readme: ---
  language: mn
  datasets:
  - common_voice
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Mongolian by Salim Shaikh
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice mn
        type: common_voice
        args: {mn}  
      metrics:
         - name: Test WER
           type: wer
           value: 38.14
  ---
  # Wav2Vec2-Large-XLSR-53-Mongolian
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Mongolian using the [Common Voice](https://huggingface.co/datasets/common_voice)
  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 torchaudio
  from datasets import load_dataset, load_metric
  from transformers import (
      Wav2Vec2ForCTC,
      Wav2Vec2Processor,
  )
  import torch
  import re
  import sys
  
  model_name = "sammy786/wav2vec2-large-xlsr-mongolian"
  device = "cuda"
  chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\)\\(\\*)]'
  
  model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
  processor = Wav2Vec2Processor.from_pretrained(model_name)
  
  ds = load_dataset("common_voice", "mn", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
  
  resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
  
  def map_to_array(batch):
      speech, _ = torchaudio.load(batch["path"])
      batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
      batch["sampling_rate"] = resampler.new_freq
      batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
      return batch
  
  ds = ds.map(map_to_array)
  
  def map_to_pred(batch):
      features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
      input_values = features.input_values.to(device)
      attention_mask = features.attention_mask.to(device)
      with torch.no_grad():
          logits = model(input_values, attention_mask=attention_mask).logits
      pred_ids = torch.argmax(logits, dim=-1)
      batch["predicted"] = processor.batch_decode(pred_ids)
      batch["target"] = batch["sentence"]
      return batch
  
  result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
  
  wer = load_metric("wer")
  print(wer.compute(predictions=result["predicted"], references=result["target"]))
  
  ```
  **Test Result**: 38.14 %
  