readme: ---
  language:
  - ur
  license: apache-2.0
  tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-large-xls-r-300m-Urdu
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice 8
        args: ur
      metrics:
      - type: wer
        value: 39.89
        name: Test WER
      - name: Test CER
        type: cer
        value: 16.7
  ---
  ---
  
  <!-- 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. -->
  
  # wav2vec2-large-xls-r-300m-Urdu
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9889
  - Wer: 0.5607
  - Cer: 0.2370
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-300m-Urdu --dataset mozilla-foundation/common_voice_8_0 --config ur --split test
  ```
  
  
  ### Inference With LM
  
  ```python
  from datasets import load_dataset, Audio
  from transformers import pipeline
  model = "kingabzpro/wav2vec2-large-xls-r-300m-Urdu"
  data = load_dataset("mozilla-foundation/common_voice_8_0",
                       "ur",
                       split="test", 
                       streaming=True, 
                       use_auth_token=True)
  
  sample_iter = iter(data.cast_column("path", 
                      Audio(sampling_rate=16_000)))
  sample = next(sample_iter)
  
  asr = pipeline("automatic-speech-recognition", model=model)
  prediction = asr(sample["path"]["array"], 
                    chunk_length_s=5, 
                    stride_length_s=1)
  prediction
  # => {'text': 'اب یہ ونگین لمحاتانکھار دلمیں میںفوث کریلیا اجائ'}
  ```
  
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 8
  - 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: 1000
  - num_epochs: 200
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
  | 3.6398        | 30.77  | 400  | 3.3517          | 1.0    | 1.0    |
  | 2.9225        | 61.54  | 800  | 2.5123          | 1.0    | 0.8310 |
  | 1.2568        | 92.31  | 1200 | 0.9699          | 0.6273 | 0.2575 |
  | 0.8974        | 123.08 | 1600 | 0.9715          | 0.5888 | 0.2457 |
  | 0.7151        | 153.85 | 2000 | 0.9984          | 0.5588 | 0.2353 |
  | 0.6416        | 184.62 | 2400 | 0.9889          | 0.5607 | 0.2370 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  
  ### Eval results on Common Voice 8 "test" (WER):
  
  | Without LM | With LM (run `./eval.py`) |
  |---|---|
  | 52.03 | 39.89 |
  