readme: ---
  language:
  - tr
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  - tr
  datasets:
  - common_voice
  model-index:
  - name: ''
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4164
  - Wer: 0.3098
  - Cer: 0.0764
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Language Model
  N-gram language model is trained by [mpoyraz](https://huggingface.co/mpoyraz/wav2vec2-xls-r-300m-cv7-turkish) on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0005
  - train_batch_size: 64
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 0.6356        | 9.09  | 500  | 0.5055          | 0.5536 | 0.1381 |
  | 0.3847        | 18.18 | 1000 | 0.4002          | 0.4247 | 0.1065 |
  | 0.3377        | 27.27 | 1500 | 0.4193          | 0.4167 | 0.1078 |
  | 0.2175        | 36.36 | 2000 | 0.4351          | 0.3861 | 0.0974 |
  | 0.2074        | 45.45 | 2500 | 0.3962          | 0.3622 | 0.0916 |
  | 0.159         | 54.55 | 3000 | 0.4062          | 0.3526 | 0.0888 |
  | 0.1882        | 63.64 | 3500 | 0.3991          | 0.3445 | 0.0850 |
  | 0.1766        | 72.73 | 4000 | 0.4214          | 0.3396 | 0.0847 |
  | 0.116         | 81.82 | 4500 | 0.4182          | 0.3265 | 0.0812 |
  | 0.0718        | 90.91 | 5000 | 0.4259          | 0.3191 | 0.0781 |
  | 0.019         | 100.0 | 5500 | 0.4164          | 0.3098 | 0.0764 |
  
  
  ## Evaluation Commands
  Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing.
  1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
  ```bash
  python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset mozilla-foundation/common_voice_8_0 --config tr --split test
  ```
  
  2. To evaluate on `speech-recognition-community-v2/dev_data`
  
  ```bash
  python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
  ```
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  