---
language: mn
datasets:
- common_voice mn
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-large-xlsr-mongolian-v1
  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: 35.42
---

# Wav2Vec2-Large-XLSR-53-Mongolian-v1

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on 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.

## Evaluation

The model can be evaluated as follows on the Mongolian test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "mn", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("bayartsogt/wav2vec2-large-xlsr-mongolian-v1")
model = Wav2Vec2ForCTC.from_pretrained("bayartsogt/wav2vec2-large-xlsr-mongolian-v1")
model.to("cuda")

chars_to_ignore_regex = '[\\\\!\\\\"\\\\'\\\\,\\\\.\\\\«\\\\»\\\\?\\\\-]'
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["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=-
     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**: 35.42 % 


## Training

The Common Voice `train` dataset was used for training as well as ... and ...