readme: ---
  language:
  - ja
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - ja
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M - Japanese
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: ja
      metrics:
      - name: Test WER
        type: wer
        value: 54.05
      - name: Test CER
        type: cer
        value: 27.54
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: ja
      metrics:
      - name: Validation WER
        type: wer
        value: 48.77
      - name: Validation CER
        type: cer
        value: 24.87
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: ja
      metrics:
      - name: Test CER
        type: cer
        value: 27.36
  ---
  
  # 
  
  This model is for transcribing audio into Hiragana, one format of Japanese language.
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `mozilla-foundation/common_voice_8_0 dataset`. Note that the following results are achieved by:
  - Modify `eval.py` to suit the use case.
  - Since kanji and katakana shares the same sound as hiragana, we convert all texts to hiragana using [pykakasi](https://pykakasi.readthedocs.io) and tokenize them using [fugashi](https://github.com/polm/fugashi).
  
  It achieves the following results on the evaluation set:
  - Loss: 0.7751
  - Cer: 0.2227
  
  # Evaluation results (Running ./eval.py):
  
  | Model    | Metric | Common-Voice-8/test | speech-recognition-community-v2/dev-data   |
  |:--------:|:------:|:-------------------:|:------------------------------------------:|
  | w/o LM   | WER    | 0.5964              | 0.5532                                     |
  |          | CER    | 0.2944              | 0.2629                                     |
  | w/  LM   | WER    | 0.5405              | 0.4877                                     |
  |          | CER    | **0.2754**              | **0.2487**                                     |
  
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - training_steps: 4000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Cer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.4081        | 1.6   | 500   | 4.0983          | 1.0    |
  | 3.303         | 3.19  | 1000  | 3.3563          | 1.0    |
  | 3.1538        | 4.79  | 1500  | 3.2066          | 0.9239 |
  | 2.1526        | 6.39  | 2000  | 1.1597          | 0.3355 |
  | 1.8726        | 7.98  | 2500  | 0.9023          | 0.2505 |
  | 1.7817        | 9.58  | 3000  | 0.8219          | 0.2334 |
  | 1.7488        | 11.18 | 3500  | 0.7915          | 0.2222 |
  | 1.7039        | 12.78 | 4000  | 0.7751          | 0.2227 |
  | Stop & Train  |       |       |                 |        |
  | 1.6571        | 15.97 | 5000  | 0.6788          | 0.1685 |
  | 1.520400      | 19.16 | 6000  | 0.6095          | 0.1409 |
  | 1.448200      | 22.35 | 7000  | 0.5843          | 0.1430 |
  | 1.385400      | 25.54 | 8000  | 0.5699          | 0.1263 |
  | 1.354200      | 28.73 | 9000  | 0.5686          | 0.1219 |
  | 1.331500      | 31.92 | 10000 | 0.5502          | 0.1144 |
  | 1.290800      | 35.11 | 11000 | 0.5371          | 0.1140 |
  | Stop & Train  |       |       |                 |        |
  | 1.235200      | 38.30 | 12000 | 0.5394          | 0.1106 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  