File size: 2,851 Bytes
9150b34
 
22d6da8
 
 
 
 
 
 
9150b34
 
22d6da8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-E30_freq_speed_pause
  results: []
---

<!-- 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-E30_freq_speed_pause

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0912
- Cer: 46.1231

## 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: 8
- 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: 50
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Cer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 31.7937       | 0.1289 | 200  | 5.1147          | 100.0   |
| 4.9653        | 0.2579 | 400  | 4.6684          | 100.0   |
| 4.8114        | 0.3868 | 600  | 4.6765          | 100.0   |
| 4.7658        | 0.5158 | 800  | 4.6123          | 97.7150 |
| 4.6791        | 0.6447 | 1000 | 4.6076          | 98.9544 |
| 4.6438        | 0.7737 | 1200 | 4.6205          | 97.6974 |
| 4.5903        | 0.9026 | 1400 | 4.4614          | 97.8442 |
| 4.439         | 1.0316 | 1600 | 4.4028          | 98.2848 |
| 4.1968        | 1.1605 | 1800 | 4.2323          | 94.1612 |
| 3.8917        | 1.2895 | 2000 | 3.8326          | 78.5127 |
| 3.5148        | 1.4184 | 2200 | 3.6092          | 70.7119 |
| 3.2601        | 1.5474 | 2400 | 3.3938          | 71.4873 |
| 3.0276        | 1.6763 | 2600 | 3.1059          | 64.2094 |
| 2.8883        | 1.8053 | 2800 | 2.9391          | 61.2841 |
| 2.7381        | 1.9342 | 3000 | 2.7814          | 59.1929 |
| 2.5905        | 2.0632 | 3200 | 2.5964          | 54.9988 |
| 2.4555        | 2.1921 | 3400 | 2.3926          | 51.0456 |
| 2.3566        | 2.3211 | 3600 | 2.3930          | 51.1689 |
| 2.2751        | 2.4500 | 3800 | 2.2846          | 49.4596 |
| 2.1796        | 2.5790 | 4000 | 2.1934          | 48.0028 |
| 2.1292        | 2.7079 | 4200 | 2.1426          | 47.0923 |
| 2.0724        | 2.8369 | 4400 | 2.1201          | 47.0042 |
| 2.0759        | 2.9658 | 4600 | 2.0912          | 46.1231 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1