readme: ---
  language:
  - ar
  - multilingual
  
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - whisper-event
  - generated_from_trainer
  - Arabic
  - multilingual
  - STT
  datasets:
  - mozilla-foundation/common_voice_12_0
  metrics:
  - wer
  model-index:
  
  - name: Kalemat-Tech Arabic Speech Recognition Model (STT)
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_12_0
        name: mozilla-foundation/common_voice_12_0
        config: ar
        split: test
        args: ar
      metrics:
      - type: wer
        value: 58.5848
        name: wer
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  # Kalemat-Tech Arabic Speech Recognition Model (STT) - Mohamed Salama
  # نموذج كلماتك للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص
  
  # KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/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
  
  ## 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")
  ```
  ## 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
  