Hugging Face's logo

JovialValley
/
model_broadclass_onSet1.1

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Model card Files
xet
 Metrics Community
1
JovialValley commited on
Commit
30b2ae5
·
1 Parent(s): cd731c0

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +108 -0
README.md ADDED
@@ -0,0 +1,108 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - accuracy
7
+ - wer
8
+ model-index:
9
+ - name: model_broadclass_onSet1.1
10
+ results: []
11
+ ---
12
+
13
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
+ should probably proofread and complete it, then remove this comment. -->
15
+
16
+ # model_broadclass_onSet1.1
17
+
18
+ This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
19
+ It achieves the following results on the evaluation set:
20
+ - Loss: 0.2469
21
+ - 0 Precision: 1.0
22
+ - 0 Recall: 1.0
23
+ - 0 F1-score: 1.0
24
+ - 0 Support: 24
25
+ - 1 Precision: 1.0
26
+ - 1 Recall: 1.0
27
+ - 1 F1-score: 1.0
28
+ - 1 Support: 39
29
+ - 2 Precision: 1.0
30
+ - 2 Recall: 1.0
31
+ - 2 F1-score: 1.0
32
+ - 2 Support: 23
33
+ - 3 Precision: 1.0
34
+ - 3 Recall: 1.0
35
+ - 3 F1-score: 1.0
36
+ - 3 Support: 12
37
+ - Accuracy: 1.0
38
+ - Macro avg Precision: 1.0
39
+ - Macro avg Recall: 1.0
40
+ - Macro avg F1-score: 1.0
41
+ - Macro avg Support: 98
42
+ - Weighted avg Precision: 1.0
43
+ - Weighted avg Recall: 1.0
44
+ - Weighted avg F1-score: 1.0
45
+ - Weighted avg Support: 98
46
+ - Wer: 0.2423
47
+ - Mtrix: [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
48
+
49
+ ## Model description
50
+
51
+ More information needed
52
+
53
+ ## Intended uses & limitations
54
+
55
+ More information needed
56
+
57
+ ## Training and evaluation data
58
+
59
+ More information needed
60
+
61
+ ## Training procedure
62
+
63
+ ### Training hyperparameters
64
+
65
+ The following hyperparameters were used during training:
66
+ - learning_rate: 0.0003
67
+ - train_batch_size: 8
68
+ - eval_batch_size: 8
69
+ - seed: 42
70
+ - gradient_accumulation_steps: 2
71
+ - total_train_batch_size: 16
72
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
73
+ - lr_scheduler_type: linear
74
+ - lr_scheduler_warmup_steps: 200
75
+ - num_epochs: 80
76
+ - mixed_precision_training: Native AMP
77
+
78
+ ### Training results
79
+
80
+ | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix |
81
+ |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:|
82
+ | 2.3722 | 4.16 | 100 | 2.1950 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
83
+ | 2.2944 | 8.33 | 200 | 2.1537 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
84
+ | 1.9927 | 12.49 | 300 | 1.8879 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
85
+ | 1.7175 | 16.65 | 400 | 1.6374 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
86
+ | 1.6065 | 20.82 | 500 | 1.5619 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
87
+ | 1.5362 | 24.98 | 600 | 1.5019 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
88
+ | 1.5599 | 29.16 | 700 | 1.4858 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
89
+ | 1.5344 | 33.33 | 800 | 1.4721 | 0.2759 | 1.0 | 0.4324 | 24 | 1.0 | 0.2821 | 0.4400 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.3571 | 0.3190 | 0.3205 | 0.2181 | 98 | 0.4655 | 0.3571 | 0.2810 | 98 | 0.9919 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 28, 11, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
90
+ | 1.4024 | 37.49 | 900 | 1.3532 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.9742 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
91
+ | 0.9429 | 41.65 | 1000 | 0.9455 | 0.96 | 1.0 | 0.9796 | 24 | 0.9744 | 0.9744 | 0.9744 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9796 | 0.9836 | 0.9827 | 0.9829 | 98 | 0.9800 | 0.9796 | 0.9796 | 98 | 0.9084 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]] |
92
+ | 0.8955 | 45.82 | 1100 | 0.8890 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9246 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
93
+ | 0.8708 | 49.98 | 1200 | 0.8304 | 1.0 | 1.0 | 1.0 | 24 | 0.975 | 1.0 | 0.9873 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.9938 | 0.9891 | 0.9913 | 98 | 0.9901 | 0.9898 | 0.9897 | 98 | 0.9272 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]] |
94
+ | 0.8671 | 54.16 | 1300 | 0.8028 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9891 | 0.9893 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9211 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 1, 0, 22, 0], [3, 0, 0, 0, 12]] |
95
+ | 0.8383 | 58.33 | 1400 | 0.7804 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.9170 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
96
+ | 0.7872 | 62.49 | 1500 | 0.7745 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9439 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
97
+ | 0.7538 | 66.65 | 1600 | 0.7141 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9267 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
98
+ | 0.6439 | 70.82 | 1700 | 0.5818 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.8574 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
99
+ | 0.5295 | 74.98 | 1800 | 0.3775 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.4633 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
100
+ | 0.4184 | 79.16 | 1900 | 0.2507 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.2529 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
101
+
102
+
103
+ ### Framework versions
104
+
105
+ - Transformers 4.25.1
106
+ - Pytorch 1.13.0+cu116
107
+ - Datasets 2.8.0
108
+ - Tokenizers 0.13.2