Hugging Face's logo

alinerodrigues
/
wav2vec2-large-xlsr-coraa-exp-13

PyTorch
wav2vec2
Generated from Trainer
Model card Files Community
alinerodrigues commited on
Commit
3718a54
·
1 Parent(s): 317e465

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +206 -0
README.md ADDED
@@ -0,0 +1,206 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - wer
7
+ model-index:
8
+ - name: wav2vec2-large-xlsr-coraa-exp-13
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ # wav2vec2-large-xlsr-coraa-exp-13
16
+
17
+ This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
18
+ It achieves the following results on the evaluation set:
19
+ - Loss: 0.5682
20
+ - Wer: 0.3462
21
+ - Cer: 0.1810
22
+
23
+ ## Model description
24
+
25
+ More information needed
26
+
27
+ ## Intended uses & limitations
28
+
29
+ More information needed
30
+
31
+ ## Training and evaluation data
32
+
33
+ More information needed
34
+
35
+ ## Training procedure
36
+
37
+ ### Training hyperparameters
38
+
39
+ The following hyperparameters were used during training:
40
+ - learning_rate: 3e-05
41
+ - train_batch_size: 16
42
+ - eval_batch_size: 8
43
+ - seed: 42
44
+ - gradient_accumulation_steps: 2
45
+ - total_train_batch_size: 32
46
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
47
+ - lr_scheduler_type: linear
48
+ - num_epochs: 150
49
+ - mixed_precision_training: Native AMP
50
+
51
+ ### Training results
52
+
53
+ | Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
54
+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
55
+ | 38.268 | 1.0 | 14 | 32.0584 | 1.0 | 0.9430 |
56
+ | 38.268 | 2.0 | 28 | 10.3763 | 1.0 | 0.9619 |
57
+ | 38.268 | 3.0 | 42 | 4.8976 | 1.0 | 0.9619 |
58
+ | 38.268 | 4.0 | 56 | 4.0406 | 1.0 | 0.9619 |
59
+ | 38.268 | 5.0 | 70 | 3.7470 | 1.0 | 0.9619 |
60
+ | 38.268 | 6.0 | 84 | 3.5903 | 1.0 | 0.9619 |
61
+ | 38.268 | 7.0 | 98 | 3.4750 | 1.0 | 0.9619 |
62
+ | 10.1654 | 8.0 | 112 | 3.3406 | 1.0 | 0.9619 |
63
+ | 10.1654 | 9.0 | 126 | 3.2267 | 1.0 | 0.9619 |
64
+ | 10.1654 | 10.0 | 140 | 3.1887 | 1.0 | 0.9619 |
65
+ | 10.1654 | 11.0 | 154 | 3.1301 | 1.0 | 0.9619 |
66
+ | 10.1654 | 12.0 | 168 | 3.1046 | 1.0 | 0.9619 |
67
+ | 10.1654 | 13.0 | 182 | 3.0909 | 1.0 | 0.9619 |
68
+ | 10.1654 | 14.0 | 196 | 3.0603 | 1.0 | 0.9619 |
69
+ | 3.0823 | 15.0 | 210 | 3.0584 | 1.0 | 0.9619 |
70
+ | 3.0823 | 16.0 | 224 | 3.0485 | 1.0 | 0.9619 |
71
+ | 3.0823 | 17.0 | 238 | 3.0464 | 1.0 | 0.9619 |
72
+ | 3.0823 | 18.0 | 252 | 3.0242 | 1.0 | 0.9619 |
73
+ | 3.0823 | 19.0 | 266 | 3.0237 | 1.0 | 0.9619 |
74
+ | 3.0823 | 20.0 | 280 | 3.0304 | 1.0 | 0.9619 |
75
+ | 3.0823 | 21.0 | 294 | 3.0119 | 1.0 | 0.9619 |
76
+ | 2.9562 | 22.0 | 308 | 3.0148 | 1.0 | 0.9619 |
77
+ | 2.9562 | 23.0 | 322 | 3.0061 | 1.0 | 0.9619 |
78
+ | 2.9562 | 24.0 | 336 | 3.0042 | 1.0 | 0.9619 |
79
+ | 2.9562 | 25.0 | 350 | 3.0033 | 1.0 | 0.9619 |
80
+ | 2.9562 | 26.0 | 364 | 3.0029 | 1.0 | 0.9619 |
81
+ | 2.9562 | 27.0 | 378 | 3.0082 | 1.0 | 0.9619 |
82
+ | 2.9562 | 28.0 | 392 | 2.9956 | 1.0 | 0.9619 |
83
+ | 2.9262 | 29.0 | 406 | 2.9948 | 1.0 | 0.9619 |
84
+ | 2.9262 | 30.0 | 420 | 2.9982 | 1.0 | 0.9619 |
85
+ | 2.9262 | 31.0 | 434 | 2.9962 | 1.0 | 0.9619 |
86
+ | 2.9262 | 32.0 | 448 | 2.9931 | 1.0 | 0.9619 |
87
+ | 2.9262 | 33.0 | 462 | 2.9809 | 1.0 | 0.9619 |
88
+ | 2.9262 | 34.0 | 476 | 2.9804 | 1.0 | 0.9619 |
89
+ | 2.9262 | 35.0 | 490 | 2.9742 | 1.0 | 0.9619 |
90
+ | 2.9125 | 36.0 | 504 | 2.9522 | 1.0 | 0.9619 |
91
+ | 2.9125 | 37.0 | 518 | 2.9015 | 1.0 | 0.9619 |
92
+ | 2.9125 | 38.0 | 532 | 2.8522 | 1.0 | 0.9619 |
93
+ | 2.9125 | 39.0 | 546 | 2.8285 | 1.0 | 0.9619 |
94
+ | 2.9125 | 40.0 | 560 | 2.7294 | 1.0 | 0.9615 |
95
+ | 2.9125 | 41.0 | 574 | 2.6491 | 1.0 | 0.9605 |
96
+ | 2.9125 | 42.0 | 588 | 2.4883 | 1.0 | 0.8950 |
97
+ | 2.7205 | 43.0 | 602 | 2.3631 | 1.0 | 0.8365 |
98
+ | 2.7205 | 44.0 | 616 | 2.0546 | 1.0 | 0.6074 |
99
+ | 2.7205 | 45.0 | 630 | 1.7867 | 1.0 | 0.5148 |
100
+ | 2.7205 | 46.0 | 644 | 1.5453 | 1.0 | 0.4532 |
101
+ | 2.7205 | 47.0 | 658 | 1.3554 | 0.9990 | 0.4064 |
102
+ | 2.7205 | 48.0 | 672 | 1.2016 | 0.9829 | 0.3670 |
103
+ | 2.7205 | 49.0 | 686 | 1.0777 | 0.8805 | 0.3167 |
104
+ | 1.6469 | 50.0 | 700 | 0.9790 | 0.7030 | 0.2594 |
105
+ | 1.6469 | 51.0 | 714 | 0.8962 | 0.5270 | 0.2224 |
106
+ | 1.6469 | 52.0 | 728 | 0.8429 | 0.4974 | 0.2176 |
107
+ | 1.6469 | 53.0 | 742 | 0.8159 | 0.4659 | 0.2089 |
108
+ | 1.6469 | 54.0 | 756 | 0.7980 | 0.4512 | 0.2066 |
109
+ | 1.6469 | 55.0 | 770 | 0.7541 | 0.4441 | 0.2044 |
110
+ | 1.6469 | 56.0 | 784 | 0.7299 | 0.4273 | 0.2015 |
111
+ | 1.6469 | 57.0 | 798 | 0.7078 | 0.4092 | 0.1964 |
112
+ | 0.7997 | 58.0 | 812 | 0.7079 | 0.4110 | 0.1973 |
113
+ | 0.7997 | 59.0 | 826 | 0.6861 | 0.4137 | 0.1983 |
114
+ | 0.7997 | 60.0 | 840 | 0.7035 | 0.4011 | 0.1975 |
115
+ | 0.7997 | 61.0 | 854 | 0.6676 | 0.4000 | 0.1942 |
116
+ | 0.7997 | 62.0 | 868 | 0.6562 | 0.3980 | 0.1937 |
117
+ | 0.7997 | 63.0 | 882 | 0.6580 | 0.3850 | 0.1911 |
118
+ | 0.7997 | 64.0 | 896 | 0.6643 | 0.3911 | 0.1925 |
119
+ | 0.5379 | 65.0 | 910 | 0.6532 | 0.3929 | 0.1928 |
120
+ | 0.5379 | 66.0 | 924 | 0.6483 | 0.3866 | 0.1906 |
121
+ | 0.5379 | 67.0 | 938 | 0.6267 | 0.3757 | 0.1870 |
122
+ | 0.5379 | 68.0 | 952 | 0.6296 | 0.3793 | 0.1880 |
123
+ | 0.5379 | 69.0 | 966 | 0.6415 | 0.3785 | 0.1902 |
124
+ | 0.5379 | 70.0 | 980 | 0.6227 | 0.3746 | 0.1885 |
125
+ | 0.5379 | 71.0 | 994 | 0.6213 | 0.3738 | 0.1878 |
126
+ | 0.4372 | 72.0 | 1008 | 0.6110 | 0.3726 | 0.1872 |
127
+ | 0.4372 | 73.0 | 1022 | 0.6019 | 0.3696 | 0.1862 |
128
+ | 0.4372 | 74.0 | 1036 | 0.6037 | 0.3722 | 0.1867 |
129
+ | 0.4372 | 75.0 | 1050 | 0.5994 | 0.3657 | 0.1881 |
130
+ | 0.4372 | 76.0 | 1064 | 0.6083 | 0.3704 | 0.1881 |
131
+ | 0.4372 | 77.0 | 1078 | 0.5838 | 0.3696 | 0.1865 |
132
+ | 0.4372 | 78.0 | 1092 | 0.5795 | 0.3718 | 0.1855 |
133
+ | 0.3912 | 79.0 | 1106 | 0.6201 | 0.3714 | 0.1877 |
134
+ | 0.3912 | 80.0 | 1120 | 0.5915 | 0.3661 | 0.1854 |
135
+ | 0.3912 | 81.0 | 1134 | 0.5894 | 0.3651 | 0.1843 |
136
+ | 0.3912 | 82.0 | 1148 | 0.5994 | 0.3681 | 0.1859 |
137
+ | 0.3912 | 83.0 | 1162 | 0.6001 | 0.3655 | 0.1864 |
138
+ | 0.3912 | 84.0 | 1176 | 0.6008 | 0.3653 | 0.1865 |
139
+ | 0.3912 | 85.0 | 1190 | 0.5770 | 0.3602 | 0.1832 |
140
+ | 0.3485 | 86.0 | 1204 | 0.5905 | 0.3566 | 0.1836 |
141
+ | 0.3485 | 87.0 | 1218 | 0.5810 | 0.3580 | 0.1828 |
142
+ | 0.3485 | 88.0 | 1232 | 0.5765 | 0.3584 | 0.1830 |
143
+ | 0.3485 | 89.0 | 1246 | 0.5902 | 0.3641 | 0.1845 |
144
+ | 0.3485 | 90.0 | 1260 | 0.5812 | 0.3614 | 0.1831 |
145
+ | 0.3485 | 91.0 | 1274 | 0.5966 | 0.3586 | 0.1844 |
146
+ | 0.3485 | 92.0 | 1288 | 0.5686 | 0.3557 | 0.1822 |
147
+ | 0.3234 | 93.0 | 1302 | 0.5839 | 0.3553 | 0.1828 |
148
+ | 0.3234 | 94.0 | 1316 | 0.5765 | 0.3553 | 0.1820 |
149
+ | 0.3234 | 95.0 | 1330 | 0.5780 | 0.3566 | 0.1820 |
150
+ | 0.3234 | 96.0 | 1344 | 0.5862 | 0.3596 | 0.1834 |
151
+ | 0.3234 | 97.0 | 1358 | 0.5702 | 0.3555 | 0.1821 |
152
+ | 0.3234 | 98.0 | 1372 | 0.5787 | 0.3547 | 0.1821 |
153
+ | 0.3234 | 99.0 | 1386 | 0.5767 | 0.3531 | 0.1824 |
154
+ | 0.2803 | 100.0 | 1400 | 0.5778 | 0.3570 | 0.1818 |
155
+ | 0.2803 | 101.0 | 1414 | 0.5759 | 0.3543 | 0.1817 |
156
+ | 0.2803 | 102.0 | 1428 | 0.5838 | 0.3572 | 0.1824 |
157
+ | 0.2803 | 103.0 | 1442 | 0.5696 | 0.3541 | 0.1815 |
158
+ | 0.2803 | 104.0 | 1456 | 0.5724 | 0.3541 | 0.1820 |
159
+ | 0.2803 | 105.0 | 1470 | 0.5698 | 0.3543 | 0.1820 |
160
+ | 0.2803 | 106.0 | 1484 | 0.5727 | 0.3523 | 0.1816 |
161
+ | 0.2803 | 107.0 | 1498 | 0.5609 | 0.3511 | 0.1809 |
162
+ | 0.2718 | 108.0 | 1512 | 0.5655 | 0.3497 | 0.1807 |
163
+ | 0.2718 | 109.0 | 1526 | 0.5761 | 0.3535 | 0.1816 |
164
+ | 0.2718 | 110.0 | 1540 | 0.5753 | 0.3523 | 0.1815 |
165
+ | 0.2718 | 111.0 | 1554 | 0.5703 | 0.3503 | 0.1805 |
166
+ | 0.2718 | 112.0 | 1568 | 0.5623 | 0.3470 | 0.1802 |
167
+ | 0.2718 | 113.0 | 1582 | 0.5723 | 0.3511 | 0.1813 |
168
+ | 0.2718 | 114.0 | 1596 | 0.5608 | 0.3486 | 0.1803 |
169
+ | 0.2614 | 115.0 | 1610 | 0.5613 | 0.3511 | 0.1809 |
170
+ | 0.2614 | 116.0 | 1624 | 0.5742 | 0.3533 | 0.1817 |
171
+ | 0.2614 | 117.0 | 1638 | 0.5715 | 0.3523 | 0.1817 |
172
+ | 0.2614 | 118.0 | 1652 | 0.5695 | 0.3533 | 0.1817 |
173
+ | 0.2614 | 119.0 | 1666 | 0.5713 | 0.3531 | 0.1825 |
174
+ | 0.2614 | 120.0 | 1680 | 0.5664 | 0.3533 | 0.1821 |
175
+ | 0.2614 | 121.0 | 1694 | 0.5716 | 0.3531 | 0.1822 |
176
+ | 0.2463 | 122.0 | 1708 | 0.5680 | 0.3476 | 0.1810 |
177
+ | 0.2463 | 123.0 | 1722 | 0.5760 | 0.3527 | 0.1817 |
178
+ | 0.2463 | 124.0 | 1736 | 0.5561 | 0.3456 | 0.1803 |
179
+ | 0.2463 | 125.0 | 1750 | 0.5698 | 0.3478 | 0.1812 |
180
+ | 0.2463 | 126.0 | 1764 | 0.5667 | 0.3482 | 0.1811 |
181
+ | 0.2463 | 127.0 | 1778 | 0.5677 | 0.3478 | 0.1813 |
182
+ | 0.2463 | 128.0 | 1792 | 0.5681 | 0.3446 | 0.1805 |
183
+ | 0.2477 | 129.0 | 1806 | 0.5666 | 0.3470 | 0.1809 |
184
+ | 0.2477 | 130.0 | 1820 | 0.5696 | 0.3458 | 0.1804 |
185
+ | 0.2477 | 131.0 | 1834 | 0.5704 | 0.3478 | 0.1810 |
186
+ | 0.2477 | 132.0 | 1848 | 0.5656 | 0.3470 | 0.1808 |
187
+ | 0.2477 | 133.0 | 1862 | 0.5697 | 0.3472 | 0.1807 |
188
+ | 0.2477 | 134.0 | 1876 | 0.5716 | 0.3472 | 0.1810 |
189
+ | 0.2477 | 135.0 | 1890 | 0.5742 | 0.3484 | 0.1810 |
190
+ | 0.221 | 136.0 | 1904 | 0.5671 | 0.3472 | 0.1807 |
191
+ | 0.221 | 137.0 | 1918 | 0.5670 | 0.3462 | 0.1810 |
192
+ | 0.221 | 138.0 | 1932 | 0.5675 | 0.3460 | 0.1810 |
193
+ | 0.221 | 139.0 | 1946 | 0.5704 | 0.3462 | 0.1810 |
194
+ | 0.221 | 140.0 | 1960 | 0.5675 | 0.3458 | 0.1808 |
195
+ | 0.221 | 141.0 | 1974 | 0.5618 | 0.3444 | 0.1800 |
196
+ | 0.221 | 142.0 | 1988 | 0.5633 | 0.3454 | 0.1800 |
197
+ | 0.2217 | 143.0 | 2002 | 0.5664 | 0.3456 | 0.1807 |
198
+ | 0.2217 | 144.0 | 2016 | 0.5682 | 0.3462 | 0.1810 |
199
+
200
+
201
+ ### Framework versions
202
+
203
+ - Transformers 4.28.0
204
+ - Pytorch 2.4.1+cu121
205
+ - Datasets 3.2.0
206
+ - Tokenizers 0.13.3