readme: ---
  language: mr
  datasets:
  - openslr
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Large 53 Marathi by Gunjan Chhablani
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: OpenSLR mr
        type: openslr
      metrics:
         - name: Test WER
           type: wer
           value: 14.53
  ---
  
  # Wav2Vec2-Large-XLSR-53-Marathi
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, although it works very well for male voice too. When using this model, make sure that your speech input is sampled at 16kHz. 
  
  ## Usage
  
  The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi `sentence` and `path` fields:
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.
  
  processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")
  model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
  
  # Preprocessing the datasets.
  # We need to read the audio 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["speech"][:2], 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["sentence"][:2])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows on 10% of the Marathi data on OpenSLR.
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.
  
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")
  model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")
  model.to("cuda")
  
  chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]'
  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):
      batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
      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)
  
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def evaluate(batch):
      inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
      with torch.no_grad():
          logits = model(inputs.input_values.to("cuda"), 
          attention_mask=inputs.attention_mask.to("cuda")).logits
          pred_ids = torch.argmax(logits, dim=-1)
          batch["pred_strings"] = processor.batch_decode(pred_ids)
          return batch
  
  result = test_dataset.map(evaluate, batched=True, batch_size=8)
  
  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
  ```
  
  **Test Result**: 14.53 %  
  
  ## Training
  
  90% of the OpenSLR Marathi dataset was used for training.
  The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1_BbLyLqDUsXG3RpSULfLRjC6UY3RjwME?usp=sharing). 
  