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-300-m
    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: 95.82
      - name: Test CER
        type: cer
        value: 23.64
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: de
      metrics:
      - name: Test WER
        type: wer
        value: 100.0
      - name: Test CER
        type: cer
        value: 30.99
    - 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: Test CER
        type: cer
        value: 30.37
    - 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: 34.42
  ---
  
  <!-- 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 MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.
  
  Kanji are converted into Hiragana using the [pykakasi](https://pykakasi.readthedocs.io/en/latest/index.html) library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not a suitable metric for evaluating performance and CER is more suitable.
  
  On mozilla-foundation/common_voice_8_0 it achieved:
  - cer: 23.64%
  
  On speech-recognition-community-v2/dev_data it achieved:
  - cer: 30.99%
  
  It achieves the following results on the evaluation set:
  - Loss: 0.5212
  - Wer: 1.3068
  
  ## 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: 7.5e-05
  - train_batch_size: 48
  - 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: 2000
  - num_epochs: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.0974        | 4.72  | 1000  | 4.0178          | 1.9535 |
  | 2.1276        | 9.43  | 2000  | 0.9301          | 1.2128 |
  | 1.7622        | 14.15 | 3000  | 0.7103          | 1.5527 |
  | 1.6397        | 18.87 | 4000  | 0.6729          | 1.4269 |
  | 1.5468        | 23.58 | 5000  | 0.6087          | 1.2497 |
  | 1.4885        | 28.3  | 6000  | 0.5786          | 1.3222 |
  | 1.451         | 33.02 | 7000  | 0.5726          | 1.3768 |
  | 1.3912        | 37.74 | 8000  | 0.5518          | 1.2497 |
  | 1.3617        | 42.45 | 9000  | 0.5352          | 1.2694 |
  | 1.3113        | 47.17 | 10000 | 0.5228          | 1.2781 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.dev0
  - Tokenizers 0.11.0
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs
  ```
  
  2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
  ```