readme: ---
  language: ru
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Russian XLSR Wav2Vec2 Large 53 by Anton Lozhkov
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice ru
        type: common_voice
        args: ru
      metrics:
         - name: Test WER
           type: wer
           value: 17.39
  ---
  
  # Wav2Vec2-Large-XLSR-53-Russian
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Russian using the [Common Voice](https://huggingface.co/datasets/common_voice) 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
  
  test_dataset = load_dataset("common_voice", "ru", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
  model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # 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 the Russian test data of Common Voice.
  
  
  ```python
  import torch
  import torchaudio
  import urllib.request
  import tarfile
  import pandas as pd
  from tqdm.auto import tqdm
  from datasets import load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  # Download the raw data instead of using HF datasets to save disk space 
  data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ru.tar.gz"
  filestream = urllib.request.urlopen(data_url)
  data_file = tarfile.open(fileobj=filestream, mode="r|gz")
  data_file.extractall()
  
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
  model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
  model.to("cuda")
  
  cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/ru/test.tsv", sep='\t')
  clips_path = "cv-corpus-6.1-2020-12-11/ru/clips/"
  
  def clean_sentence(sent):
      sent = sent.lower()
      # these letters are considered equivalent in written Russian
      sent = sent.replace('ё', 'е')
      # replace non-alpha characters with space
      sent = "".join(ch if ch.isalpha() else " " for ch in sent)
      # remove repeated spaces
      sent = " ".join(sent.split())
      return sent
  
  targets = []
  preds = []
  
  for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
      row["sentence"] = clean_sentence(row["sentence"])
      speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
      resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
      row["speech"] = resampler(speech_array).squeeze().numpy()
  
      inputs = processor(row["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)
  
      targets.append(row["sentence"])
      preds.append(processor.batch_decode(pred_ids)[0])
  
  # free up some memory
  del model
  del processor
  del cv_test
  
  print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
  ```
  
  **Test Result**: 17.39 %  
  
  
  ## Training
  
  The Common Voice `train` and `validation` datasets were used for training.
  