Hugging Face's logo

lgris
/
wavlm-large-CORAA-pt-cv7

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Portuguese
wavlm
mozilla-foundation/common_voice_7_0
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
lgris commited on
Commit
5502094
•
1 Parent(s): fb44bea

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +110 -0
README.md ADDED
@@ -0,0 +1,110 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - generated_from_trainer
4
+ datasets:
5
+ - common_voice
6
+ model-index:
7
+ - name: wavlm-large-CORAA-pt-cv7
8
+ results: []
9
+ ---
10
+
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
+
14
+ # wavlm-large-CORAA-pt-cv7
15
+
16
+ This model is a fine-tuned version of [lgris/WavLM-large-CORAA-pt](https://huggingface.co/lgris/WavLM-large-CORAA-pt) on the common_voice dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Loss: 0.2546
19
+ - Wer: 0.2261
20
+
21
+ ## Model description
22
+
23
+ More information needed
24
+
25
+ ## Intended uses & limitations
26
+
27
+ More information needed
28
+
29
+ ## Training and evaluation data
30
+
31
+ More information needed
32
+
33
+ ## Training procedure
34
+
35
+ ### Training hyperparameters
36
+
37
+ The following hyperparameters were used during training:
38
+ - learning_rate: 0.0001
39
+ - train_batch_size: 8
40
+ - eval_batch_size: 8
41
+ - seed: 42
42
+ - gradient_accumulation_steps: 2
43
+ - total_train_batch_size: 16
44
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
45
+ - lr_scheduler_type: linear
46
+ - lr_scheduler_warmup_steps: 100
47
+ - training_steps: 5000
48
+
49
+ ### Training results
50
+
51
+ | Training Loss | Epoch | Step | Validation Loss | Wer |
52
+ |:-------------:|:-----:|:----:|:---------------:|:------:|
53
+ | 0.6029 | 0.13 | 100 | 0.3679 | 0.3347 |
54
+ | 0.5297 | 0.26 | 200 | 0.3516 | 0.3227 |
55
+ | 0.5134 | 0.39 | 300 | 0.3327 | 0.3167 |
56
+ | 0.4941 | 0.52 | 400 | 0.3281 | 0.3122 |
57
+ | 0.4816 | 0.65 | 500 | 0.3154 | 0.3102 |
58
+ | 0.4649 | 0.78 | 600 | 0.3199 | 0.3058 |
59
+ | 0.461 | 0.91 | 700 | 0.3047 | 0.2974 |
60
+ | 0.4613 | 1.04 | 800 | 0.3006 | 0.2900 |
61
+ | 0.4198 | 1.17 | 900 | 0.2951 | 0.2891 |
62
+ | 0.3864 | 1.3 | 1000 | 0.2989 | 0.2862 |
63
+ | 0.3963 | 1.43 | 1100 | 0.2932 | 0.2830 |
64
+ | 0.3953 | 1.56 | 1200 | 0.2936 | 0.2829 |
65
+ | 0.3962 | 1.69 | 1300 | 0.2952 | 0.2773 |
66
+ | 0.3811 | 1.82 | 1400 | 0.2915 | 0.2748 |
67
+ | 0.3736 | 1.95 | 1500 | 0.2839 | 0.2684 |
68
+ | 0.3507 | 2.08 | 1600 | 0.2914 | 0.2678 |
69
+ | 0.3277 | 2.21 | 1700 | 0.2895 | 0.2652 |
70
+ | 0.3344 | 2.34 | 1800 | 0.2843 | 0.2673 |
71
+ | 0.335 | 2.47 | 1900 | 0.2821 | 0.2635 |
72
+ | 0.3559 | 2.6 | 2000 | 0.2830 | 0.2599 |
73
+ | 0.3254 | 2.73 | 2100 | 0.2711 | 0.2577 |
74
+ | 0.3263 | 2.86 | 2200 | 0.2685 | 0.2546 |
75
+ | 0.3266 | 2.99 | 2300 | 0.2679 | 0.2521 |
76
+ | 0.3066 | 3.12 | 2400 | 0.2727 | 0.2526 |
77
+ | 0.2998 | 3.25 | 2500 | 0.2648 | 0.2537 |
78
+ | 0.2961 | 3.38 | 2600 | 0.2630 | 0.2519 |
79
+ | 0.3046 | 3.51 | 2700 | 0.2684 | 0.2506 |
80
+ | 0.3006 | 3.64 | 2800 | 0.2604 | 0.2492 |
81
+ | 0.2992 | 3.77 | 2900 | 0.2682 | 0.2508 |
82
+ | 0.2775 | 3.9 | 3000 | 0.2732 | 0.2440 |
83
+ | 0.2903 | 4.03 | 3100 | 0.2659 | 0.2427 |
84
+ | 0.2535 | 4.16 | 3200 | 0.2650 | 0.2433 |
85
+ | 0.2714 | 4.29 | 3300 | 0.2588 | 0.2394 |
86
+ | 0.2636 | 4.42 | 3400 | 0.2652 | 0.2434 |
87
+ | 0.2647 | 4.55 | 3500 | 0.2624 | 0.2371 |
88
+ | 0.2796 | 4.67 | 3600 | 0.2611 | 0.2373 |
89
+ | 0.2644 | 4.8 | 3700 | 0.2604 | 0.2341 |
90
+ | 0.2657 | 4.93 | 3800 | 0.2567 | 0.2331 |
91
+ | 0.2423 | 5.06 | 3900 | 0.2594 | 0.2322 |
92
+ | 0.2556 | 5.19 | 4000 | 0.2587 | 0.2323 |
93
+ | 0.2327 | 5.32 | 4100 | 0.2639 | 0.2299 |
94
+ | 0.2613 | 5.45 | 4200 | 0.2569 | 0.2310 |
95
+ | 0.2382 | 5.58 | 4300 | 0.2585 | 0.2298 |
96
+ | 0.2404 | 5.71 | 4400 | 0.2543 | 0.2287 |
97
+ | 0.2368 | 5.84 | 4500 | 0.2553 | 0.2286 |
98
+ | 0.2514 | 5.97 | 4600 | 0.2517 | 0.2279 |
99
+ | 0.2415 | 6.1 | 4700 | 0.2524 | 0.2270 |
100
+ | 0.2338 | 6.23 | 4800 | 0.2540 | 0.2265 |
101
+ | 0.219 | 6.36 | 4900 | 0.2549 | 0.2263 |
102
+ | 0.2428 | 6.49 | 5000 | 0.2546 | 0.2261 |
103
+
104
+
105
+ ### Framework versions
106
+
107
+ - Transformers 4.16.1
108
+ - Pytorch 1.10.0+cu111
109
+ - Datasets 1.18.2
110
+ - Tokenizers 0.11.0