File size: 5,343 Bytes
d84f4ed
ef3cdad
 
61752de
d84f4ed
ef3cdad
 
d84f4ed
f165be0
 
 
d84f4ed
f165be0
 
d84f4ed
f165be0
 
 
 
 
 
 
 
 
 
 
 
25de0cf
f165be0
 
25de0cf
f165be0
 
 
 
 
 
 
 
 
 
25de0cf
f165be0
 
25de0cf
d84f4ed
 
 
 
f165be0
 
 
 
 
ef3cdad
 
d84f4ed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
55a2553
628520f
 
d84f4ed
074846f
628520f
d84f4ed
 
55a2553
 
d84f4ed
 
 
 
55a2553
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d84f4ed
 
 
 
628520f
 
 
d84f4ed
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
134
135
136
137
138
139
140
141
142
143
144
---
language:
- sv-SE
license: cc0-1.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- sv
- robust-speech-event
- model_for_talk
datasets:
- mozilla-foundation/common_voice_8_0
- marinone94/nst_sv
model-index:
- name: XLS-R-300M - Swedish
  results:
  - task: 
      name: Automatic Speech Recognition 
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_8_0
      type: mozilla-foundation/common_voice_8_0
      args: sv-SE
    metrics:
       - name: Test WER
         type: wer
         value: 9.44
       - name: Test CER
         type: cer
         value: 3.29
  - task: 
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: speech-recognition-community-v2/dev_data
      type: speech-recognition-community-v2/dev_data
      args: sv
    metrics:
       - name: Test WER
         type: wer
         value: 19.63
       - name: Test CER
         type: cer
         value: 9.06
---

# 

This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on 2 epochs of the MARINONE94/NST_SV - SV dataset (80% random split with seed 42 as the dataset for now has only the "train" split), and then on 50 epochs of the the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset ("train+validation" split).
See run.sh to have a complete overview of all the training steps.
NOTE: the first training for now didn't work as expected, so it might be useless or even degrade performance. Further investigation and development is needed.

It achieves the following results on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE "test" set, without any language model:
- Loss: 0.1497
- Wer: 0.1261

## 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: 0.00025
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3533        | 1.1   | 100  | 3.2807          | 1.0    |
| 3.1709        | 2.2   | 200  | 3.1325          | 1.0    |
| 3.0573        | 3.3   | 300  | 3.0615          | 1.0    |
| 3.0314        | 4.39  | 400  | 3.0990          | 1.0    |
| 3.0129        | 5.49  | 500  | 3.0400          | 1.0    |
| 2.9964        | 6.59  | 600  | 2.9990          | 1.0    |
| 2.9602        | 7.69  | 700  | 2.9620          | 1.0    |
| 2.8756        | 8.79  | 800  | 2.7302          | 1.0    |
| 2.2931        | 9.89  | 900  | 1.5058          | 0.9776 |
| 1.8427        | 10.98 | 1000 | 0.9155          | 0.7832 |
| 1.4286        | 12.09 | 1100 | 0.4075          | 0.3796 |
| 1.2229        | 13.19 | 1200 | 0.2893          | 0.2652 |
| 1.1106        | 14.28 | 1300 | 0.2469          | 0.2254 |
| 1.0663        | 15.38 | 1400 | 0.2219          | 0.1973 |
| 1.0667        | 16.48 | 1500 | 0.2129          | 0.1894 |
| 1.0193        | 17.58 | 1600 | 0.1991          | 0.1789 |
| 0.9816        | 18.68 | 1700 | 0.1940          | 0.1801 |
| 0.9814        | 19.78 | 1800 | 0.1860          | 0.1667 |
| 0.9787        | 20.87 | 1900 | 0.1888          | 0.1642 |
| 0.9699        | 21.97 | 2000 | 0.1875          | 0.1704 |
| 0.9616        | 23.08 | 2100 | 0.1802          | 0.1617 |
| 0.9378        | 24.17 | 2200 | 0.1793          | 0.1577 |
| 0.888         | 25.27 | 2300 | 0.1764          | 0.1545 |
| 0.8942        | 26.37 | 2400 | 0.1674          | 0.1492 |
| 0.8701        | 27.47 | 2500 | 0.1739          | 0.1512 |
| 0.8555        | 28.57 | 2600 | 0.1690          | 0.1446 |
| 0.8513        | 29.67 | 2700 | 0.1649          | 0.1477 |
| 0.8659        | 30.77 | 2800 | 0.1637          | 0.1422 |
| 0.8419        | 31.86 | 2900 | 0.1614          | 0.1397 |
| 0.8491        | 32.96 | 3000 | 0.1595          | 0.1401 |
| 0.8395        | 34.07 | 3100 | 0.1607          | 0.1376 |
| 0.83          | 35.16 | 3200 | 0.1538          | 0.1379 |
| 0.7835        | 36.26 | 3300 | 0.1602          | 0.1408 |
| 0.7703        | 37.36 | 3400 | 0.1601          | 0.1369 |
| 0.7474        | 38.46 | 3500 | 0.1514          | 0.1342 |
| 0.7719        | 39.56 | 3600 | 0.1593          | 0.1353 |
| 0.7638        | 40.66 | 3700 | 0.1536          | 0.1338 |
| 0.771         | 41.75 | 3800 | 0.1531          | 0.1317 |
| 0.7594        | 42.85 | 3900 | 0.1498          | 0.1288 |
| 0.7383        | 43.95 | 4000 | 0.1527          | 0.1300 |
| 0.7565        | 45.05 | 4100 | 0.1482          | 0.1289 |
| 0.7697        | 46.15 | 4200 | 0.1495          | 0.1272 |
| 0.7194        | 47.25 | 4300 | 0.1493          | 0.1269 |
| 0.7479        | 48.35 | 4400 | 0.1490          | 0.1276 |
| 0.7132        | 49.45 | 4500 | 0.1501          | 0.1265 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0