readme: ---
  language: tr
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Turkish by Davut Emre TASAR
    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
           
  ---
  
  # wav2vec-tr-lite-AG
  
  ## 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", "tr", split="test[:2%]") 
  
  processor = Wav2Vec2Processor.from_pretrained("emre/wav2vec-tr-lite-AG")
  model = Wav2Vec2ForCTC.from_pretrained("emre/wav2vec-tr-lite-AG")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  ### Training hyperparameters
  The following hyperparameters were used during training:
  - learning_rate: 0.00005
  - train_batch_size: 2
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 32
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30.0
  - mixed_precision_training: Native AMP
  ### Training results
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.4388        | 3.7   | 400  | 1.366          | 0.9701 |
  | 0.3766        | 7.4   | 800  | 0.4914          | 0.5374 |
  | 0.2295        | 11.11 | 1200 | 0.3934          | 0.4125 |
  | 0.1121        | 14.81 | 1600 | 0.3264          | 0.2904 |
  | 0.1473        | 18.51 | 2000 | 0.3103          | 0.2671 |
  | 0.1013        | 22.22 | 2400 | 0.2589          | 0.2324 |
  | 0.0704        | 25.92 | 2800 | 0.2826          | 0.2339 |
  | 0.0537        | 29.63 | 3200 | 0.2704          | 0.2309 |
  ### Framework versions
  - Transformers 4.12.0.dev0
  - Pytorch 1.8.1
  - Datasets 1.14.1.dev0
  - Tokenizers 0.10.3
  
  