readme: ---
  language:
  - ar
  license: apache-2.0
  tags:
  - ar
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_7_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  model-index:
  - name: XLS-R-300M - Arabic
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: ar
      metrics:
      - name: Test WER
        type: wer
        value: NA
      - name: Test CER
        type: cer
        value: NA
    - 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: NA
      - name: Test CER
        type: cer
        value: NA
  ---
  
  <!-- 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. -->
  
  # XLS-R-300m-SV
  
  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_7_0 - AR dataset.
  It achieves the following results on the evaluation set:
  - Loss: NA
  - Wer: NA
  
  ## 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: 7.5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.0+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.10.3
  
  #### Evaluation Commands
  
  1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
  
  ```bash
  python eval.py \
      --model_id infinitejoy/wav2vec2-large-xls-r-300m-arabic \
      --dataset mozilla-foundation/common_voice_7_0 --config ar --split test --log_outputs
  ```
  
  2. To evaluate on `speech-recognition-community-v2/dev_data`
  
  ```bash
  python eval.py \
      --model_id infinitejoy/wav2vec2-large-xls-r-300m-arabic --dataset speech-recognition-community-v2/dev_data \
      --config ar --split validation --chunk_length_s 10 --stride_length_s 1
  ```
  
  ### Inference With LM
  
  ```python
  import torch
  from datasets import load_dataset
  from transformers import AutoModelForCTC, AutoProcessor
  import torchaudio.functional as F
  
  
  model_id = "infinitejoy/wav2vec2-large-xls-r-300m-arabic"
  
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "ar", split="test", streaming=True, use_auth_token=True))
  
  sample = next(sample_iter)
  resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
  
  model = AutoModelForCTC.from_pretrained(model_id)
  processor = AutoProcessor.from_pretrained(model_id)
  
  input_values = processor(resampled_audio, return_tensors="pt").input_values
  
  with torch.no_grad():
      logits = model(input_values).logits
  
  transcription = processor.batch_decode(logits.numpy()).text
  
  ```
  
  ### Eval results on Common Voice 7 "test" (WER):
  
  | Without LM | With LM (run `./eval.py`) |
  |---|---|
  | NA | NA |
  
  