wav2vec2-large-xlsr-53-german-cv9
This model is a fine-tuned version of ./facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - DE dataset.
It achieves the following results on the test set:
- CER: 2.273015898213336
- Wer: 9.480663281840769
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Eval Wer |
---|---|---|---|---|
0.4129 | 1.0 | 3557 | 0.3015 | 0.2499 |
0.2121 | 2.0 | 7114 | 0.1596 | 0.1567 |
0.1455 | 3.0 | 10671 | 0.1377 | 0.1354 |
0.1436 | 4.0 | 14228 | 0.1301 | 0.1282 |
0.1144 | 5.0 | 17785 | 0.1225 | 0.1245 |
0.1219 | 6.0 | 21342 | 0.1254 | 0.1208 |
0.104 | 7.0 | 24899 | 0.1198 | 0.1232 |
0.1016 | 8.0 | 28456 | 0.1149 | 0.1174 |
0.1093 | 9.0 | 32013 | 0.1186 | 0.1186 |
0.0858 | 10.0 | 35570 | 0.1182 | 0.1164 |
0.102 | 11.0 | 39127 | 0.1191 | 0.1186 |
0.0834 | 12.0 | 42684 | 0.1161 | 0.1096 |
0.0916 | 13.0 | 46241 | 0.1147 | 0.1107 |
0.0811 | 14.0 | 49798 | 0.1174 | 0.1136 |
0.0814 | 15.0 | 53355 | 0.1132 | 0.1114 |
0.0865 | 16.0 | 56912 | 0.1134 | 0.1097 |
0.0701 | 17.0 | 60469 | 0.1096 | 0.1054 |
0.0891 | 18.0 | 64026 | 0.1110 | 0.1076 |
0.071 | 19.0 | 67583 | 0.1141 | 0.1074 |
0.0726 | 20.0 | 71140 | 0.1094 | 0.1093 |
0.0647 | 21.0 | 74697 | 0.1088 | 0.1095 |
0.0643 | 22.0 | 78254 | 0.1105 | 0.1044 |
0.0764 | 23.0 | 81811 | 0.1072 | 0.1042 |
0.0605 | 24.0 | 85368 | 0.1095 | 0.1026 |
0.0722 | 25.0 | 88925 | 0.1144 | 0.1066 |
0.0597 | 26.0 | 92482 | 0.1087 | 0.1022 |
0.062 | 27.0 | 96039 | 0.1073 | 0.1027 |
0.0536 | 28.0 | 99596 | 0.1068 | 0.1027 |
0.0616 | 29.0 | 103153 | 0.1097 | 0.1037 |
0.0642 | 30.0 | 106710 | 0.1117 | 0.1020 |
0.0555 | 31.0 | 110267 | 0.1109 | 0.0990 |
0.0632 | 32.0 | 113824 | 0.1104 | 0.0977 |
0.0482 | 33.0 | 117381 | 0.1108 | 0.0958 |
0.0601 | 34.0 | 120938 | 0.1095 | 0.0957 |
0.0508 | 35.0 | 124495 | 0.1079 | 0.0973 |
0.0526 | 36.0 | 128052 | 0.1068 | 0.0967 |
0.0487 | 37.0 | 131609 | 0.1081 | 0.0966 |
0.0495 | 38.0 | 135166 | 0.1099 | 0.0956 |
0.0528 | 39.0 | 138723 | 0.1091 | 0.0923 |
0.0439 | 40.0 | 142280 | 0.1111 | 0.0928 |
0.0467 | 41.0 | 145837 | 0.1131 | 0.0943 |
0.0407 | 42.0 | 149394 | 0.1115 | 0.0944 |
0.046 | 43.0 | 152951 | 0.1106 | 0.0935 |
0.0447 | 44.0 | 156508 | 0.1083 | 0.0919 |
0.0434 | 45.0 | 160065 | 0.1093 | 0.0909 |
0.0472 | 46.0 | 163622 | 0.1092 | 0.0921 |
0.0414 | 47.0 | 167179 | 0.1106 | 0.0922 |
0.0501 | 48.0 | 170736 | 0.1094 | 0.0918 |
0.0388 | 49.0 | 174293 | 0.1099 | 0.0918 |
0.0428 | 50.0 | 177850 | 0.1103 | 0.0915 |
Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
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Dataset used to train oliverguhr/wav2vec2-large-xlsr-53-german-cv9
Evaluation results
- Test WER on Common Voice 9self-reported9.481
- Test CER on Common Voice 9self-reported1.917
- Test WER (+LM) on Common Voice 9self-reported7.490
- Test CER (+LM) on Common Voice 9self-reported1.917
- Test WER on Common Voice 6.1self-reported8.122
- Test CER on Common Voice 6.1self-reported1.000
- Test WER (+LM) on Common Voice 6.1self-reported6.145
- Test CER (+LM) on Common Voice 6.1self-reported1.525