readme: ---
  language:
  - pl
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  - xlsr-fine-tuning-week
  - hf-asr-leaderboard
  datasets:
  - common_voice
  model-index:
  - name: Polish comodoro Wav2Vec2 XLSR 300M CV8
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: pl
      metrics:
      - name: Test WER
        type: wer
        value: 17.0
      - name: Test CER
        type: cer
        value: 3.8
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: pl
      metrics:
      - name: Test WER
        type: wer
        value: 38.97
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: pl
      metrics:
      - name: Test WER
        type: wer
        value: 46.05
  ---
  # wav2vec2-xls-r-300m-pl-cv8
  
  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 8.0 dataset.
  It achieves the following results on the evaluation set while training:
  - Loss: 0.1716
  - Wer: 0.1697
  - Cer: 0.0385
  
  The `eval.py` script results are:
  WER: 0.16970531733661967
  CER: 0.03839135416519316
  
  ## Model description
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  
  The model can be used directly (without a language model) as follows:
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "pl", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
  model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def speech_file_to_array_fn(batch):
  	speech_array, sampling_rate = torchaudio.load(batch["path"])
  	batch["speech"] = resampler(speech_array).squeeze().numpy()
  	return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
  with torch.no_grad():
  	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
  
  predicted_ids = torch.argmax(logits, dim=-1)
  
  print("Prediction:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset[:2]["sentence"])
  ```
  
  ## Evaluation
  
  The model can be evaluated using the attached `eval.py` script:
  ```
  python eval.py --model_id comodoro/wav2vec2-xls-r-300m-pl-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config pl
  ```
  
  ## Training and evaluation data
  
  The Common Voice 8.0 `train` and `validation` datasets were used for training
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used:
  
  - learning_rate: 1e-4
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 1
  - total_train_batch_size: 640
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 150
  - mixed_precision_training: Native AMP
  
  The training was interrupted after 3250 steps.
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  