Kalemat-Tech Arabic Speech Recognition Model (STT)

نموذج كلماتك للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص

This model is a fine-tuned version of openai/whisper-small on Common_Voice_Arabic_12.0_Augmented. It achieves the following results on the evaluation set:

Loss: 0.5362
Wer: 58.5848

You can find the model here:

KalemaTech-Arabic-STT-ASR

Read Model documentation

Example of usage:

from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("Salama1429/KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small")

model = AutoModelForSpeechSeq2Seq.from_pretrained("Salama1429/KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small")

• Or just clone the model repo

git lfs install
git clone https://huggingface.co/Salama1429/KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small

- Intended uses & limitations

Automatic Speech Recognition

- Training and evaluation data

Common_Voice_Arabic_12.0 and I made some augmentations to it as follows:

- 25% of the data TimeMasking
- 25% of the data SpecAugmentation
- 25% of the data WavAugmentation (AddGaussianNoise)
- The final dataset is the original common voice plus the augmented files

- Training procedure

Training hyperparameters The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 64
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 25
mixed_precision_training: Native AMP

- Training results:

+---------------+-------+-------+-----------------+---------+
| Training Loss | Epoch | Step  | Validation Loss |   Wer   |
+---------------+-------+-------+-----------------+---------+
|        0.2728 |  1.01 |  1000 |          0.3063 | 60.4733 |
|        0.1442 |  2.01 |  2000 |          0.2878 | 55.6935 |
|        0.0648 |  3.02 |  3000 |          0.3009 | 59.2568 |
|        0.0318 |  4.03 |  4000 |          0.3278 | 59.2993 |
|        0.0148 |  5.04 |  5000 |          0.3539 | 61.0364 |
|        0.0088 |  6.04 |  6000 |          0.3714 | 56.9154 |
|        0.0061 |  7.05 |  7000 |          0.3920 | 57.5515 |
|        0.0041 |  8.06 |  8000 |          0.4149 | 61.6328 |
|        0.0033 |  9.06 |  9000 |          0.4217 | 58.0310 |
|        0.0033 | 10.07 | 10000 |          0.4376 | 59.9594 |
|        0.0021 | 11.08 | 11000 |          0.4485 | 56.7812 |
|        0.0015 | 12.08 | 12000 |          0.4577 | 57.6936 |
|        0.0013 | 13.09 | 13000 |          0.4671 | 60.6606 |
|        0.0011 |  14.1 | 14000 |          0.4686 | 59.8159 |
|        0.0008 | 15.11 | 15000 |          0.4856 | 60.7111 |
|        0.0011 | 16.11 | 16000 |          0.4851 | 59.5198 |
|        0.0005 | 17.12 | 17000 |          0.4936 | 59.2608 |
|        0.0004 | 18.13 | 18000 |          0.4995 | 57.9619 |
|        0.0003 | 19.13 | 19000 |          0.5085 | 58.3630 |
|        0.0002 | 20.14 | 20000 |          0.5155 | 58.0987 |
|        0.0001 | 21.15 | 21000 |          0.5251 | 58.8504 |
|        0.0001 | 22.16 | 22000 |          0.5268 | 58.4228 |
|        0.0001 | 23.16 | 23000 |          0.5317 | 59.0881 |
|        0.0001 | 24.17 | 24000 |          0.5362 | 58.5848 |
+---------------+-------+-------+-----------------+---------+

- Framework versions:

Transformers 4.25.1
Pytorch 1.13.1+cu117
Datasets 2.8.0
Tokenizers 0.13.2