readme: ---
  language: Bengali
  datasets:
  - OpenSLR
  metrics:
  - wer
  tags:
  - bn
  - audio
  - automatic-speech-recognition
  - speech
  license: cc-by-sa-4.0
  model-index:
  - name: XLSR Wav2Vec2 Bengali by Arijit
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: OpenSLR
        type: OpenSLR
        args: ben
      metrics:
      - name: Test WER
        type: wer
        value: 32.45
  ---
  # Wav2Vec2-Large-XLSR-Bengali
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using a subset of 40,000 utterances from [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/). Tested WER using ~4200 held out from training.
  When using this model, make sure that your speech input is sampled at 16kHz.
  Train Script can be Found at : train.py 
      Data Prep Notebook : https://colab.research.google.com/drive/1JMlZPU-DrezXjZ2t7sOVqn7CJjZhdK2q?usp=sharing
      Inference Notebook : https://colab.research.google.com/drive/1uKC2cK9JfUPDTUHbrNdOYqKtNozhxqgZ?usp=sharing
  ## Usage
  
  The model can be used directly (without a language model) as follows:
  ```python
  import torch
  import torchaudio
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  processor = Wav2Vec2Processor.from_pretrained("arijitx/wav2vec2-large-xlsr-bengali")
  model = Wav2Vec2ForCTC.from_pretrained("arijitx/wav2vec2-large-xlsr-bengali")
  # model = model.to("cuda")
  resampler = torchaudio.transforms.Resample(TEST_AUDIO_SR, 16_000)
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = torchaudio.load(batch)
      speech =  resampler(speech_array).squeeze().numpy()
      return speech
  speech_array = speech_file_to_array_fn("test_file.wav")
  inputs = processor(speech_array, 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)
  preds = processor.batch_decode(predicted_ids)[0]
  print(preds.replace("[PAD]",""))
  ```
  **Test Result**: WER on ~4200 utterance : 32.45 %