File size: 9,462 Bytes
a663308 a2ccdd3 a663308 9fae68b 3e990d7 9fae68b a663308 9fae68b a663308 9fae68b a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 a2ccdd3 a663308 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
---
language: ko
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- kresnik/zeroth_korean
model-index:
- name: Wav2Vec2 XLS-R 300M Korean LM
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Zeroth Korean
type: kresnik/zeroth_korean
args: clean
metrics:
- name: Test WER
type: wer
value: 30.94
- name: Test CER
type: cer
value: 7.97
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ko
metrics:
- name: Test WER
type: wer
value: 68.34
- name: Test CER
type: cer
value: 37.08
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ko
metrics:
- name: Test WER
type: wer
value: 66.47
---
# Wav2Vec2 XLS-R 300M Korean LM
Wav2Vec2 XLS-R 300M Korean LM is an automatic speech recognition model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Zeroth Korean](https://huggingface.co/datasets/kresnik/zeroth_korean) dataset. A 5-gram Language model, trained on the Korean subset of [Open Subtitles](https://huggingface.co/datasets/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](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) 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](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-korean-lm/tree/main) tab, as well as the [Training metrics](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-korean-lm/tensorboard) logged via Tensorboard.
As for the N-gram language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) 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](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-korean).
### Training hyperparameters
The following hyperparameters were used during training:
- `learning_rate`: 7.5e-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`: 2000
- `num_epochs`: 50.0
- `mixed_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](https://w11wo.github.io/). 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
|