readme: ---
  language:
  - ar
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  - cer
  model-index:
  - name: Sinai Voice Arabic Speech Recognition Model
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice ar
        args: ar
      metrics:
      - type: wer
        value: 0.181
        name: Test WER
      - type: cer
        value: 0.049
        name: Test CER
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: ar
      metrics:
      - name: Test WER
        type: wer
        value: 93.03
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: ar
      metrics:
      - name: Test WER
        type: wer
        value: 90.79
  widget:
  - example_title: Example 1
    src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19077324.mp3
  - example_title: Example 2
    src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19205138.mp3
  - example_title: Example 3
    src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19331711.mp3
  ---
  <!-- 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. -->
  
  # Sinai Voice Arabic Speech Recognition Model
  
  # نموذج **صوت سيناء** للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2141
  - Wer: 0.1808
  
  It achieves the following results on the evaluation set:
  - eval_loss               =     0.2141
  - eval_samples            =      10388
  - eval_wer                =     0.181
  - eval_cer                =     0.049
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id bakrianoo/sinai-voice-ar-stt --dataset mozilla-foundation/common_voice_8_0 --config ar --split test
  ```
  
  
  ### Inference Without LM
  
  ```python
  from transformers import (Wav2Vec2Processor, Wav2Vec2ForCTC)
  import torchaudio
  import torch
  
  def speech_file_to_array_fn(voice_path, resampling_to=16000):
      speech_array, sampling_rate = torchaudio.load(voice_path)
      resampler = torchaudio.transforms.Resample(sampling_rate, resampling_to)
      
      return resampler(speech_array)[0].numpy(), sampling_rate
  
  # load the model
  cp = "bakrianoo/sinai-voice-ar-stt"
  processor = Wav2Vec2Processor.from_pretrained(cp)
  model = Wav2Vec2ForCTC.from_pretrained(cp)
  
  # recognize the text in a sample sound file
  sound_path = './my_voice.mp3'
  
  sample, sr = speech_file_to_array_fn(sound_path)
  inputs = processor([sample], sampling_rate=16_000, return_tensors="pt", padding=True)
  
  with torch.no_grad():
      logits = model(inputs.input_values,).logits
  
  predicted_ids = torch.argmax(logits, dim=-1)
  
  print("Prediction:", processor.batch_decode(predicted_ids))
  ```
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0002
  - train_batch_size: 32
  - eval_batch_size: 10
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 256
  - total_eval_batch_size: 80
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.354         | 0.64  | 1000  | 0.4109          | 0.4493 |
  | 0.5886        | 1.28  | 2000  | 0.2798          | 0.3099 |
  | 0.4977        | 1.92  | 3000  | 0.2387          | 0.2673 |
  | 0.4253        | 2.56  | 4000  | 0.2266          | 0.2523 |
  | 0.3942        | 3.2   | 5000  | 0.2171          | 0.2437 |
  | 0.3619        | 3.84  | 6000  | 0.2076          | 0.2253 |
  | 0.3245        | 4.48  | 7000  | 0.2088          | 0.2186 |
  | 0.308         | 5.12  | 8000  | 0.2086          | 0.2206 |
  | 0.2881        | 5.76  | 9000  | 0.2089          | 0.2105 |
  | 0.2557        | 6.4   | 10000 | 0.2015          | 0.2004 |
  | 0.248         | 7.04  | 11000 | 0.2044          | 0.1953 |
  | 0.2251        | 7.68  | 12000 | 0.2058          | 0.1932 |
  | 0.2052        | 8.32  | 13000 | 0.2117          | 0.1878 |
  | 0.1976        | 8.96  | 14000 | 0.2104          | 0.1825 |
  | 0.1845        | 9.6   | 1