This model is a fine-tuned version of 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 and tokenize them using 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

vitouphy/wav2vec2-xls-r-300m-japanese

Evaluation results (Running ./eval.py):

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vitouphy/
wav2vec2-xls-r-300m-japanese