README.md

metadata

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
          - 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 is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.

Kanji are converted into Hiragana using the pykakasi 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

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

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