Hugging Face's logo

xekri
/
wav2vec2-common_voice_13_0-eo-10_1

Automatic Speech Recognition
Transformers
PyTorch
Esperanto
wav2vec2
mozilla-foundation/common_voice_13_0
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
xekri commited on
Commit
8449774
•
1 Parent(s): 6c98da9

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +100 -0
README.md ADDED
@@ -0,0 +1,100 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ datasets:
6
+ - common_voice_13_0
7
+ metrics:
8
+ - wer
9
+ model-index:
10
+ - name: wav2vec2-common_voice_13_0-eo-10_1
11
+ results:
12
+ - task:
13
+ name: Automatic Speech Recognition
14
+ type: automatic-speech-recognition
15
+ dataset:
16
+ name: common_voice_13_0
17
+ type: common_voice_13_0
18
+ config: eo
19
+ split: validation
20
+ args: eo
21
+ metrics:
22
+ - name: Wer
23
+ type: wer
24
+ value: 0.053735309652713587
25
+ ---
26
+
27
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
28
+ should probably proofread and complete it, then remove this comment. -->
29
+
30
+ # wav2vec2-common_voice_13_0-eo-10_1
31
+
32
+ This model is a fine-tuned version of [xekri/wav2vec2-common_voice_13_0-eo-10](https://huggingface.co/xekri/wav2vec2-common_voice_13_0-eo-10) on the common_voice_13_0 dataset.
33
+ It achieves the following results on the evaluation set:
34
+ - Loss: 0.0391
35
+ - Cer: 0.0098
36
+ - Wer: 0.0537
37
+
38
+ ## Model description
39
+
40
+ More information needed
41
+
42
+ ## Intended uses & limitations
43
+
44
+ More information needed
45
+
46
+ ## Training and evaluation data
47
+
48
+ More information needed
49
+
50
+ ## Training procedure
51
+
52
+ ### Training hyperparameters
53
+
54
+ The following hyperparameters were used during training:
55
+ - learning_rate: 3e-05
56
+ - train_batch_size: 16
57
+ - eval_batch_size: 8
58
+ - seed: 42
59
+ - gradient_accumulation_steps: 2
60
+ - total_train_batch_size: 32
61
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
+ - lr_scheduler_type: linear
63
+ - lr_scheduler_warmup_steps: 500
64
+ - num_epochs: 5
65
+ - mixed_precision_training: Native AMP
66
+
67
+ ### Training results
68
+
69
+ | Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
70
+ |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
71
+ | 0.1142 | 0.22 | 1000 | 0.0483 | 0.0126 | 0.0707 |
72
+ | 0.1049 | 0.44 | 2000 | 0.0474 | 0.0123 | 0.0675 |
73
+ | 0.0982 | 0.67 | 3000 | 0.0471 | 0.0120 | 0.0664 |
74
+ | 0.092 | 0.89 | 4000 | 0.0459 | 0.0117 | 0.0640 |
75
+ | 0.0847 | 1.11 | 5000 | 0.0459 | 0.0115 | 0.0631 |
76
+ | 0.0837 | 1.33 | 6000 | 0.0453 | 0.0113 | 0.0624 |
77
+ | 0.0803 | 1.56 | 7000 | 0.0443 | 0.0109 | 0.0598 |
78
+ | 0.0826 | 1.78 | 8000 | 0.0441 | 0.0110 | 0.0604 |
79
+ | 0.0809 | 2.0 | 9000 | 0.0437 | 0.0110 | 0.0605 |
80
+ | 0.0728 | 2.22 | 10000 | 0.0451 | 0.0109 | 0.0597 |
81
+ | 0.0707 | 2.45 | 11000 | 0.0444 | 0.0108 | 0.0591 |
82
+ | 0.0698 | 2.67 | 12000 | 0.0442 | 0.0105 | 0.0576 |
83
+ | 0.0981 | 2.89 | 13000 | 0.0411 | 0.0104 | 0.0572 |
84
+ | 0.0928 | 3.11 | 14000 | 0.0413 | 0.0102 | 0.0561 |
85
+ | 0.0927 | 3.34 | 15000 | 0.0410 | 0.0102 | 0.0565 |
86
+ | 0.0886 | 3.56 | 16000 | 0.0402 | 0.0102 | 0.0558 |
87
+ | 0.091 | 3.78 | 17000 | 0.0400 | 0.0101 | 0.0553 |
88
+ | 0.0888 | 4.0 | 18000 | 0.0398 | 0.0100 | 0.0546 |
89
+ | 0.0885 | 4.23 | 19000 | 0.0395 | 0.0099 | 0.0542 |
90
+ | 0.0869 | 4.45 | 20000 | 0.0394 | 0.0099 | 0.0540 |
91
+ | 0.0844 | 4.67 | 21000 | 0.0393 | 0.0098 | 0.0539 |
92
+ | 0.0882 | 4.89 | 22000 | 0.0391 | 0.0098 | 0.0537 |
93
+
94
+
95
+ ### Framework versions
96
+
97
+ - Transformers 4.29.2
98
+ - Pytorch 2.0.1+cu117
99
+ - Datasets 2.12.0
100
+ - Tokenizers 0.13.3