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hiba2
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wav2vec2_fleurs

Automatic Speech Recognition
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wav2vec2
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End of training

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  1. README.md +251 -129
  2. preprocessor_config.json +1 -0
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Wer
24
  type: wer
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- value: 0.3367091772943236
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  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
32
 
33
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the fleurs dataset.
34
  It achieves the following results on the evaluation set:
35
- - Loss: 0.4033
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- - Wer: 0.3367
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  ## Model description
39
 
@@ -52,145 +52,267 @@ More information needed
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  ### Training hyperparameters
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54
  The following hyperparameters were used during training:
55
- - learning_rate: 4e-05
56
  - train_batch_size: 4
57
  - eval_batch_size: 8
58
  - seed: 42
 
 
59
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
60
- - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 100
62
- - num_epochs: 20
63
  - mixed_precision_training: Native AMP
64
 
65
  ### Training results
66
 
67
  | Training Loss | Epoch | Step | Validation Loss | Wer |
68
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
69
- | 18.6221 | 0.17 | 100 | 10.1383 | 1.0 |
70
- | 6.5078 | 0.33 | 200 | 4.0182 | 1.0 |
71
- | 3.632 | 0.5 | 300 | 3.2678 | 1.0 |
72
- | 3.2359 | 0.67 | 400 | 3.1984 | 1.0 |
73
- | 3.2014 | 0.83 | 500 | 3.1752 | 1.0 |
74
- | 3.1857 | 1.0 | 600 | 3.1671 | 1.0 |
75
- | 3.1816 | 1.17 | 700 | 3.1657 | 1.0 |
76
- | 3.1912 | 1.33 | 800 | 3.1570 | 1.0 |
77
- | 3.186 | 1.5 | 900 | 3.1548 | 1.0 |
78
- | 3.1554 | 1.67 | 1000 | 3.1478 | 1.0 |
79
- | 3.1521 | 1.83 | 1100 | 3.1442 | 1.0 |
80
- | 3.1584 | 2.0 | 1200 | 3.1369 | 1.0 |
81
- | 3.1554 | 2.17 | 1300 | 3.1340 | 1.0 |
82
- | 3.172 | 2.33 | 1400 | 3.1304 | 1.0 |
83
- | 3.1479 | 2.5 | 1500 | 3.1303 | 1.0 |
84
- | 3.1359 | 2.67 | 1600 | 3.0864 | 1.0 |
85
- | 3.0757 | 2.83 | 1700 | 2.9191 | 1.0 |
86
- | 2.8491 | 3.0 | 1800 | 2.5490 | 1.0 |
87
- | 2.4969 | 3.17 | 1900 | 1.9998 | 0.9785 |
88
- | 2.048 | 3.33 | 2000 | 1.5004 | 0.9297 |
89
- | 1.7632 | 3.5 | 2100 | 1.2369 | 0.8613 |
90
- | 1.5885 | 3.67 | 2200 | 1.0752 | 0.7953 |
91
- | 1.3712 | 3.83 | 2300 | 0.9573 | 0.7519 |
92
- | 1.2916 | 4.0 | 2400 | 0.9038 | 0.7089 |
93
- | 1.2559 | 4.17 | 2500 | 0.8269 | 0.6853 |
94
- | 1.1625 | 4.33 | 2600 | 0.7781 | 0.6539 |
95
- | 1.1264 | 4.5 | 2700 | 0.7555 | 0.6337 |
96
- | 1.032 | 4.67 | 2800 | 0.7215 | 0.6032 |
97
- | 1.0592 | 4.83 | 2900 | 0.6883 | 0.5734 |
98
- | 0.9682 | 5.0 | 3000 | 0.6657 | 0.5504 |
99
- | 0.9851 | 5.17 | 3100 | 0.6518 | 0.5448 |
100
- | 0.9515 | 5.33 | 3200 | 0.6382 | 0.5403 |
101
- | 0.9009 | 5.5 | 3300 | 0.6226 | 0.5296 |
102
- | 0.9048 | 5.67 | 3400 | 0.6123 | 0.5161 |
103
- | 0.8882 | 5.83 | 3500 | 0.6047 | 0.5098 |
104
- | 0.8749 | 6.0 | 3600 | 0.5909 | 0.5006 |
105
- | 0.7939 | 6.17 | 3700 | 0.5804 | 0.4931 |
106
- | 0.8363 | 6.33 | 3800 | 0.5744 | 0.4877 |
107
- | 0.8605 | 6.5 | 3900 | 0.5776 | 0.4884 |
108
- | 0.8358 | 6.67 | 4000 | 0.5497 | 0.4745 |
109
- | 0.7744 | 6.83 | 4100 | 0.5549 | 0.4664 |
110
- | 0.7867 | 7.0 | 4200 | 0.5429 | 0.4629 |
111
- | 0.7166 | 7.17 | 4300 | 0.5306 | 0.4465 |
112
- | 0.7347 | 7.33 | 4400 | 0.5363 | 0.4521 |
113
- | 0.7173 | 7.5 | 4500 | 0.5289 | 0.4429 |
114
- | 0.7653 | 7.67 | 4600 | 0.5240 | 0.4389 |
115
- | 0.7388 | 7.83 | 4700 | 0.5062 | 0.4304 |
116
- | 0.7326 | 8.0 | 4800 | 0.5073 | 0.4290 |
117
- | 0.6622 | 8.17 | 4900 | 0.5049 | 0.4236 |
118
- | 0.7495 | 8.33 | 5000 | 0.5094 | 0.4254 |
119
- | 0.6898 | 8.5 | 5100 | 0.4874 | 0.4216 |
120
- | 0.6664 | 8.67 | 5200 | 0.4948 | 0.4225 |
121
- | 0.6783 | 8.83 | 5300 | 0.4879 | 0.4131 |
122
- | 0.7205 | 9.0 | 5400 | 0.4751 | 0.4136 |
123
- | 0.6182 | 9.17 | 5500 | 0.4795 | 0.4085 |
124
- | 0.6895 | 9.33 | 5600 | 0.4730 | 0.4099 |
125
- | 0.6503 | 9.5 | 5700 | 0.4713 | 0.4029 |
126
- | 0.624 | 9.67 | 5800 | 0.4699 | 0.4024 |
127
- | 0.6268 | 9.83 | 5900 | 0.4726 | 0.4069 |
128
- | 0.6525 | 10.0 | 6000 | 0.4593 | 0.3953 |
129
- | 0.6112 | 10.17 | 6100 | 0.4558 | 0.3922 |
130
- | 0.657 | 10.33 | 6200 | 0.4621 | 0.3940 |
131
- | 0.6445 | 10.5 | 6300 | 0.4579 | 0.3906 |
132
- | 0.5869 | 10.67 | 6400 | 0.4548 | 0.3903 |
133
- | 0.5855 | 10.83 | 6500 | 0.4433 | 0.3840 |
134
- | 0.5538 | 11.0 | 6600 | 0.4514 | 0.3897 |
135
- | 0.5599 | 11.17 | 6700 | 0.4403 | 0.3786 |
136
- | 0.5691 | 11.33 | 6800 | 0.4411 | 0.3800 |
137
- | 0.5731 | 11.5 | 6900 | 0.4396 | 0.3768 |
138
- | 0.5707 | 11.67 | 7000 | 0.4492 | 0.3770 |
139
- | 0.5504 | 11.83 | 7100 | 0.4391 | 0.3690 |
140
- | 0.6058 | 12.0 | 7200 | 0.4344 | 0.3717 |
141
- | 0.5676 | 12.17 | 7300 | 0.4354 | 0.3758 |
142
- | 0.5684 | 12.33 | 7400 | 0.4351 | 0.3656 |
143
- | 0.5404 | 12.5 | 7500 | 0.4324 | 0.3636 |
144
- | 0.5504 | 12.67 | 7600 | 0.4313 | 0.3658 |
145
- | 0.5596 | 12.83 | 7700 | 0.4268 | 0.3632 |
146
- | 0.5246 | 13.0 | 7800 | 0.4316 | 0.3633 |
147
- | 0.5441 | 13.17 | 7900 | 0.4233 | 0.3648 |
148
- | 0.5318 | 13.33 | 8000 | 0.4260 | 0.3597 |
149
- | 0.5116 | 13.5 | 8100 | 0.4279 | 0.3591 |
150
- | 0.5299 | 13.67 | 8200 | 0.4233 | 0.3606 |
151
- | 0.5519 | 13.83 | 8300 | 0.4166 | 0.3567 |
152
- | 0.5452 | 14.0 | 8400 | 0.4233 | 0.3573 |
153
- | 0.5111 | 14.17 | 8500 | 0.4203 | 0.3580 |
154
- | 0.5365 | 14.33 | 8600 | 0.4163 | 0.3577 |
155
- | 0.5023 | 14.5 | 8700 | 0.4135 | 0.3552 |
156
- | 0.5189 | 14.67 | 8800 | 0.4133 | 0.3485 |
157
- | 0.5492 | 14.83 | 8900 | 0.4133 | 0.3478 |
158
- | 0.5128 | 15.0 | 9000 | 0.4114 | 0.3478 |
159
- | 0.486 | 15.17 | 9100 | 0.4222 | 0.3472 |
160
- | 0.5015 | 15.33 | 9200 | 0.4129 | 0.3515 |
161
- | 0.4871 | 15.5 | 9300 | 0.4132 | 0.3430 |
162
- | 0.5267 | 15.67 | 9400 | 0.4109 | 0.3481 |
163
- | 0.4814 | 15.83 | 9500 | 0.4109 | 0.3461 |
164
- | 0.4801 | 16.0 | 9600 | 0.4140 | 0.3453 |
165
- | 0.4894 | 16.17 | 9700 | 0.4074 | 0.3433 |
166
- | 0.4756 | 16.33 | 9800 | 0.4070 | 0.3410 |
167
- | 0.4446 | 16.5 | 9900 | 0.4088 | 0.3412 |
168
- | 0.4838 | 16.67 | 10000 | 0.4070 | 0.3407 |
169
- | 0.5087 | 16.83 | 10100 | 0.4048 | 0.3422 |
170
- | 0.4994 | 17.0 | 10200 | 0.4043 | 0.3442 |
171
- | 0.5421 | 17.17 | 10300 | 0.4088 | 0.3483 |
172
- | 0.489 | 17.33 | 10400 | 0.4097 | 0.3450 |
173
- | 0.4618 | 17.5 | 10500 | 0.4077 | 0.3430 |
174
- | 0.4734 | 17.67 | 10600 | 0.4028 | 0.3433 |
175
- | 0.4882 | 17.83 | 10700 | 0.4040 | 0.3393 |
176
- | 0.4804 | 18.0 | 10800 | 0.4045 | 0.3385 |
177
- | 0.483 | 18.17 | 10900 | 0.4055 | 0.3366 |
178
- | 0.4916 | 18.33 | 11000 | 0.4077 | 0.3375 |
179
- | 0.4933 | 18.5 | 11100 | 0.4056 | 0.3365 |
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- | 0.4881 | 18.67 | 11200 | 0.4023 | 0.3375 |
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- | 0.4869 | 18.83 | 11300 | 0.4031 | 0.3378 |
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- | 0.4649 | 19.0 | 11400 | 0.4026 | 0.3382 |
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- | 0.4793 | 19.17 | 11500 | 0.4035 | 0.3376 |
184
- | 0.5252 | 19.33 | 11600 | 0.4019 | 0.3375 |
185
- | 0.4681 | 19.5 | 11700 | 0.4026 | 0.3382 |
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- | 0.4311 | 19.67 | 11800 | 0.4026 | 0.3368 |
187
- | 0.4799 | 19.83 | 11900 | 0.4034 | 0.3372 |
188
- | 0.4323 | 20.0 | 12000 | 0.4033 | 0.3367 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
189
 
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191
  ### Framework versions
192
 
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- - Transformers 4.36.0.dev0
194
- - Pytorch 2.1.0+cu118
195
- - Datasets 2.15.0
196
  - Tokenizers 0.15.0
 
22
  metrics:
23
  - name: Wer
24
  type: wer
25
+ value: 0.28107026756689174
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the fleurs dataset.
34
  It achieves the following results on the evaluation set:
35
+ - Loss: 0.3761
36
+ - Wer: 0.2811
37
 
38
  ## Model description
39
 
 
52
  ### Training hyperparameters
53
 
54
  The following hyperparameters were used during training:
55
+ - learning_rate: 1e-05
56
  - train_batch_size: 4
57
  - eval_batch_size: 8
58
  - seed: 42
59
+ - gradient_accumulation_steps: 2
60
+ - total_train_batch_size: 8
61
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
63
  - lr_scheduler_warmup_steps: 100
64
+ - num_epochs: 40
65
  - mixed_precision_training: Native AMP
66
 
67
  ### Training results
68
 
69
  | Training Loss | Epoch | Step | Validation Loss | Wer |
70
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
71
+ | 17.6592 | 0.33 | 100 | 7.5846 | 1.0 |
72
+ | 5.291 | 0.67 | 200 | 3.6732 | 1.0 |
73
+ | 3.4166 | 1.0 | 300 | 3.2280 | 1.0 |
74
+ | 3.2149 | 1.33 | 400 | 3.1839 | 1.0 |
75
+ | 3.1987 | 1.67 | 500 | 3.1726 | 1.0 |
76
+ | 3.1839 | 2.0 | 600 | 3.1652 | 1.0 |
77
+ | 3.1746 | 2.33 | 700 | 3.1607 | 1.0 |
78
+ | 3.1727 | 2.67 | 800 | 3.1576 | 1.0 |
79
+ | 3.1776 | 3.0 | 900 | 3.1446 | 1.0 |
80
+ | 3.1622 | 3.33 | 1000 | 3.1419 | 1.0 |
81
+ | 3.155 | 3.67 | 1100 | 3.1375 | 1.0 |
82
+ | 3.1611 | 4.0 | 1200 | 3.1334 | 1.0 |
83
+ | 3.1535 | 4.33 | 1300 | 3.1327 | 1.0 |
84
+ | 3.146 | 4.67 | 1400 | 3.1266 | 1.0 |
85
+ | 3.1437 | 5.0 | 1500 | 3.1036 | 1.0 |
86
+ | 3.098 | 5.33 | 1600 | 2.9865 | 1.0 |
87
+ | 2.9291 | 5.67 | 1700 | 2.6558 | 1.0 |
88
+ | 2.6039 | 6.0 | 1800 | 2.0985 | 0.9974 |
89
+ | 2.1178 | 6.33 | 1900 | 1.5524 | 0.9067 |
90
+ | 1.697 | 6.67 | 2000 | 1.1500 | 0.8066 |
91
+ | 1.4577 | 7.0 | 2100 | 0.9821 | 0.7531 |
92
+ | 1.2932 | 7.33 | 2200 | 0.8853 | 0.7084 |
93
+ | 1.1964 | 7.67 | 2300 | 0.8097 | 0.6629 |
94
+ | 1.1006 | 8.0 | 2400 | 0.7561 | 0.6190 |
95
+ | 1.05 | 8.33 | 2500 | 0.7303 | 0.6097 |
96
+ | 0.9948 | 8.67 | 2600 | 0.6801 | 0.5776 |
97
+ | 1.0013 | 9.0 | 2700 | 0.6637 | 0.5544 |
98
+ | 0.9249 | 9.33 | 2800 | 0.6479 | 0.5448 |
99
+ | 0.9226 | 9.67 | 2900 | 0.6200 | 0.5203 |
100
+ | 0.9098 | 10.0 | 3000 | 0.6051 | 0.5069 |
101
+ | 0.8517 | 10.33 | 3100 | 0.5971 | 0.4997 |
102
+ | 0.8322 | 10.67 | 3200 | 0.5780 | 0.4849 |
103
+ | 0.8497 | 11.0 | 3300 | 0.5761 | 0.4831 |
104
+ | 0.8197 | 11.33 | 3400 | 0.5599 | 0.4712 |
105
+ | 0.7526 | 11.67 | 3500 | 0.5521 | 0.4694 |
106
+ | 0.8102 | 12.0 | 3600 | 0.5462 | 0.4641 |
107
+ | 0.7809 | 12.33 | 3700 | 0.5404 | 0.4605 |
108
+ | 0.7684 | 12.67 | 3800 | 0.5371 | 0.4579 |
109
+ | 0.752 | 13.0 | 3900 | 0.5287 | 0.4517 |
110
+ | 0.7293 | 13.33 | 4000 | 0.5233 | 0.4515 |
111
+ | 0.729 | 13.67 | 4100 | 0.5225 | 0.4471 |
112
+ | 0.7107 | 14.0 | 4200 | 0.5068 | 0.4407 |
113
+ | 0.6823 | 14.33 | 4300 | 0.5064 | 0.4364 |
114
+ | 0.6988 | 14.67 | 4400 | 0.5016 | 0.4325 |
115
+ | 0.7049 | 15.0 | 4500 | 0.4939 | 0.4357 |
116
+ | 0.674 | 15.33 | 4600 | 0.4939 | 0.4295 |
117
+ | 0.7003 | 15.67 | 4700 | 0.4917 | 0.4300 |
118
+ | 0.684 | 16.0 | 4800 | 0.4862 | 0.4255 |
119
+ | 0.6581 | 16.33 | 4900 | 0.4854 | 0.4247 |
120
+ | 0.6839 | 16.67 | 5000 | 0.4806 | 0.4200 |
121
+ | 0.6494 | 17.0 | 5100 | 0.4798 | 0.4222 |
122
+ | 0.6695 | 17.33 | 5200 | 0.4770 | 0.4169 |
123
+ | 0.6396 | 17.67 | 5300 | 0.4758 | 0.4187 |
124
+ | 0.6676 | 18.0 | 5400 | 0.4740 | 0.4184 |
125
+ | 0.6309 | 18.33 | 5500 | 0.4741 | 0.4150 |
126
+ | 0.657 | 18.67 | 5600 | 0.4735 | 0.4127 |
127
+ | 0.6768 | 19.0 | 5700 | 0.4717 | 0.4129 |
128
+ | 0.6433 | 19.33 | 5800 | 0.4704 | 0.4135 |
129
+ | 0.6298 | 19.67 | 5900 | 0.4701 | 0.4131 |
130
+ | 0.6555 | 20.0 | 6000 | 0.4698 | 0.4114 |
131
+ | 0.6775 | 10.17 | 6100 | 0.4858 | 0.4204 |
132
+ | 0.6795 | 10.33 | 6200 | 0.4896 | 0.4164 |
133
+ | 0.6112 | 10.5 | 6300 | 0.4760 | 0.4071 |
134
+ | 0.6233 | 10.67 | 6400 | 0.4672 | 0.4110 |
135
+ | 0.6452 | 10.83 | 6500 | 0.4670 | 0.4110 |
136
+ | 0.6558 | 11.0 | 6600 | 0.4586 | 0.3987 |
137
+ | 0.5709 | 11.17 | 6700 | 0.4527 | 0.3937 |
138
+ | 0.5884 | 11.33 | 6800 | 0.4669 | 0.4032 |
139
+ | 0.6245 | 11.5 | 6900 | 0.4541 | 0.4010 |
140
+ | 0.6294 | 11.67 | 7000 | 0.4462 | 0.3880 |
141
+ | 0.6167 | 11.83 | 7100 | 0.4383 | 0.3768 |
142
+ | 0.6043 | 12.0 | 7200 | 0.4332 | 0.3697 |
143
+ | 0.5714 | 12.17 | 7300 | 0.4450 | 0.3705 |
144
+ | 0.5372 | 12.33 | 7400 | 0.4398 | 0.3781 |
145
+ | 0.5772 | 12.5 | 7500 | 0.4429 | 0.3755 |
146
+ | 0.5943 | 12.67 | 7600 | 0.4325 | 0.3708 |
147
+ | 0.571 | 12.83 | 7700 | 0.4447 | 0.3797 |
148
+ | 0.5055 | 13.0 | 7800 | 0.4237 | 0.3610 |
149
+ | 0.5316 | 13.17 | 7900 | 0.4279 | 0.3621 |
150
+ | 0.5225 | 13.33 | 8000 | 0.4200 | 0.3611 |
151
+ | 0.5162 | 13.5 | 8100 | 0.4295 | 0.3593 |
152
+ | 0.5353 | 13.67 | 8200 | 0.4148 | 0.3568 |
153
+ | 0.4887 | 13.83 | 8300 | 0.4096 | 0.3513 |
154
+ | 0.5302 | 14.0 | 8400 | 0.4185 | 0.3538 |
155
+ | 0.506 | 14.17 | 8500 | 0.4226 | 0.3480 |
156
+ | 0.5099 | 14.33 | 8600 | 0.4253 | 0.3517 |
157
+ | 0.473 | 14.5 | 8700 | 0.4096 | 0.3461 |
158
+ | 0.4963 | 14.67 | 8800 | 0.4074 | 0.3462 |
159
+ | 0.4984 | 14.83 | 8900 | 0.4135 | 0.3445 |
160
+ | 0.4896 | 15.0 | 9000 | 0.4038 | 0.3378 |
161
+ | 0.4836 | 15.17 | 9100 | 0.4108 | 0.3412 |
162
+ | 0.4393 | 15.33 | 9200 | 0.4258 | 0.3320 |
163
+ | 0.4589 | 15.5 | 9300 | 0.4045 | 0.3306 |
164
+ | 0.4711 | 15.67 | 9400 | 0.4052 | 0.3355 |
165
+ | 0.471 | 15.83 | 9500 | 0.4069 | 0.3337 |
166
+ | 0.4778 | 16.0 | 9600 | 0.4003 | 0.3270 |
167
+ | 0.4495 | 16.17 | 9700 | 0.3973 | 0.3276 |
168
+ | 0.4512 | 16.33 | 9800 | 0.4097 | 0.3308 |
169
+ | 0.4555 | 16.5 | 9900 | 0.4113 | 0.3283 |
170
+ | 0.4535 | 16.67 | 10000 | 0.4024 | 0.3271 |
171
+ | 0.4226 | 16.83 | 10100 | 0.3938 | 0.3265 |
172
+ | 0.457 | 17.0 | 10200 | 0.4116 | 0.3363 |
173
+ | 0.4002 | 17.17 | 10300 | 0.4037 | 0.3261 |
174
+ | 0.3894 | 17.33 | 10400 | 0.4037 | 0.3202 |
175
+ | 0.4473 | 17.5 | 10500 | 0.4005 | 0.3246 |
176
+ | 0.4059 | 17.67 | 10600 | 0.3995 | 0.3183 |
177
+ | 0.4122 | 17.83 | 10700 | 0.4039 | 0.3258 |
178
+ | 0.4519 | 18.0 | 10800 | 0.3972 | 0.3267 |
179
+ | 0.3908 | 18.17 | 10900 | 0.3988 | 0.3188 |
180
+ | 0.4182 | 18.33 | 11000 | 0.3943 | 0.3181 |
181
+ | 0.3978 | 18.5 | 11100 | 0.3901 | 0.3191 |
182
+ | 0.4396 | 18.67 | 11200 | 0.3926 | 0.3087 |
183
+ | 0.4098 | 18.83 | 11300 | 0.3844 | 0.3110 |
184
+ | 0.3765 | 19.0 | 11400 | 0.3902 | 0.3180 |
185
+ | 0.3816 | 19.17 | 11500 | 0.3895 | 0.3130 |
186
+ | 0.3959 | 19.33 | 11600 | 0.3927 | 0.3117 |
187
+ | 0.3636 | 19.5 | 11700 | 0.3922 | 0.3108 |
188
+ | 0.3503 | 19.67 | 11800 | 0.3903 | 0.3071 |
189
+ | 0.4234 | 19.83 | 11900 | 0.3922 | 0.3093 |
190
+ | 0.3963 | 20.0 | 12000 | 0.3806 | 0.3071 |
191
+ | 0.3776 | 20.17 | 12100 | 0.3831 | 0.3110 |
192
+ | 0.3729 | 20.33 | 12200 | 0.3791 | 0.3028 |
193
+ | 0.382 | 20.5 | 12300 | 0.3874 | 0.3040 |
194
+ | 0.387 | 20.67 | 12400 | 0.3895 | 0.3057 |
195
+ | 0.3756 | 20.83 | 12500 | 0.3970 | 0.3061 |
196
+ | 0.3511 | 21.0 | 12600 | 0.3884 | 0.3047 |
197
+ | 0.378 | 21.17 | 12700 | 0.3919 | 0.3027 |
198
+ | 0.3687 | 21.33 | 12800 | 0.3930 | 0.3062 |
199
+ | 0.355 | 21.5 | 12900 | 0.3837 | 0.2989 |
200
+ | 0.3381 | 21.67 | 13000 | 0.3835 | 0.2967 |
201
+ | 0.3673 | 21.83 | 13100 | 0.3870 | 0.3023 |
202
+ | 0.3883 | 22.0 | 13200 | 0.3799 | 0.2999 |
203
+ | 0.3513 | 22.17 | 13300 | 0.3783 | 0.3003 |
204
+ | 0.3259 | 22.33 | 13400 | 0.3833 | 0.2962 |
205
+ | 0.3446 | 22.5 | 13500 | 0.3843 | 0.2976 |
206
+ | 0.3519 | 22.67 | 13600 | 0.3822 | 0.2954 |
207
+ | 0.3573 | 22.83 | 13700 | 0.3802 | 0.2932 |
208
+ | 0.3458 | 23.0 | 13800 | 0.3770 | 0.2922 |
209
+ | 0.338 | 23.17 | 13900 | 0.3808 | 0.3002 |
210
+ | 0.3391 | 23.33 | 14000 | 0.3837 | 0.2952 |
211
+ | 0.3343 | 23.5 | 14100 | 0.3988 | 0.2977 |
212
+ | 0.3203 | 23.67 | 14200 | 0.3828 | 0.2947 |
213
+ | 0.3486 | 23.83 | 14300 | 0.3746 | 0.2933 |
214
+ | 0.3779 | 24.0 | 14400 | 0.3722 | 0.2919 |
215
+ | 0.3269 | 24.17 | 14500 | 0.3810 | 0.2946 |
216
+ | 0.3503 | 24.33 | 14600 | 0.3745 | 0.2907 |
217
+ | 0.3313 | 24.5 | 14700 | 0.3825 | 0.2903 |
218
+ | 0.321 | 24.67 | 14800 | 0.3872 | 0.2956 |
219
+ | 0.3327 | 24.83 | 14900 | 0.3812 | 0.2917 |
220
+ | 0.3387 | 25.0 | 15000 | 0.3822 | 0.2897 |
221
+ | 0.3207 | 25.17 | 15100 | 0.3799 | 0.2914 |
222
+ | 0.3308 | 25.33 | 15200 | 0.3916 | 0.2933 |
223
+ | 0.3253 | 25.5 | 15300 | 0.3863 | 0.2901 |
224
+ | 0.3291 | 25.67 | 15400 | 0.3824 | 0.2859 |
225
+ | 0.288 | 25.83 | 15500 | 0.3739 | 0.2884 |
226
+ | 0.3364 | 26.0 | 15600 | 0.3741 | 0.2897 |
227
+ | 0.2987 | 26.17 | 15700 | 0.3826 | 0.2882 |
228
+ | 0.3114 | 26.33 | 15800 | 0.3810 | 0.2908 |
229
+ | 0.3221 | 26.5 | 15900 | 0.3886 | 0.2873 |
230
+ | 0.3283 | 26.67 | 16000 | 0.3850 | 0.2946 |
231
+ | 0.3021 | 26.83 | 16100 | 0.3799 | 0.2879 |
232
+ | 0.3169 | 27.0 | 16200 | 0.3850 | 0.2839 |
233
+ | 0.3048 | 27.17 | 16300 | 0.3777 | 0.2862 |
234
+ | 0.3052 | 27.33 | 16400 | 0.3821 | 0.2862 |
235
+ | 0.2691 | 27.5 | 16500 | 0.3882 | 0.2859 |
236
+ | 0.3335 | 27.67 | 16600 | 0.3847 | 0.2872 |
237
+ | 0.3341 | 27.83 | 16700 | 0.3764 | 0.2869 |
238
+ | 0.3042 | 28.0 | 16800 | 0.3820 | 0.2876 |
239
+ | 0.297 | 28.17 | 16900 | 0.3801 | 0.2847 |
240
+ | 0.3218 | 28.33 | 17000 | 0.3747 | 0.2877 |
241
+ | 0.3227 | 28.5 | 17100 | 0.3794 | 0.2836 |
242
+ | 0.3247 | 28.67 | 17200 | 0.3828 | 0.2877 |
243
+ | 0.2952 | 28.83 | 17300 | 0.3887 | 0.2889 |
244
+ | 0.3078 | 29.0 | 17400 | 0.3803 | 0.2842 |
245
+ | 0.2943 | 29.17 | 17500 | 0.3798 | 0.2839 |
246
+ | 0.2769 | 29.33 | 17600 | 0.3791 | 0.2858 |
247
+ | 0.3152 | 29.5 | 17700 | 0.3839 | 0.2856 |
248
+ | 0.326 | 29.67 | 17800 | 0.3817 | 0.2839 |
249
+ | 0.3102 | 29.83 | 17900 | 0.3795 | 0.2872 |
250
+ | 0.2856 | 30.0 | 18000 | 0.3768 | 0.2851 |
251
+ | 0.2789 | 30.17 | 18100 | 0.3838 | 0.2831 |
252
+ | 0.3096 | 30.33 | 18200 | 0.3756 | 0.2853 |
253
+ | 0.3188 | 30.5 | 18300 | 0.3813 | 0.2839 |
254
+ | 0.3019 | 30.67 | 18400 | 0.3793 | 0.2834 |
255
+ | 0.297 | 30.83 | 18500 | 0.3827 | 0.2853 |
256
+ | 0.2826 | 31.0 | 18600 | 0.3778 | 0.2837 |
257
+ | 0.3096 | 31.17 | 18700 | 0.3833 | 0.2826 |
258
+ | 0.2891 | 31.33 | 18800 | 0.3830 | 0.2832 |
259
+ | 0.2959 | 31.5 | 18900 | 0.3800 | 0.2821 |
260
+ | 0.2818 | 31.67 | 19000 | 0.3767 | 0.2828 |
261
+ | 0.2677 | 31.83 | 19100 | 0.3781 | 0.2831 |
262
+ | 0.2893 | 32.0 | 19200 | 0.3810 | 0.2814 |
263
+ | 0.293 | 32.17 | 19300 | 0.3812 | 0.2789 |
264
+ | 0.3025 | 32.33 | 19400 | 0.3839 | 0.2802 |
265
+ | 0.2589 | 32.5 | 19500 | 0.3807 | 0.2788 |
266
+ | 0.3011 | 32.67 | 19600 | 0.3813 | 0.2803 |
267
+ | 0.301 | 32.83 | 19700 | 0.3824 | 0.2817 |
268
+ | 0.2989 | 33.0 | 19800 | 0.3794 | 0.2828 |
269
+ | 0.3082 | 33.17 | 19900 | 0.3770 | 0.2812 |
270
+ | 0.2806 | 33.33 | 20000 | 0.3787 | 0.2798 |
271
+ | 0.271 | 33.5 | 20100 | 0.3814 | 0.2796 |
272
+ | 0.3318 | 33.67 | 20200 | 0.3764 | 0.2801 |
273
+ | 0.3083 | 33.83 | 20300 | 0.3758 | 0.2789 |
274
+ | 0.2542 | 34.0 | 20400 | 0.3786 | 0.2822 |
275
+ | 0.2795 | 34.17 | 20500 | 0.3760 | 0.2806 |
276
+ | 0.2778 | 34.33 | 20600 | 0.3766 | 0.2814 |
277
+ | 0.2863 | 34.5 | 20700 | 0.3816 | 0.2809 |
278
+ | 0.2902 | 34.67 | 20800 | 0.3792 | 0.2811 |
279
+ | 0.3005 | 34.83 | 20900 | 0.3742 | 0.2807 |
280
+ | 0.2863 | 35.0 | 21000 | 0.3759 | 0.2801 |
281
+ | 0.3005 | 35.17 | 21100 | 0.3747 | 0.2814 |
282
+ | 0.2696 | 35.33 | 21200 | 0.3779 | 0.2816 |
283
+ | 0.326 | 35.5 | 21300 | 0.3741 | 0.2812 |
284
+ | 0.2696 | 35.67 | 21400 | 0.3770 | 0.2803 |
285
+ | 0.2756 | 35.83 | 21500 | 0.3789 | 0.2816 |
286
+ | 0.2648 | 36.0 | 21600 | 0.3802 | 0.2814 |
287
+ | 0.3 | 36.17 | 21700 | 0.3791 | 0.2822 |
288
+ | 0.2695 | 36.33 | 21800 | 0.3801 | 0.2827 |
289
+ | 0.2685 | 36.5 | 21900 | 0.3783 | 0.2813 |
290
+ | 0.2718 | 36.67 | 22000 | 0.3775 | 0.2813 |
291
+ | 0.2982 | 36.83 | 22100 | 0.3780 | 0.2814 |
292
+ | 0.302 | 37.0 | 22200 | 0.3769 | 0.2814 |
293
+ | 0.2885 | 37.17 | 22300 | 0.3774 | 0.2817 |
294
+ | 0.2918 | 37.33 | 22400 | 0.3769 | 0.2821 |
295
+ | 0.2631 | 37.5 | 22500 | 0.3776 | 0.2819 |
296
+ | 0.2854 | 37.67 | 22600 | 0.3768 | 0.2818 |
297
+ | 0.2626 | 37.83 | 22700 | 0.3763 | 0.2803 |
298
+ | 0.311 | 38.0 | 22800 | 0.3756 | 0.2806 |
299
+ | 0.2971 | 38.17 | 22900 | 0.3762 | 0.2807 |
300
+ | 0.2496 | 38.33 | 23000 | 0.3762 | 0.2808 |
301
+ | 0.3004 | 38.5 | 23100 | 0.3758 | 0.2814 |
302
+ | 0.3125 | 38.67 | 23200 | 0.3756 | 0.2814 |
303
+ | 0.272 | 38.83 | 23300 | 0.3758 | 0.2809 |
304
+ | 0.286 | 39.0 | 23400 | 0.3762 | 0.2809 |
305
+ | 0.2562 | 39.17 | 23500 | 0.3762 | 0.2811 |
306
+ | 0.2946 | 39.33 | 23600 | 0.3761 | 0.2812 |
307
+ | 0.3202 | 39.5 | 23700 | 0.3761 | 0.2813 |
308
+ | 0.2806 | 39.67 | 23800 | 0.3760 | 0.2812 |
309
+ | 0.2856 | 39.83 | 23900 | 0.3761 | 0.2803 |
310
+ | 0.2556 | 40.0 | 24000 | 0.3761 | 0.2811 |
311
 
312
 
313
  ### Framework versions
314
 
315
+ - Transformers 4.37.0.dev0
316
+ - Pytorch 2.1.0+cu121
317
+ - Datasets 2.16.0
318
  - Tokenizers 0.15.0
preprocessor_config.json CHANGED
@@ -4,6 +4,7 @@
4
  "feature_size": 1,
5
  "padding_side": "right",
6
  "padding_value": 0.0,
 
7
  "return_attention_mask": true,
8
  "sampling_rate": 16000
9
  }
 
4
  "feature_size": 1,
5
  "padding_side": "right",
6
  "padding_value": 0.0,
7
+ "processor_class": "Wav2Vec2Processor",
8
  "return_attention_mask": true,
9
  "sampling_rate": 16000
10
  }