readme: ---
  language: te
  datasets:
  - openslr
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Anurag Singh XLSR Wav2Vec2 Large 53 Telugu
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: OpenSLR te
        type: openslr
        args: te
      metrics:
         - name: Test WER
           type: wer
           value: 44.98
  ---
  # Wav2Vec2-Large-XLSR-53-Telugu
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Telugu using the [OpenSLR SLR66](http://openslr.org/66/) dataset.
  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:
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import pandas as pd
  # Evaluation notebook contains the procedure to download the data
  df = pd.read_csv("/content/te/test.tsv", sep="\t")
  df["path"] = "/content/te/clips/" + df["path"]
  test_dataset = Dataset.from_pandas(df)
  processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu")
  model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") 
  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["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
  ```python
  import torch
  import torchaudio
  from datasets import Dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  from sklearn.model_selection import train_test_split
  import pandas as pd
  # Evaluation notebook contains the procedure to download the data
  df = pd.read_csv("/content/te/test.tsv", sep="\t")
  df["path"] = "/content/te/clips/" + df["path"]
  test_dataset = Dataset.from_pandas(df)
  wer = load_metric("wer")
  processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu")
  model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") 
  model.to("cuda")
  chars_to_ignore_regex = '[\,\?\.\!\-\_\;\:\"\“\%\‘\”\।\’\'\&]'
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  def normalizer(text):
      # Use your custom normalizer
      text = text.replace("\\n","\n")
      text = ' '.join(text.split())
      text = re.sub(r'''([a-z]+)''','',text,flags=re.IGNORECASE)
      text = re.sub(r'''%'''," శాతం ", text)
      text = re.sub(r'''(/|-|_)'''," ", text)
      text = re.sub("ై","ై", text)
      text = text.strip()
      return text
  def speech_file_to_array_fn(batch):
      batch["sentence"] = normalizer(batch["sentence"])
      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**: 44.98%
  ## Training
  70% of the OpenSLR Telugu dataset was used for training.
  
  Train Split of annotations is [here](https://www.dropbox.com/s/xqc0wtour7f9h4c/train.tsv)
  
  Test Split of annotations is [here](https://www.dropbox.com/s/qw1uy63oj4qdiu4/test.tsv)
  
  Training Data Preparation notebook can be found [here](https://colab.research.google.com/drive/1_VR1QtY9qoiabyXBdJcOI29-xIKGdIzU?usp=sharing)
  
  Training notebook can be found[here](https://colab.research.google.com/drive/14N-j4m0Ng_oktPEBN5wiUhDDbyrKYt8I?usp=sharing)
  
  Evaluation notebook is [here](https://colab.research.google.com/drive/1SLEvbTWBwecIRTNqpQ0fFTqmr1-7MnSI?usp=sharing)