readme: ---
  language: lt
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Lithuanian by Enes Burak Dundar
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice lt
        type: common_voice
        args: lt
      metrics:
         - name: Test WER
           type: wer
           value: 35.87
  ---
  
  # Wav2Vec2-Large-XLSR-53-Lithuanian
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Lithuanian 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 torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  test_dataset = load_dataset("common_voice", "lt", split="test[:2%]") 
  
  processor = Wav2Vec2Processor.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian")
  model = Wav2Vec2ForCTC.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian")
  
  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
  
  The model can be evaluated as follows on the Lithuanian 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", "lt", split="test")
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian")
  model = Wav2Vec2ForCTC.from_pretrained("dundar/wav2vec2-large-xlsr-53-lithuanian")
  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=-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**: 35.87 %  
  
  
  ## Training
  
  The Common Voice datasets `except the test` set were used for training.
  
  The script used for training can be found [here](https://github.com/ebdundar/)