readme: ---
  license: apache-2.0
  language: tr
  tags:
  - automatic-speech-recognition
  - common_voice
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  - tr
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: mpoyraz/wav2vec2-xls-r-300m-cv8-turkish
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: tr
      metrics:
      - name: Test WER
        type: wer
        value: 10.61
      - name: Test CER
        type: cer
        value: 2.67
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: tr
      metrics:
      - name: Test WER
        type: wer
        value: 36.46
      - name: Test CER
        type: cer
        value: 12.38
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: tr
      metrics:
      - name: Test WER
        type: wer
        value: 40.91
  ---
  
  
  # wav2vec2-xls-r-300m-cv8-turkish
  
  ## Model description
  This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
  
  ## Training and evaluation data
  The following datasets were used for finetuning:
   - [Common Voice 8.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) All `validated` split except `test` split was used for training.
  
  ## Training procedure
  To support the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose.
  
  ### Training hyperparameters
  The following hypermaters were used for finetuning:
  - learning_rate 2.5e-4
  - num_train_epochs 20
  - warmup_steps 500
  - freeze_feature_extractor
  - mask_time_prob 0.1
  - mask_feature_prob 0.1
  - feat_proj_dropout 0.05
  - attention_dropout 0.05
  - final_dropout 0.1
  - activation_dropout 0.05
  - per_device_train_batch_size 8
  - per_device_eval_batch_size 8
  - gradient_accumulation_steps 8
  
  ### Framework versions
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.1
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  
  ## Language Model
  N-gram language model is trained 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.
  
  ## 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_8_0` with split `test`
  ```bash
  python eval.py --model_id mpoyraz/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 mpoyraz/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
  ```
  ## Evaluation results:
  
  | Dataset | WER | CER |
  |---|---|---|
  |Common Voice 8 TR test split| 10.61 | 2.67 |
  |Speech Recognition Community dev data| 36.46 | 12.38 |
  