automatic-speech-recognition generated_from_trainer hf-asr-leaderboard ja mozilla-foundation/common_voice_8_0 robust-speech-event

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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:

On speech-recognition-community-v2/dev_data it achieved:

It achieves the following results on the evaluation set:

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:

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

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
  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 speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0