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robinhad
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wav2vec2-xls-r-300m-uk

Automatic Speech Recognition
Transformers
PyTorch
Ukrainian
wav2vec2
common_voice
generated_from_trainer
Eval Results
Inference Endpoints
Model card Files Community
2
robinhad commited on
Commit
5617a47
•
1 Parent(s): 4634eb6

Update to model with WER 12.22%

Browse files
Files changed (3) hide show
  1. README.md +60 -73
  2. config.json +4 -4
  3. pytorch_model.bin +1 -1
README.md CHANGED
@@ -1,7 +1,7 @@
1
  ---
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  language:
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  - uk
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- license: apache-2.0
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  tags:
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  - automatic-speech-recognition
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  - common_voice
@@ -21,20 +21,20 @@ model-index:
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  metrics:
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  - name: Test WER
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  type: wer
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- value: 27.99
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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  # wav2vec2-xls-r-300m-uk
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- This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
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- Notebook for training is located in this repository: [https://github.com/robinhad/wav2vec2-xls-r-ukrainian](https://github.com/robinhad/wav2vec2-xls-r-ukrainian).
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4165
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- - Wer: 0.2799
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- - Cer: 0.0601
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  ## Model description
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@@ -53,80 +53,67 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0003
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- - train_batch_size: 8
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- - eval_batch_size: 8
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  - seed: 42
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- - gradient_accumulation_steps: 20
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- - total_train_batch_size: 160
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - lr_scheduler_warmup_steps: 500
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- - num_epochs: 500
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
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- |:-------------:|:------:|:-----:|:------:|:---------------:|:------:|
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- | 4.3982 | 9.3 | 400 | 0.1437 | 0.5218 | 0.6507 |
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- | 0.229 | 18.6 | 800 | 0.0848 | 0.3679 | 0.4048 |
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- | 0.1054 | 27.9 | 1200 | 0.0778 | 0.3813 | 0.3670 |
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- | 0.0784 | 37.21 | 1600 | 0.0747 | 0.3839 | 0.3550 |
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- | 0.066 | 46.51 | 2000 | 0.0736 | 0.3970 | 0.3443 |
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- | 0.0603 | 55.8 | 2400 | 0.0722 | 0.3702 | 0.3393 |
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- | 0.0539 | 65.11 | 2800 | 0.0724 | 0.3762 | 0.3388 |
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- | 0.0497 | 74.41 | 3200 | 0.0713 | 0.3623 | 0.3414 |
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- | 0.0432 | 83.71 | 3600 | 0.0725 | 0.3847 | 0.3346 |
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- | 0.0438 | 93.02 | 4000 | 0.0750 | 0.4058 | 0.3393 |
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- | 0.0413 | 102.32 | 4400 | 0.0727 | 0.3957 | 0.3363 |
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- | 0.039 | 111.62 | 4800 | 0.0718 | 0.3865 | 0.3330 |
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- | 0.0356 | 120.92 | 5200 | 0.0711 | 0.3860 | 0.3319 |
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- | 0.0336 | 130.23 | 5600 | 0.0700 | 0.3902 | 0.3242 |
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- | 0.034 | 139.53 | 6000 | 0.0732 | 0.3930 | 0.3337 |
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- | 0.0273 | 148.83 | 6400 | 0.0748 | 0.3912 | 0.3375 |
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- | 0.027 | 158.14 | 6800 | 0.0752 | 0.4266 | 0.3434 |
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- | 0.028 | 167.44 | 7200 | 0.0708 | 0.3895 | 0.3227 |
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- | 0.0241 | 176.73 | 7600 | 0.0727 | 0.3967 | 0.3294 |
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- | 0.0241 | 186.05 | 8000 | 0.0712 | 0.4058 | 0.3255 |
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- | 0.0209 | 195.34 | 8400 | 0.0702 | 0.4102 | 0.3233 |
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- | 0.0206 | 204.64 | 8800 | 0.0699 | 0.4075 | 0.3194 |
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- | 0.0172 | 213.94 | 9200 | 0.0695 | 0.4222 | 0.3191 |
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- | 0.0166 | 223.25 | 9600 | 0.0678 | 0.3860 | 0.3135 |
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- | 0.0156 | 232.55 | 10000 | 0.0677 | 0.4035 | 0.3117 |
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- | 0.0149 | 241.85 | 10400 | 0.0677 | 0.3951 | 0.3087 |
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- | 0.0142 | 251.16 | 10800 | 0.0674 | 0.3972 | 0.3097 |
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- | 0.0134 | 260.46 | 11200 | 0.0675 | 0.4069 | 0.3111 |
100
- | 0.0116 | 269.76 | 11600 | 0.0697 | 0.4189 | 0.3161 |
101
- | 0.0119 | 279.07 | 12000 | 0.0648 | 0.3902 | 0.3008 |
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- | 0.0098 | 288.37 | 12400 | 0.0652 | 0.4095 | 0.3002 |
103
- | 0.0091 | 297.67 | 12800 | 0.0644 | 0.3892 | 0.2990 |
104
- | 0.0094 | 306.96 | 13200 | 0.0647 | 0.4026 | 0.2983 |
105
- | 0.0081 | 316.28 | 13600 | 0.0646 | 0.4303 | 0.2978 |
106
- | 0.0079 | 325.57 | 14000 | 0.0643 | 0.4044 | 0.2980 |
107
- | 0.0072 | 334.87 | 14400 | 0.0655 | 0.3828 | 0.2999 |
108
- | 0.0081 | 344.18 | 14800 | 0.0668 | 0.4108 | 0.3046 |
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- | 0.0088 | 353.48 | 15200 | 0.0654 | 0.4019 | 0.2993 |
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- | 0.0088 | 362.78 | 15600 | 0.0681 | 0.4073 | 0.3091 |
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- | 0.0079 | 372.09 | 16000 | 0.0667 | 0.4204 | 0.3055 |
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- | 0.0072 | 381.39 | 16400 | 0.0656 | 0.4030 | 0.3028 |
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- | 0.0073 | 390.69 | 16800 | 0.0677 | 0.4032 | 0.3081 |
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- | 0.0069 | 399.99 | 17200 | 0.0669 | 0.4130 | 0.3021 |
115
- | 0.0063 | 409.3 | 17600 | 0.0651 | 0.4072 | 0.2979 |
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- | 0.0059 | 418.6 | 18000 | 0.0640 | 0.4110 | 0.2969 |
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- | 0.0056 | 427.9 | 18400 | 0.0647 | 0.4229 | 0.2995 |
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- | 0.005 | 437.21 | 18800 | 0.0624 | 0.4118 | 0.2885 |
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- | 0.0046 | 446.51 | 19200 | 0.0615 | 0.4111 | 0.2841 |
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- | 0.0043 | 455.8 | 19600 | 0.0616 | 0.4071 | 0.2850 |
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- | 0.0038 | 465.11 | 20000 | 0.0624 | 0.4268 | 0.2867 |
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- | 0.0035 | 474.41 | 20400 | 0.0605 | 0.4117 | 0.2820 |
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- | 0.0035 | 483.71 | 20800 | 0.0602 | 0.4155 | 0.2819 |
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- | 0.0034 | 493.02 | 21200 | 0.0601 | 0.4165 | 0.2799 |
125
 
126
 
127
  ### Framework versions
128
 
129
- - Transformers 4.14.1
130
- - Pytorch 1.10.0
131
- - Datasets 1.16.1
132
- - Tokenizers 0.10.3
1
  ---
2
  language:
3
  - uk
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+ license: mit
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  tags:
6
  - automatic-speech-recognition
7
  - common_voice
21
  metrics:
22
  - name: Test WER
23
  type: wer
24
+ value: 12.22
25
  ---
26
 
27
+
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
29
  should probably proofread and complete it, then remove this comment. -->
30
 
31
  # wav2vec2-xls-r-300m-uk
32
 
33
+ This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
 
34
  It achieves the following results on the evaluation set:
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+ - Loss: 0.0927
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+ - Wer: 0.1222
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+ - Cer: 0.0204
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39
  ## Model description
40
 
53
  ### Training hyperparameters
54
 
55
  The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 40
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+ - eval_batch_size: 40
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  - seed: 42
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+ - gradient_accumulation_steps: 6
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+ - total_train_batch_size: 240
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 100
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  - mixed_precision_training: Native AMP
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68
  ### Training results
69
 
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+ | Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
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+ |:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
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+ | 9.0008 | 1.68 | 200 | 1.0 | 3.7590 | 1.0 |
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+ | 3.4972 | 3.36 | 400 | 1.0 | 3.3933 | 1.0 |
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+ | 3.3432 | 5.04 | 600 | 1.0 | 3.2617 | 1.0 |
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+ | 3.2421 | 6.72 | 800 | 1.0 | 3.0712 | 1.0 |
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+ | 1.9839 | 7.68 | 1000 | 0.1400 | 0.7204 | 0.6561 |
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+ | 0.8017 | 9.36 | 1200 | 0.0766 | 0.3734 | 0.4159 |
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+ | 0.5554 | 11.04 | 1400 | 0.0583 | 0.2621 | 0.3237 |
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+ | 0.4309 | 12.68 | 1600 | 0.0486 | 0.2085 | 0.2753 |
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+ | 0.3697 | 14.36 | 1800 | 0.0421 | 0.1746 | 0.2427 |
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+ | 0.3293 | 16.04 | 2000 | 0.0388 | 0.1597 | 0.2243 |
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+ | 0.2934 | 17.72 | 2200 | 0.0358 | 0.1428 | 0.2083 |
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+ | 0.2704 | 19.4 | 2400 | 0.0333 | 0.1326 | 0.1949 |
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+ | 0.2547 | 21.08 | 2600 | 0.0322 | 0.1255 | 0.1882 |
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+ | 0.2366 | 22.76 | 2800 | 0.0309 | 0.1211 | 0.1815 |
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+ | 0.2183 | 24.44 | 3000 | 0.0294 | 0.1159 | 0.1727 |
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+ | 0.2115 | 26.13 | 3200 | 0.0280 | 0.1117 | 0.1661 |
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+ | 0.1968 | 27.8 | 3400 | 0.0274 | 0.1063 | 0.1622 |
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+ | 0.1922 | 29.48 | 3600 | 0.0269 | 0.1082 | 0.1598 |
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+ | 0.1847 | 31.17 | 3800 | 0.0260 | 0.1061 | 0.1550 |
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+ | 0.1715 | 32.84 | 4000 | 0.0252 | 0.1014 | 0.1496 |
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+ | 0.1689 | 34.53 | 4200 | 0.0250 | 0.1012 | 0.1492 |
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+ | 0.1655 | 36.21 | 4400 | 0.0243 | 0.0999 | 0.1450 |
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+ | 0.1585 | 37.88 | 4600 | 0.0239 | 0.0967 | 0.1432 |
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+ | 0.1492 | 39.57 | 4800 | 0.0237 | 0.0978 | 0.1421 |
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+ | 0.1491 | 41.25 | 5000 | 0.0236 | 0.0963 | 0.1412 |
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+ | 0.1453 | 42.93 | 5200 | 0.0230 | 0.0979 | 0.1373 |
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+ | 0.1386 | 44.61 | 5400 | 0.0227 | 0.0959 | 0.1353 |
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+ | 0.1387 | 46.29 | 5600 | 0.0226 | 0.0927 | 0.1355 |
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+ | 0.1329 | 47.97 | 5800 | 0.0224 | 0.0951 | 0.1341 |
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+ | 0.1295 | 49.65 | 6000 | 0.0219 | 0.0950 | 0.1306 |
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+ | 0.1287 | 51.33 | 6200 | 0.0216 | 0.0937 | 0.1290 |
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+ | 0.1277 | 53.02 | 6400 | 0.0215 | 0.0963 | 0.1294 |
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+ | 0.1201 | 54.69 | 6600 | 0.0213 | 0.0959 | 0.1282 |
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+ | 0.1199 | 56.38 | 6800 | 0.0215 | 0.0944 | 0.1286 |
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+ | 0.1221 | 58.06 | 7000 | 0.0209 | 0.0938 | 0.1249 |
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+ | 0.1145 | 59.68 | 7200 | 0.0208 | 0.0941 | 0.1254 |
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+ | 0.1143 | 61.36 | 7400 | 0.0209 | 0.0941 | 0.1249 |
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+ | 0.1143 | 63.04 | 7600 | 0.0209 | 0.0940 | 0.1248 |
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+ | 0.1137 | 64.72 | 7800 | 0.0205 | 0.0931 | 0.1234 |
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+ | 0.1125 | 66.4 | 8000 | 0.0204 | 0.0927 | 0.1222 |
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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+ - Transformers 4.25.1
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+ - Pytorch 1.13.1+cu117
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2
config.json CHANGED
@@ -8,7 +8,7 @@
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  "architectures": [
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  "Wav2Vec2ForCTC"
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  ],
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- "attention_dropout": 0.1,
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  "bos_token_id": 1,
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  "classifier_proj_size": 256,
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  "codevector_dim": 768,
@@ -49,17 +49,17 @@
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  "feat_extract_activation": "gelu",
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  "feat_extract_dropout": 0.0,
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  "feat_extract_norm": "layer",
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- "feat_proj_dropout": 0.1,
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  "feat_quantizer_dropout": 0.0,
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  "final_dropout": 0.0,
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  "gradient_checkpointing": false,
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  "hidden_act": "gelu",
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- "hidden_dropout": 0.1,
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  "hidden_size": 1024,
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  "initializer_range": 0.02,
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  "intermediate_size": 4096,
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  "layer_norm_eps": 1e-05,
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- "layerdrop": 0.1,
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  "mask_feature_length": 10,
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  "mask_feature_min_masks": 0,
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  "mask_feature_prob": 0.0,
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  "architectures": [
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  "Wav2Vec2ForCTC"
10
  ],
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+ "attention_dropout": 0.07,
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  "bos_token_id": 1,
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  "classifier_proj_size": 256,
14
  "codevector_dim": 768,
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  "feat_extract_activation": "gelu",
50
  "feat_extract_dropout": 0.0,
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  "feat_extract_norm": "layer",
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+ "feat_proj_dropout": 0.07,
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  "feat_quantizer_dropout": 0.0,
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  "final_dropout": 0.0,
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  "gradient_checkpointing": false,
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  "hidden_act": "gelu",
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+ "hidden_dropout": 0.07,
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  "hidden_size": 1024,
59
  "initializer_range": 0.02,
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  "intermediate_size": 4096,
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  "layer_norm_eps": 1e-05,
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+ "layerdrop": 0.07,
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  "mask_feature_length": 10,
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  "mask_feature_min_masks": 0,
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  "mask_feature_prob": 0.0,
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