readme: ---
  language: Bengali
  datasets:
  - OpenSLR
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: cc-by-sa-4.0
  model-index:
  - name: XLSR Wav2Vec2 Bengali by Tanmoy Sarkar
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: OpenSLR
        type: OpenSLR
        args: ben
      metrics:
      - name: Test WER
        type: wer
        value: 88.58
  ---
  # Wav2Vec2-Large-XLSR-Bengali
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using the [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/).
  When using this model, make sure that your speech input is sampled at 16kHz.
  ## Usage
  Dataset must be downloaded from [this website](https://www.openslr.org/53/) and preprocessed accordingly. For example 1250 test samples has been chosen.
  ```python
  import pandas as pd
  test_dataset = pd.read_csv('utt_spk_text.tsv', sep='\\t', header=None)[60000:61250]
  test_dataset.columns = ["audio_path", "__", "label"]
  test_dataset = test_data.drop("__", axis=1)
  def add_file_path(text):
    path = "data/" + text[:2] + "/" + text + '.flac'
    return path
  test_dataset['audio_path'] = test_dataset['audio_path'].map(lambda x: add_file_path(x))
  ```
  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("tanmoyio/wav2vec2-large-xlsr-bengali")
  model = Wav2Vec2ForCTC.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
  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["audio_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["label"][:2])
  ```
  ## Evaluation
  The model can be evaluated as follows on the Bengali test data of OpenSLR.  
  ```python
  import torch
  import torchaudio
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  wer = load_metric("wer")
  processor = Wav2Vec2Processor.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
  model = Wav2Vec2ForCTC.from_pretrained("tanmoyio/wav2vec2-large-xlsr-bengali")
  model.to("cuda")
  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["label"]).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**: 88.58 %
  ## Training
  The script used for training can be found [Bengali ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1Bkc5C_cJV9BeS0FD0MuHyayl8hqcbdRZ?usp=sharing)
  