readme: ---
  language: mr
  datasets:
  - openslr
  - interspeech_2021_asr
  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, InterSpeech 2021 ASR mr
        type: openslr, interspeech_2021_asr
      metrics:
         - name: Test WER
           type: wer
           value: 19.05
  ---
  
  # 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 and [InterSpeech 2021](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) Marathi datasets. Note that this data OpenSLR contains only female voices. Please keep this in mind before using the model for your task. 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 `text` and `audio_path` fields:
  
  ```python
  import torch
  import torchaudio
  import librosa
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  # test_data = #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-3")
  model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3")
  
  # 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["audio_path"])
      batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) # sampling_rate can vary
      return batch
  
  test_data= test_data.map(speech_file_to_array_fn)
  inputs = processor(test_data["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_data["text"][:2])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows on 10% of the Marathi data on OpenSLR.
  
  ```python
  import torch
  import torchaudio
  import librosa
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  # test_data = #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-3")
  model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3")
  model.to("cuda")
  
  chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]'
  
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      batch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower()
      speech_array, sampling_rate = torchaudio.load(batch["audio_path"])
      batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000)
      return batch
  
  test_data= test_data.map(speech_file_to_array_fn)
  
  # Preprocessing the datasets.
  # We need to read the audio 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_data.map(evaluate, batched=True, batch_size=8)
  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"])))
  ```
  
  **Test Result**: 19.05 % (157+157 examples)
   
  **Test Result on OpenSLR test**: 14.15 % (157 examples)
  
  **Test Results on InterSpeech test**: 27.14 % (157 examples)
  
  ## Training
  
  1412 examples of the OpenSLR Marathi dataset  and 1412 examples of InterSpeech 2021 Marathi ASR dataset were used for training. For testing, 157 examples from each were used.
  
  The colab notebook used for training and evaluation can be found [here](https://colab.research.google.com/drive/15fUhb4bUFFGJyNLr-_alvPxVX4w0YXRu?usp=sharing). 
  