readme: ---
  language: tr
  datasets:
  - common_voice 
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Wav2Vec2-Large-XLSR-53-Turkish
  results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice tr
        type: common_voice
        args: tr
      metrics:
         - name: Test WER
           type: wer
           value: 17.46
  ---
  
  # Wav2Vec2-Large-XLSR-53-Turkish
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish 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
  from unicode_tr import unicode_tr
  
  test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("aniltrkkn/wav2vec2-large-xlsr-53-turkish")
  model = Wav2Vec2ForCTC.from_pretrained("aniltrkkn/wav2vec2-large-xlsr-53-turkish")
  
  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):
  \tspeech_array, sampling_rate = torchaudio.load(batch["path"])
  \tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  \treturn 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():
  \tlogits = 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 Turkish 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", "tr", split="test")
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("aniltrkkn/wav2vec2-large-xlsr-53-turkish")
  model = Wav2Vec2ForCTC.from_pretrained("aniltrkkn/wav2vec2-large-xlsr-53-turkish")
  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):
  \tbatch["sentence"] = str(unicode_tr(re.sub(chars_to_ignore_regex, "", batch["sentence"])).lower())
  \tspeech_array, sampling_rate = torchaudio.load(batch["path"])
  \tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  \treturn 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):
  \tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
  \twith torch.no_grad():
  \t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
  
  \tpred_ids = torch.argmax(logits, dim=-1)
  \tbatch["pred_strings"] = processor.batch_decode(pred_ids)
  \treturn 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**: 17.46 %
  
  ## Training
  unicode_tr package is used for converting sentences to lower case since regular lower() does not work well with Turkish.
  
  Since training data is very limited for Turkish, all data is employed with a K-Fold (k=5) training approach. Best model out of the 5 trainings is uploaded. Training arguments:
      --num_train_epochs="30" \\
      --per_device_train_batch_size="32" \\
      --evaluation_strategy="steps" \\
      --activation_dropout="0.055" \\
      --attention_dropout="0.094" \\
      --feat_proj_dropout="0.04" \\
      --hidden_dropout="0.047" \\
      --layerdrop="0.041" \\
      --learning_rate="2.34e-4" \\
      --mask_time_prob="0.082" \\
      --warmup_steps="250" \\
  
  All trainings took ~20 hours with a GeForce RTX 3090 Graphics Card.