update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,118 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: apache-2.0
|
3 |
+
tags:
|
4 |
+
- generated_from_trainer
|
5 |
+
datasets:
|
6 |
+
- common_voice
|
7 |
+
model-index:
|
8 |
+
- name: wav2vec2-large-xls-r-300m-nl
|
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-xls-r-300m-nl
|
16 |
+
|
17 |
+
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.
|
18 |
+
It achieves the following results on the evaluation set:
|
19 |
+
- Loss: 0.3923
|
20 |
+
- Wer: 0.1748
|
21 |
+
|
22 |
+
## Model description
|
23 |
+
|
24 |
+
More information needed
|
25 |
+
|
26 |
+
## Intended uses & limitations
|
27 |
+
|
28 |
+
More information needed
|
29 |
+
|
30 |
+
## Training and evaluation data
|
31 |
+
|
32 |
+
More information needed
|
33 |
+
|
34 |
+
## Training procedure
|
35 |
+
|
36 |
+
### Training hyperparameters
|
37 |
+
|
38 |
+
The following hyperparameters were used during training:
|
39 |
+
- learning_rate: 7.5e-05
|
40 |
+
- train_batch_size: 16
|
41 |
+
- eval_batch_size: 8
|
42 |
+
- seed: 42
|
43 |
+
- gradient_accumulation_steps: 2
|
44 |
+
- total_train_batch_size: 32
|
45 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
46 |
+
- lr_scheduler_type: linear
|
47 |
+
- lr_scheduler_warmup_steps: 500
|
48 |
+
- num_epochs: 50
|
49 |
+
- mixed_precision_training: Native AMP
|
50 |
+
|
51 |
+
### Training results
|
52 |
+
|
53 |
+
| Training Loss | Epoch | Step | Validation Loss | Wer |
|
54 |
+
|:-------------:|:-----:|:-----:|:---------------:|:------:|
|
55 |
+
| 1.5787 | 0.89 | 400 | 0.6354 | 0.5643 |
|
56 |
+
| 0.3036 | 1.78 | 800 | 0.3690 | 0.3552 |
|
57 |
+
| 0.188 | 2.67 | 1200 | 0.3239 | 0.2958 |
|
58 |
+
| 0.1434 | 3.56 | 1600 | 0.3093 | 0.2515 |
|
59 |
+
| 0.1245 | 4.44 | 2000 | 0.3024 | 0.2433 |
|
60 |
+
| 0.1095 | 5.33 | 2400 | 0.3249 | 0.2643 |
|
61 |
+
| 0.0979 | 6.22 | 2800 | 0.3191 | 0.2281 |
|
62 |
+
| 0.0915 | 7.11 | 3200 | 0.3152 | 0.2216 |
|
63 |
+
| 0.0829 | 8.0 | 3600 | 0.3419 | 0.2218 |
|
64 |
+
| 0.0777 | 8.89 | 4000 | 0.3432 | 0.2132 |
|
65 |
+
| 0.073 | 9.78 | 4400 | 0.3223 | 0.2131 |
|
66 |
+
| 0.0688 | 10.67 | 4800 | 0.3094 | 0.2152 |
|
67 |
+
| 0.0647 | 11.56 | 5200 | 0.3411 | 0.2152 |
|
68 |
+
| 0.0639 | 12.44 | 5600 | 0.3762 | 0.2135 |
|
69 |
+
| 0.0599 | 13.33 | 6000 | 0.3790 | 0.2137 |
|
70 |
+
| 0.0572 | 14.22 | 6400 | 0.3693 | 0.2118 |
|
71 |
+
| 0.0563 | 15.11 | 6800 | 0.3495 | 0.2139 |
|
72 |
+
| 0.0521 | 16.0 | 7200 | 0.3800 | 0.2023 |
|
73 |
+
| 0.0508 | 16.89 | 7600 | 0.3678 | 0.2033 |
|
74 |
+
| 0.0513 | 17.78 | 8000 | 0.3845 | 0.1987 |
|
75 |
+
| 0.0476 | 18.67 | 8400 | 0.3511 | 0.2037 |
|
76 |
+
| 0.045 | 19.56 | 8800 | 0.3794 | 0.1994 |
|
77 |
+
| 0.044 | 20.44 | 9200 | 0.3525 | 0.2050 |
|
78 |
+
| 0.043 | 21.33 | 9600 | 0.4082 | 0.2007 |
|
79 |
+
| 0.0409 | 22.22 | 10000 | 0.3866 | 0.2004 |
|
80 |
+
| 0.0393 | 23.11 | 10400 | 0.3899 | 0.2008 |
|
81 |
+
| 0.0382 | 24.0 | 10800 | 0.3626 | 0.1951 |
|
82 |
+
| 0.039 | 24.89 | 11200 | 0.3936 | 0.1953 |
|
83 |
+
| 0.0361 | 25.78 | 11600 | 0.4262 | 0.1928 |
|
84 |
+
| 0.0362 | 26.67 | 12000 | 0.3796 | 0.1934 |
|
85 |
+
| 0.033 | 27.56 | 12400 | 0.3616 | 0.1934 |
|
86 |
+
| 0.0321 | 28.44 | 12800 | 0.3742 | 0.1933 |
|
87 |
+
| 0.0325 | 29.33 | 13200 | 0.3582 | 0.1869 |
|
88 |
+
| 0.0309 | 30.22 | 13600 | 0.3717 | 0.1874 |
|
89 |
+
| 0.029 | 31.11 | 14000 | 0.3814 | 0.1894 |
|
90 |
+
| 0.0296 | 32.0 | 14400 | 0.3698 | 0.1877 |
|
91 |
+
| 0.0281 | 32.89 | 14800 | 0.3976 | 0.1899 |
|
92 |
+
| 0.0275 | 33.78 | 15200 | 0.3854 | 0.1858 |
|
93 |
+
| 0.0264 | 34.67 | 15600 | 0.4021 | 0.1889 |
|
94 |
+
| 0.0261 | 35.56 | 16000 | 0.3850 | 0.1830 |
|
95 |
+
| 0.0242 | 36.44 | 16400 | 0.4091 | 0.1878 |
|
96 |
+
| 0.0245 | 37.33 | 16800 | 0.4012 | 0.1846 |
|
97 |
+
| 0.0243 | 38.22 | 17200 | 0.3996 | 0.1833 |
|
98 |
+
| 0.0223 | 39.11 | 17600 | 0.3962 | 0.1815 |
|
99 |
+
| 0.0223 | 40.0 | 18000 | 0.3898 | 0.1832 |
|
100 |
+
| 0.0219 | 40.89 | 18400 | 0.4019 | 0.1822 |
|
101 |
+
| 0.0211 | 41.78 | 18800 | 0.4035 | 0.1809 |
|
102 |
+
| 0.021 | 42.67 | 19200 | 0.3915 | 0.1826 |
|
103 |
+
| 0.0208 | 43.56 | 19600 | 0.3934 | 0.1784 |
|
104 |
+
| 0.0188 | 44.44 | 20000 | 0.3912 | 0.1787 |
|
105 |
+
| 0.0195 | 45.33 | 20400 | 0.3989 | 0.1766 |
|
106 |
+
| 0.0186 | 46.22 | 20800 | 0.3887 | 0.1773 |
|
107 |
+
| 0.0188 | 47.11 | 21200 | 0.3982 | 0.1758 |
|
108 |
+
| 0.0175 | 48.0 | 21600 | 0.3933 | 0.1755 |
|
109 |
+
| 0.0172 | 48.89 | 22000 | 0.3921 | 0.1749 |
|
110 |
+
| 0.0187 | 49.78 | 22400 | 0.3923 | 0.1748 |
|
111 |
+
|
112 |
+
|
113 |
+
### Framework versions
|
114 |
+
|
115 |
+
- Transformers 4.16.0.dev0
|
116 |
+
- Pytorch 1.10.1+cu102
|
117 |
+
- Datasets 1.17.1.dev0
|
118 |
+
- Tokenizers 0.11.0
|