Wav2Vec2 XLS-R 300M Korean LM
Wav2Vec2 XLS-R 300M Korean LM is an automatic speech recognition model based on the XLS-R architecture. This model is a fine-tuned version of Wav2Vec2-XLS-R-300M on the Zeroth Korean dataset. A 5-gram Language model, trained on the Korean subset of Open Subtitles, was then subsequently added to this model.
This model was trained using HuggingFace's PyTorch framework and is part of the Robust Speech Challenge Event organized by HuggingFace. All training was done on a Tesla V100, sponsored by OVH.
All necessary scripts used for training could be found in the Files and versions tab, as well as the Training metrics logged via Tensorboard.
As for the N-gram language model training, we followed the blog post tutorial provided by HuggingFace.
Model
Model | #params | Arch. | Training/Validation data (text) |
---|---|---|---|
wav2vec2-xls-r-300m-korean-lm |
300M | XLS-R | Zeroth Korean Dataset |
Evaluation Results
The model achieves the following results on evaluation without a language model:
Dataset | WER | CER |
---|---|---|
Zeroth Korean |
29.54% | 9.53% |
Robust Speech Event - Dev Data |
76.26% | 38.67% |
With the addition of the language model, it achieves the following results:
Dataset | WER | CER |
---|---|---|
Zeroth Korean |
30.94% | 7.97% |
Robust Speech Event - Dev Data |
68.34% | 37.08% |
Training procedure
The training process did not involve the addition of a language model. The following results were simply lifted from the original automatic speech recognition model training.
Training hyperparameters
The following hyperparameters were used during training:
learning_rate
: 7.5e-05train_batch_size
: 8eval_batch_size
: 8seed
: 42gradient_accumulation_steps
: 4total_train_batch_size
: 32optimizer
: Adam withbetas=(0.9, 0.999)
andepsilon=1e-08
lr_scheduler_type
: linearlr_scheduler_warmup_steps
: 2000num_epochs
: 50.0mixed_precision_training
: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
---|---|---|---|---|---|
19.7138 | 0.72 | 500 | 19.6427 | 1.0 | 1.0 |
4.8039 | 1.44 | 1000 | 4.7842 | 1.0 | 1.0 |
4.5619 | 2.16 | 1500 | 4.5608 | 0.9992 | 0.9598 |
4.254 | 2.88 | 2000 | 4.2729 | 0.9955 | 0.9063 |
4.1905 | 3.6 | 2500 | 4.2257 | 0.9903 | 0.8758 |
4.0683 | 4.32 | 3000 | 3.9294 | 0.9937 | 0.7911 |
3.486 | 5.04 | 3500 | 2.7045 | 1.0012 | 0.5934 |
2.946 | 5.75 | 4000 | 1.9691 | 0.9425 | 0.4634 |
2.634 | 6.47 | 4500 | 1.5212 | 0.8807 | 0.3850 |
2.4066 | 7.19 | 5000 | 1.2551 | 0.8177 | 0.3601 |
2.2651 | 7.91 | 5500 | 1.0423 | 0.7650 | 0.3039 |
2.1828 | 8.63 | 6000 | 0.9599 | 0.7273 | 0.3106 |
2.1023 | 9.35 | 6500 | 0.9482 | 0.7161 | 0.3063 |
2.0536 | 10.07 | 7000 | 0.8242 | 0.6767 | 0.2860 |
1.9803 | 10.79 | 7500 | 0.7643 | 0.6563 | 0.2637 |
1.9468 | 11.51 | 8000 | 0.7319 | 0.6441 | 0.2505 |
1.9178 | 12.23 | 8500 | 0.6937 | 0.6320 | 0.2489 |
1.8515 | 12.95 | 9000 | 0.6443 | 0.6053 | 0.2196 |
1.8083 | 13.67 | 9500 | 0.6286 | 0.6122 | 0.2148 |
1.819 | 14.39 | 10000 | 0.6015 | 0.5986 | 0.2074 |
1.7684 | 15.11 | 10500 | 0.5682 | 0.5741 | 0.1982 |
1.7195 | 15.83 | 11000 | 0.5385 | 0.5592 | 0.2007 |
1.7044 | 16.55 | 11500 | 0.5362 | 0.5524 | 0.2097 |
1.6879 | 17.27 | 12000 | 0.5119 | 0.5489 | 0.2083 |
1.656 | 17.98 | 12500 | 0.4990 | 0.5362 | 0.1968 |
1.6122 | 18.7 | 13000 | 0.4561 | 0.5092 | 0.1900 |
1.5919 | 19.42 | 13500 | 0.4778 | 0.5225 | 0.1975 |
1.5896 | 20.14 | 14000 | 0.4563 | 0.5098 | 0.1859 |
1.5589 | 20.86 | 14500 | 0.4362 | 0.4940 | 0.1725 |
1.5353 | 21.58 | 15000 | 0.4140 | 0.4826 | 0.1580 |
1.5441 | 22.3 | 15500 | 0.4031 | 0.4742 | 0.1550 |
1.5116 | 23.02 | 16000 | 0.3916 | 0.4748 | 0.1545 |
1.4731 | 23.74 | 16500 | 0.3841 | 0.4810 | 0.1542 |
1.4647 | 24.46 | 17000 | 0.3752 | 0.4524 | 0.1475 |
1.4328 | 25.18 | 17500 | 0.3587 | 0.4476 | 0.1461 |
1.4129 | 25.9 | 18000 | 0.3429 | 0.4242 | 0.1366 |
1.4062 | 26.62 | 18500 | 0.3450 | 0.4251 | 0.1355 |
1.3928 | 27.34 | 19000 | 0.3297 | 0.4145 | 0.1322 |
1.3906 | 28.06 | 19500 | 0.3210 | 0.4185 | 0.1336 |
1.358 | 28.78 | 20000 | 0.3131 | 0.3970 | 0.1275 |
1.3445 | 29.5 | 20500 | 0.3069 | 0.3920 | 0.1276 |
1.3159 | 30.22 | 21000 | 0.3035 | 0.3961 | 0.1255 |
1.3044 | 30.93 | 21500 | 0.2952 | 0.3854 | 0.1242 |
1.3034 | 31.65 | 22000 | 0.2966 | 0.3772 | 0.1227 |
1.2963 | 32.37 | 22500 | 0.2844 | 0.3706 | 0.1208 |
1.2765 | 33.09 | 23000 | 0.2841 | 0.3567 | 0.1173 |
1.2438 | 33.81 | 23500 | 0.2734 | 0.3552 | 0.1137 |
1.2487 | 34.53 | 24000 | 0.2703 | 0.3502 | 0.1118 |
1.2249 | 35.25 | 24500 | 0.2650 | 0.3484 | 0.1142 |
1.2229 | 35.97 | 25000 | 0.2584 | 0.3374 | 0.1097 |
1.2374 | 36.69 | 25500 | 0.2568 | 0.3337 | 0.1095 |
1.2153 | 37.41 | 26000 | 0.2494 | 0.3327 | 0.1071 |
1.1925 | 38.13 | 26500 | 0.2518 | 0.3366 | 0.1077 |
1.1908 | 38.85 | 27000 | 0.2437 | 0.3272 | 0.1057 |
1.1858 | 39.57 | 27500 | 0.2396 | 0.3265 | 0.1044 |
1.1808 | 40.29 | 28000 | 0.2373 | 0.3156 | 0.1028 |
1.1842 | 41.01 | 28500 | 0.2356 | 0.3152 | 0.1026 |
1.1668 | 41.73 | 29000 | 0.2319 | 0.3188 | 0.1025 |
1.1448 | 42.45 | 29500 | 0.2293 | 0.3099 | 0.0995 |
1.1327 | 43.17 | 30000 | 0.2265 | 0.3047 | 0.0979 |
1.1307 | 43.88 | 30500 | 0.2222 | 0.3078 | 0.0989 |
1.1419 | 44.6 | 31000 | 0.2215 | 0.3038 | 0.0981 |
1.1231 | 45.32 | 31500 | 0.2193 | 0.3013 | 0.0972 |
1.139 | 46.04 | 32000 | 0.2162 | 0.3007 | 0.0968 |
1.1114 | 46.76 | 32500 | 0.2122 | 0.2982 | 0.0960 |
1.111 | 47.48 | 33000 | 0.2125 | 0.2946 | 0.0948 |
1.0982 | 48.2 | 33500 | 0.2099 | 0.2957 | 0.0953 |
1.109 | 48.92 | 34000 | 0.2092 | 0.2955 | 0.0955 |
1.0905 | 49.64 | 34500 | 0.2088 | 0.2954 | 0.0953 |
Disclaimer
Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
Authors
Wav2Vec2 XLS-R 300M Korean LM was trained and evaluated by Wilson Wongso. All computation and development are done on OVH Cloud.
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.10.3
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Dataset used to train w11wo/wav2vec2-xls-r-300m-korean-lm
Collection including w11wo/wav2vec2-xls-r-300m-korean-lm
Evaluation results
- Test WER on Zeroth Koreanself-reported30.940
- Test CER on Zeroth Koreanself-reported7.970
- Test WER on Robust Speech Event - Dev Dataself-reported68.340
- Test CER on Robust Speech Event - Dev Dataself-reported37.080
- Test WER on Robust Speech Event - Test Dataself-reported66.470