File size: 5,599 Bytes
afb3566
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
---
language:
- sr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- xlsr-fine-tuning-week
datasets:
- common_voice
- name: Serbian comodoro Wav2Vec2 XLSR 300M CV8
  results:
  - task:
      name: Automatic Speech Recognition 
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 8
      type: mozilla-foundation/common_voice_8_0
      args: hsb
    metrics:
       - name: Test WER
         type: wer
         value: 48.3
       - name: Test CER
         type: cer
         value: 18.5
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Serbian wav2vec2-xls-r-300m-sr-cv8

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.
It achieves the following results on the evaluation set:
- Loss: 1.7302
- Wer: 0.4825
- Cer: 0.1847

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 800
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 5.6536        | 15.0  | 1200  | 2.9744          | 1.0    | 1.0    |
| 2.7935        | 30.0  | 2400  | 1.6613          | 0.8998 | 0.4670 |
| 1.6538        | 45.0  | 3600  | 0.9248          | 0.6918 | 0.2699 |
| 1.2446        | 60.0  | 4800  | 0.9151          | 0.6452 | 0.2398 |
| 1.0766        | 75.0  | 6000  | 0.9110          | 0.5995 | 0.2207 |
| 0.9548        | 90.0  | 7200  | 1.0273          | 0.5921 | 0.2149 |
| 0.8919        | 105.0 | 8400  | 0.9929          | 0.5646 | 0.2117 |
| 0.8185        | 120.0 | 9600  | 1.0850          | 0.5483 | 0.2069 |
| 0.7692        | 135.0 | 10800 | 1.1001          | 0.5394 | 0.2055 |
| 0.7249        | 150.0 | 12000 | 1.1018          | 0.5380 | 0.1958 |
| 0.6786        | 165.0 | 13200 | 1.1344          | 0.5114 | 0.1941 |
| 0.6432        | 180.0 | 14400 | 1.1516          | 0.5054 | 0.1905 |
| 0.6009        | 195.0 | 15600 | 1.3149          | 0.5324 | 0.1991 |
| 0.5773        | 210.0 | 16800 | 1.2468          | 0.5124 | 0.1903 |
| 0.559         | 225.0 | 18000 | 1.2186          | 0.4956 | 0.1922 |
| 0.5298        | 240.0 | 19200 | 1.4483          | 0.5333 | 0.2085 |
| 0.5136        | 255.0 | 20400 | 1.2871          | 0.4802 | 0.1846 |
| 0.4824        | 270.0 | 21600 | 1.2891          | 0.4974 | 0.1885 |
| 0.4669        | 285.0 | 22800 | 1.3283          | 0.4942 | 0.1878 |
| 0.4511        | 300.0 | 24000 | 1.4502          | 0.5002 | 0.1994 |
| 0.4337        | 315.0 | 25200 | 1.4714          | 0.5035 | 0.1911 |
| 0.4221        | 330.0 | 26400 | 1.4971          | 0.5124 | 0.1962 |
| 0.3994        | 345.0 | 27600 | 1.4473          | 0.5007 | 0.1920 |
| 0.3892        | 360.0 | 28800 | 1.3904          | 0.4937 | 0.1887 |
| 0.373         | 375.0 | 30000 | 1.4971          | 0.4946 | 0.1902 |
| 0.3657        | 390.0 | 31200 | 1.4208          | 0.4900 | 0.1821 |
| 0.3559        | 405.0 | 32400 | 1.4648          | 0.4895 | 0.1835 |
| 0.3476        | 420.0 | 33600 | 1.4848          | 0.4946 | 0.1829 |
| 0.3276        | 435.0 | 34800 | 1.5597          | 0.4979 | 0.1873 |
| 0.3193        | 450.0 | 36000 | 1.7329          | 0.5040 | 0.1980 |
| 0.3078        | 465.0 | 37200 | 1.6379          | 0.4937 | 0.1882 |
| 0.3058        | 480.0 | 38400 | 1.5878          | 0.4942 | 0.1921 |
| 0.2987        | 495.0 | 39600 | 1.5590          | 0.4811 | 0.1846 |
| 0.2931        | 510.0 | 40800 | 1.6001          | 0.4825 | 0.1849 |
| 0.276         | 525.0 | 42000 | 1.7388          | 0.4942 | 0.1918 |
| 0.2702        | 540.0 | 43200 | 1.7037          | 0.4839 | 0.1866 |
| 0.2619        | 555.0 | 44400 | 1.6704          | 0.4755 | 0.1840 |
| 0.262         | 570.0 | 45600 | 1.6042          | 0.4751 | 0.1865 |
| 0.2528        | 585.0 | 46800 | 1.6402          | 0.4821 | 0.1865 |
| 0.2442        | 600.0 | 48000 | 1.6693          | 0.4886 | 0.1862 |
| 0.244         | 615.0 | 49200 | 1.6203          | 0.4765 | 0.1792 |
| 0.2388        | 630.0 | 50400 | 1.6829          | 0.4830 | 0.1828 |
| 0.2362        | 645.0 | 51600 | 1.8100          | 0.4928 | 0.1888 |
| 0.2224        | 660.0 | 52800 | 1.7746          | 0.4932 | 0.1899 |
| 0.2218        | 675.0 | 54000 | 1.7752          | 0.4946 | 0.1901 |
| 0.2201        | 690.0 | 55200 | 1.6775          | 0.4788 | 0.1844 |
| 0.2147        | 705.0 | 56400 | 1.7085          | 0.4844 | 0.1851 |
| 0.2103        | 720.0 | 57600 | 1.7624          | 0.4848 | 0.1864 |
| 0.2101        | 735.0 | 58800 | 1.7213          | 0.4783 | 0.1835 |
| 0.1983        | 750.0 | 60000 | 1.7452          | 0.4848 | 0.1856 |
| 0.2015        | 765.0 | 61200 | 1.7525          | 0.4872 | 0.1869 |
| 0.1969        | 780.0 | 62400 | 1.7443          | 0.4844 | 0.1852 |
| 0.2043        | 795.0 | 63600 | 1.7302          | 0.4825 | 0.1847 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0