File size: 4,515 Bytes
4e677f2
7b86b4e
 
5617a47
4e677f2
 
 
7b86b4e
4e677f2
 
 
 
dff3db0
2acfe57
 
 
 
72470a2
2acfe57
 
 
 
 
5617a47
4e677f2
 
5617a47
4e677f2
 
 
927b33b
4e677f2
5617a47
4e677f2
5617a47
 
 
4e677f2
 
7b86b4e
4e677f2
 
 
7b86b4e
4e677f2
 
 
7b86b4e
4e677f2
 
 
 
 
7b86b4e
 
5617a47
 
 
7b86b4e
5617a47
 
7b86b4e
 
5617a47
 
7b86b4e
4e677f2
 
 
5617a47
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7b86b4e
4e677f2
 
 
5617a47
 
 
 
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
---
language:
- uk
license: mit
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-uk
  results:
  - task: 
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice uk
      type: common_voice
      args: uk
    metrics:
       - name: Test WER
         type: wer
         value: 12.22
---


<!-- 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. -->

# wav2vec2-xls-r-300m-uk

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.
It achieves the following results on the evaluation set:
- Loss: 0.0927
- Wer: 0.1222
- Cer: 0.0204

## 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: 3e-05
- train_batch_size: 40
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 9.0008        | 1.68  | 200  | 1.0    | 3.7590          | 1.0    |
| 3.4972        | 3.36  | 400  | 1.0    | 3.3933          | 1.0    |
| 3.3432        | 5.04  | 600  | 1.0    | 3.2617          | 1.0    |
| 3.2421        | 6.72  | 800  | 1.0    | 3.0712          | 1.0    |
| 1.9839        | 7.68  | 1000 | 0.1400 | 0.7204          | 0.6561 |
| 0.8017        | 9.36  | 1200 | 0.0766 | 0.3734          | 0.4159 |
| 0.5554        | 11.04 | 1400 | 0.0583 | 0.2621          | 0.3237 |
| 0.4309        | 12.68 | 1600 | 0.0486 | 0.2085          | 0.2753 |
| 0.3697        | 14.36 | 1800 | 0.0421 | 0.1746          | 0.2427 |
| 0.3293        | 16.04 | 2000 | 0.0388 | 0.1597          | 0.2243 |
| 0.2934        | 17.72 | 2200 | 0.0358 | 0.1428          | 0.2083 |
| 0.2704        | 19.4  | 2400 | 0.0333 | 0.1326          | 0.1949 |
| 0.2547        | 21.08 | 2600 | 0.0322 | 0.1255          | 0.1882 |
| 0.2366        | 22.76 | 2800 | 0.0309 | 0.1211          | 0.1815 |
| 0.2183        | 24.44 | 3000 | 0.0294 | 0.1159          | 0.1727 |
| 0.2115        | 26.13 | 3200 | 0.0280 | 0.1117          | 0.1661 |
| 0.1968        | 27.8  | 3400 | 0.0274 | 0.1063          | 0.1622 |
| 0.1922        | 29.48 | 3600 | 0.0269 | 0.1082          | 0.1598 |
| 0.1847        | 31.17 | 3800 | 0.0260 | 0.1061          | 0.1550 |
| 0.1715        | 32.84 | 4000 | 0.0252 | 0.1014          | 0.1496 |
| 0.1689        | 34.53 | 4200 | 0.0250 | 0.1012          | 0.1492 |
| 0.1655        | 36.21 | 4400 | 0.0243 | 0.0999          | 0.1450 |
| 0.1585        | 37.88 | 4600 | 0.0239 | 0.0967          | 0.1432 |
| 0.1492        | 39.57 | 4800 | 0.0237 | 0.0978          | 0.1421 |
| 0.1491        | 41.25 | 5000 | 0.0236 | 0.0963          | 0.1412 |
| 0.1453        | 42.93 | 5200 | 0.0230 | 0.0979          | 0.1373 |
| 0.1386        | 44.61 | 5400 | 0.0227 | 0.0959          | 0.1353 |
| 0.1387        | 46.29 | 5600 | 0.0226 | 0.0927          | 0.1355 |
| 0.1329        | 47.97 | 5800 | 0.0224 | 0.0951          | 0.1341 |
| 0.1295        | 49.65 | 6000 | 0.0219 | 0.0950          | 0.1306 |
| 0.1287        | 51.33 | 6200 | 0.0216 | 0.0937          | 0.1290 |
| 0.1277        | 53.02 | 6400 | 0.0215 | 0.0963          | 0.1294 |
| 0.1201        | 54.69 | 6600 | 0.0213 | 0.0959          | 0.1282 |
| 0.1199        | 56.38 | 6800 | 0.0215 | 0.0944          | 0.1286 |
| 0.1221        | 58.06 | 7000 | 0.0209 | 0.0938          | 0.1249 |
| 0.1145        | 59.68 | 7200 | 0.0208 | 0.0941          | 0.1254 |
| 0.1143        | 61.36 | 7400 | 0.0209 | 0.0941          | 0.1249 |
| 0.1143        | 63.04 | 7600 | 0.0209 | 0.0940          | 0.1248 |
| 0.1137        | 64.72 | 7800 | 0.0205 | 0.0931          | 0.1234 |
| 0.1125        | 66.4  | 8000 | 0.0204 | 0.0927          | 0.1222 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2