-scale training data

Trained using 300 hours of Arabic speech data

CTC architecture

Uses Connectionist Temporal Classification (CTC) architecture, suitable for speech recognition tasks

Model Capabilities

Arabic speech recognition

Speech-to-text

Use Cases

Speech transcription

Arabic meeting minutes

Convert Arabic meeting recordings into text transcripts

Word error rate 0.3336

Media content subtitle generation

Automatically generate subtitles for Arabic video content

🚀 aradia-ctc-v1

This model is a fine - tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1 on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It can achieve the following results on the evaluation set:

Loss: 0.7171
Wer: 0.3336

🚀 Quick Start

This model is designed for automatic speech recognition. You can use it based on the pre - trained model and fine - tune it according to your own needs.

📚 Documentation

Model description

This model is a fine - tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1 on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset.

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: 0.0003
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.22	100	5.1889	1.0
No log	0.43	200	3.1129	1.0
No log	0.65	300	3.0503	1.0
No log	0.87	400	3.0279	1.0
6.2756	1.09	500	2.9965	1.0
6.2756	1.3	600	2.3618	0.9993
6.2756	1.52	700	1.2715	0.8758
6.2756	1.74	800	0.9971	0.7156
6.2756	1.96	900	0.8927	0.6382
1.712	2.17	1000	0.8252	0.5926
1.712	2.39	1100	0.7794	0.5434
1.712	2.61	1200	0.7557	0.5092
1.712	2.83	1300	0.7347	0.5203
1.712	3.04	1400	0.7189	0.4929
0.9305	3.26	1500	0.6820	0.4595
0.9305	3.48	1600	0.6792	0.4504
0.9305	3.69	1700	0.6596	0.4442
0.9305	3.91	1800	0.6756	0.4432
0.9305	4.13	1900	0.6663	0.4392
0.737	4.35	2000	0.6479	0.4372
0.737	4.56	2100	0.6353	0.4203
0.737	4.78	2200	0.6251	0.4088
0.737	5.0	2300	0.6209	0.4177
0.737	5.22	2400	0.6639	0.4094
0.6247	5.43	2500	0.6408	0.3970
0.6247	5.65	2600	0.6373	0.3932
0.6247	5.87	2700	0.6411	0.3928
0.6247	6.09	2800	0.6378	0.3897
0.6247	6.3	2900	0.6396	0.3929
0.5443	6.52	3000	0.6544	0.3864
0.5443	6.74	3100	0.6218	0.3786
0.5443	6.96	3200	0.6200	0.3784
0.5443	7.17	3300	0.6157	0.3791
0.5443	7.39	3400	0.6317	0.3798
0.4845	7.61	3500	0.6540	0.3771
0.4845	7.83	3600	0.6436	0.3670
0.4845	8.04	3700	0.6335	0.3695
0.4845	8.26	3800	0.6579	0.3610
0.4845	8.48	3900	0.6170	0.3613
0.4279	8.69	4000	0.6523	0.3617
0.4279	8.91	4100	0.6349	0.3577
0.4279	9.13	4200	0.6344	0.3673
0.4279	9.35	4300	0.6215	0.3641
0.4279	9.56	4400	0.6513	0.3608
0.3825	9.78	4500	0.6386	0.3605
0.3825	10.0	4600	0.6724	0.3549
0.3825	10.22	4700	0.6776	0.3602
0.3825	10.43	4800	0.6739	0.3544
0.3825	10.65	4900	0.6688	0.3557
0.3477	10.87	5000	0.6674	0.3564
0.3477	11.09	5100	0.6786	0.3476
0.3477	11.3	5200	0.6818	0.3478
0.3477	11.52	5300	0.6874	0.3470
0.3477	11.74	5400	0.6993	0.3424
0.3101	11.96	5500	0.6950	0.3404
0.3101	12.17	5600	0.6872	0.3406
0.3101	12.39	5700	0.6846	0.3424
0.3101	12.61	5800	0.7051	0.3405
0.3101	12.83	5900	0.7051	0.3378
0.2859	13.04	6000	0.6955	0.3403
0.2859	13.26	6100	0.7115	0.3390
0.2859	13.48	6200	0.7074	0.3384
0.2859	13.69	6300	0.7002	0.3376
0.2859	13.91	6400	0.7171	0.3360
0.2714	14.13	6500	0.7193	0.3341
0.2714	14.35	6600	0.7132	0.3347
0.2714	14.56	6700	0.7184	0.3353
0.2714	14.78	6800	0.7171	0.3331

Framework versions

Transformers 4.18.0.dev0
Pytorch 1.10.2+cu113
Datasets 1.18.4
Tokenizers 0.11.6

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご