Hugging Face's logo

FarhadMadadzade
/
wav2vec2-large-xlsr-53-english-ser-linear

Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
audio
speech
speech-emotion-recognition
Model card Files Metrics Community
FarhadMadadzade commited on
Commit
f485211
·
verified ·
1 Parent(s): e03e9b8

End of training

Browse files
Files changed (2) hide show
  1. README.md +244 -0
  2. runs/Mar29_13-56-28_DESKTOP-K2HL3M8/events.out.tfevents.1711716989.DESKTOP-K2HL3M8 +2 -2
README.md ADDED
@@ -0,0 +1,244 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
4
+ tags:
5
+ - generated_from_trainer
6
+ metrics:
7
+ - accuracy
8
+ model-index:
9
+ - name: wav2vec2-large-xlsr-53-english-ser-linear
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
+ # wav2vec2-large-xlsr-53-english-ser-linear
17
+
18
+ This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on an unknown dataset.
19
+ It achieves the following results on the evaluation set:
20
+ - Loss: 0.4643
21
+ - Accuracy: 0.8587
22
+
23
+ ## Model description
24
+
25
+ More information needed
26
+
27
+ ## Intended uses & limitations
28
+
29
+ More information needed
30
+
31
+ ## Training and evaluation data
32
+
33
+ More information needed
34
+
35
+ ## Training procedure
36
+
37
+ ### Training hyperparameters
38
+
39
+ The following hyperparameters were used during training:
40
+ - learning_rate: 0.0001
41
+ - train_batch_size: 4
42
+ - eval_batch_size: 4
43
+ - seed: 42
44
+ - gradient_accumulation_steps: 2
45
+ - total_train_batch_size: 8
46
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
47
+ - lr_scheduler_type: linear
48
+ - num_epochs: 1.5
49
+ - mixed_precision_training: Native AMP
50
+
51
+ ### Training results
52
+
53
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
54
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
55
+ | 1.8767 | 0.01 | 10 | 1.8078 | 0.1684 |
56
+ | 1.7967 | 0.02 | 20 | 1.7544 | 0.2235 |
57
+ | 1.8173 | 0.02 | 30 | 1.7072 | 0.3032 |
58
+ | 1.7604 | 0.03 | 40 | 1.7162 | 0.2227 |
59
+ | 1.7271 | 0.04 | 50 | 1.6655 | 0.3032 |
60
+ | 1.764 | 0.05 | 60 | 1.5927 | 0.3599 |
61
+ | 1.55 | 0.06 | 70 | 1.5354 | 0.3657 |
62
+ | 1.5448 | 0.07 | 80 | 1.4057 | 0.4560 |
63
+ | 1.5118 | 0.07 | 90 | 1.3551 | 0.4733 |
64
+ | 1.354 | 0.08 | 100 | 1.2319 | 0.5596 |
65
+ | 1.3675 | 0.09 | 110 | 1.1786 | 0.5735 |
66
+ | 1.4058 | 0.1 | 120 | 1.0949 | 0.6105 |
67
+ | 1.1595 | 0.11 | 130 | 1.0964 | 0.5908 |
68
+ | 1.0444 | 0.12 | 140 | 1.1262 | 0.6212 |
69
+ | 1.0483 | 0.12 | 150 | 1.0863 | 0.5982 |
70
+ | 1.0439 | 0.13 | 160 | 1.0488 | 0.6491 |
71
+ | 1.0129 | 0.14 | 170 | 0.9045 | 0.6549 |
72
+ | 1.0171 | 0.15 | 180 | 1.0276 | 0.6270 |
73
+ | 1.0867 | 0.16 | 190 | 1.0888 | 0.6023 |
74
+ | 1.0646 | 0.16 | 200 | 0.9730 | 0.6311 |
75
+ | 1.0403 | 0.17 | 210 | 0.9315 | 0.6582 |
76
+ | 0.869 | 0.18 | 220 | 0.9686 | 0.6574 |
77
+ | 0.9193 | 0.19 | 230 | 0.9076 | 0.6960 |
78
+ | 1.0266 | 0.2 | 240 | 1.0796 | 0.6565 |
79
+ | 0.8563 | 0.21 | 250 | 1.0173 | 0.6426 |
80
+ | 0.8382 | 0.21 | 260 | 0.9155 | 0.6820 |
81
+ | 0.9275 | 0.22 | 270 | 0.9397 | 0.6689 |
82
+ | 0.9402 | 0.23 | 280 | 0.8919 | 0.6861 |
83
+ | 0.8636 | 0.24 | 290 | 0.9795 | 0.6680 |
84
+ | 1.2393 | 0.25 | 300 | 0.9872 | 0.6680 |
85
+ | 0.9537 | 0.25 | 310 | 0.8181 | 0.7247 |
86
+ | 0.7361 | 0.26 | 320 | 0.8470 | 0.7025 |
87
+ | 0.8452 | 0.27 | 330 | 0.8045 | 0.7198 |
88
+ | 0.9613 | 0.28 | 340 | 0.7530 | 0.7313 |
89
+ | 0.9335 | 0.29 | 350 | 0.9019 | 0.6902 |
90
+ | 0.9414 | 0.3 | 360 | 0.8981 | 0.6795 |
91
+ | 0.7473 | 0.3 | 370 | 0.7532 | 0.7321 |
92
+ | 0.8774 | 0.31 | 380 | 0.8953 | 0.7165 |
93
+ | 0.6989 | 0.32 | 390 | 0.7381 | 0.7387 |
94
+ | 0.9826 | 0.33 | 400 | 0.7128 | 0.7403 |
95
+ | 0.783 | 0.34 | 410 | 0.8292 | 0.6952 |
96
+ | 0.9668 | 0.35 | 420 | 0.7826 | 0.7239 |
97
+ | 0.7935 | 0.35 | 430 | 0.7081 | 0.7510 |
98
+ | 0.8284 | 0.36 | 440 | 0.7304 | 0.7264 |
99
+ | 0.9404 | 0.37 | 450 | 0.6761 | 0.7650 |
100
+ | 0.7735 | 0.38 | 460 | 0.6827 | 0.7469 |
101
+ | 0.6811 | 0.39 | 470 | 0.7926 | 0.7132 |
102
+ | 0.683 | 0.39 | 480 | 0.6883 | 0.7428 |
103
+ | 0.6779 | 0.4 | 490 | 0.6608 | 0.7486 |
104
+ | 0.6329 | 0.41 | 500 | 0.6578 | 0.7617 |
105
+ | 0.5824 | 0.42 | 510 | 0.7696 | 0.7420 |
106
+ | 0.6974 | 0.43 | 520 | 0.6755 | 0.7625 |
107
+ | 0.7716 | 0.44 | 530 | 0.6453 | 0.7716 |
108
+ | 0.7463 | 0.44 | 540 | 0.6644 | 0.7642 |
109
+ | 0.7993 | 0.45 | 550 | 0.6059 | 0.7864 |
110
+ | 0.606 | 0.46 | 560 | 0.6857 | 0.7461 |
111
+ | 0.8619 | 0.47 | 570 | 0.6570 | 0.7560 |
112
+ | 0.699 | 0.48 | 580 | 0.7400 | 0.7313 |
113
+ | 0.6619 | 0.49 | 590 | 0.7014 | 0.7494 |
114
+ | 0.7696 | 0.49 | 600 | 0.6621 | 0.7584 |
115
+ | 0.6544 | 0.5 | 610 | 0.6826 | 0.7650 |
116
+ | 0.5403 | 0.51 | 620 | 0.7464 | 0.7551 |
117
+ | 0.746 | 0.52 | 630 | 0.7323 | 0.7551 |
118
+ | 0.8129 | 0.53 | 640 | 0.7221 | 0.7634 |
119
+ | 0.7245 | 0.53 | 650 | 0.6306 | 0.7790 |
120
+ | 0.7062 | 0.54 | 660 | 0.6250 | 0.7896 |
121
+ | 0.741 | 0.55 | 670 | 0.6129 | 0.7938 |
122
+ | 0.7185 | 0.56 | 680 | 0.6332 | 0.7847 |
123
+ | 0.7706 | 0.57 | 690 | 0.5988 | 0.7954 |
124
+ | 0.8147 | 0.58 | 700 | 0.7032 | 0.7781 |
125
+ | 0.5144 | 0.58 | 710 | 0.6849 | 0.7634 |
126
+ | 0.9247 | 0.59 | 720 | 0.6088 | 0.7749 |
127
+ | 0.629 | 0.6 | 730 | 0.6393 | 0.7806 |
128
+ | 0.5908 | 0.61 | 740 | 0.5696 | 0.7913 |
129
+ | 0.4951 | 0.62 | 750 | 0.6370 | 0.7765 |
130
+ | 0.6358 | 0.62 | 760 | 0.6232 | 0.7979 |
131
+ | 0.6396 | 0.63 | 770 | 0.6707 | 0.7905 |
132
+ | 0.6947 | 0.64 | 780 | 0.6981 | 0.7683 |
133
+ | 0.6748 | 0.65 | 790 | 0.6761 | 0.7765 |
134
+ | 0.5607 | 0.66 | 800 | 0.6551 | 0.7921 |
135
+ | 0.6991 | 0.67 | 810 | 0.6134 | 0.7905 |
136
+ | 0.5793 | 0.67 | 820 | 0.5633 | 0.8118 |
137
+ | 0.4755 | 0.68 | 830 | 0.6031 | 0.7929 |
138
+ | 0.7645 | 0.69 | 840 | 0.5896 | 0.7962 |
139
+ | 0.742 | 0.7 | 850 | 0.5811 | 0.8036 |
140
+ | 0.5281 | 0.71 | 860 | 0.6449 | 0.7855 |
141
+ | 0.722 | 0.72 | 870 | 0.6593 | 0.7765 |
142
+ | 0.8174 | 0.72 | 880 | 0.5410 | 0.8003 |
143
+ | 0.5373 | 0.73 | 890 | 0.5802 | 0.7954 |
144
+ | 0.3868 | 0.74 | 900 | 0.6015 | 0.7954 |
145
+ | 0.5459 | 0.75 | 910 | 0.5485 | 0.7970 |
146
+ | 0.4629 | 0.76 | 920 | 0.6961 | 0.7584 |
147
+ | 0.6952 | 0.76 | 930 | 0.5608 | 0.8053 |
148
+ | 0.8452 | 0.77 | 940 | 0.5649 | 0.8044 |
149
+ | 0.6026 | 0.78 | 950 | 0.5330 | 0.8127 |
150
+ | 0.5131 | 0.79 | 960 | 0.5971 | 0.7888 |
151
+ | 0.6814 | 0.8 | 970 | 0.5594 | 0.8061 |
152
+ | 0.6001 | 0.81 | 980 | 0.5851 | 0.7954 |
153
+ | 0.5367 | 0.81 | 990 | 0.5716 | 0.8003 |
154
+ | 0.8356 | 0.82 | 1000 | 0.6519 | 0.7683 |
155
+ | 0.502 | 0.83 | 1010 | 0.6180 | 0.7749 |
156
+ | 0.5343 | 0.84 | 1020 | 0.5377 | 0.8053 |
157
+ | 0.5288 | 0.85 | 1030 | 0.5902 | 0.7962 |
158
+ | 0.5786 | 0.86 | 1040 | 0.6221 | 0.7905 |
159
+ | 0.6272 | 0.86 | 1050 | 0.6688 | 0.7831 |
160
+ | 0.5105 | 0.87 | 1060 | 0.6209 | 0.7880 |
161
+ | 0.5806 | 0.88 | 1070 | 0.6145 | 0.7929 |
162
+ | 0.5805 | 0.89 | 1080 | 0.6150 | 0.7847 |
163
+ | 0.4812 | 0.9 | 1090 | 0.5812 | 0.8061 |
164
+ | 0.5558 | 0.9 | 1100 | 0.6388 | 0.8044 |
165
+ | 0.7507 | 0.91 | 1110 | 0.5873 | 0.8044 |
166
+ | 0.7217 | 0.92 | 1120 | 0.5404 | 0.8085 |
167
+ | 0.8146 | 0.93 | 1130 | 0.5449 | 0.8003 |
168
+ | 0.6112 | 0.94 | 1140 | 0.5038 | 0.8151 |
169
+ | 0.7305 | 0.95 | 1150 | 0.4767 | 0.8316 |
170
+ | 0.3422 | 0.95 | 1160 | 0.5178 | 0.8127 |
171
+ | 0.4644 | 0.96 | 1170 | 0.5073 | 0.8200 |
172
+ | 0.4664 | 0.97 | 1180 | 0.4988 | 0.8184 |
173
+ | 0.6223 | 0.98 | 1190 | 0.5120 | 0.8283 |
174
+ | 0.6961 | 0.99 | 1200 | 0.5217 | 0.8118 |
175
+ | 0.6706 | 1.0 | 1210 | 0.5235 | 0.8094 |
176
+ | 0.3899 | 1.0 | 1220 | 0.5085 | 0.8184 |
177
+ | 0.418 | 1.01 | 1230 | 0.5171 | 0.8135 |
178
+ | 0.5011 | 1.02 | 1240 | 0.5056 | 0.8217 |
179
+ | 0.2969 | 1.03 | 1250 | 0.5209 | 0.8217 |
180
+ | 0.5093 | 1.04 | 1260 | 0.4921 | 0.8348 |
181
+ | 0.5167 | 1.04 | 1270 | 0.5081 | 0.8274 |
182
+ | 0.6382 | 1.05 | 1280 | 0.4851 | 0.8291 |
183
+ | 0.3493 | 1.06 | 1290 | 0.4946 | 0.8324 |
184
+ | 0.3471 | 1.07 | 1300 | 0.5122 | 0.8299 |
185
+ | 0.452 | 1.08 | 1310 | 0.5592 | 0.8291 |
186
+ | 0.4362 | 1.09 | 1320 | 0.5528 | 0.8266 |
187
+ | 0.4224 | 1.09 | 1330 | 0.5571 | 0.8192 |
188
+ | 0.333 | 1.1 | 1340 | 0.5714 | 0.8110 |
189
+ | 0.2944 | 1.11 | 1350 | 0.5156 | 0.8299 |
190
+ | 0.4004 | 1.12 | 1360 | 0.5208 | 0.8340 |
191
+ | 0.6824 | 1.13 | 1370 | 0.5426 | 0.8258 |
192
+ | 0.3746 | 1.13 | 1380 | 0.4902 | 0.8365 |
193
+ | 0.3679 | 1.14 | 1390 | 0.4868 | 0.8373 |
194
+ | 0.5009 | 1.15 | 1400 | 0.5192 | 0.8283 |
195
+ | 0.5577 | 1.16 | 1410 | 0.4937 | 0.8316 |
196
+ | 0.2566 | 1.17 | 1420 | 0.5043 | 0.8250 |
197
+ | 0.6625 | 1.18 | 1430 | 0.5416 | 0.8209 |
198
+ | 0.3251 | 1.18 | 1440 | 0.5146 | 0.8291 |
199
+ | 0.4306 | 1.19 | 1450 | 0.5313 | 0.8266 |
200
+ | 0.3159 | 1.2 | 1460 | 0.5308 | 0.8291 |
201
+ | 0.3598 | 1.21 | 1470 | 0.4869 | 0.8439 |
202
+ | 0.5498 | 1.22 | 1480 | 0.4670 | 0.8537 |
203
+ | 0.4947 | 1.23 | 1490 | 0.4928 | 0.8463 |
204
+ | 0.3948 | 1.23 | 1500 | 0.4816 | 0.8455 |
205
+ | 0.3137 | 1.24 | 1510 | 0.4755 | 0.8439 |
206
+ | 0.3525 | 1.25 | 1520 | 0.4972 | 0.8389 |
207
+ | 0.4821 | 1.26 | 1530 | 0.4954 | 0.8381 |
208
+ | 0.6099 | 1.27 | 1540 | 0.5096 | 0.8324 |
209
+ | 0.3172 | 1.27 | 1550 | 0.5029 | 0.8389 |
210
+ | 0.29 | 1.28 | 1560 | 0.4852 | 0.8455 |
211
+ | 0.288 | 1.29 | 1570 | 0.4916 | 0.8496 |
212
+ | 0.3771 | 1.3 | 1580 | 0.4734 | 0.8505 |
213
+ | 0.3106 | 1.31 | 1590 | 0.4746 | 0.8431 |
214
+ | 0.3494 | 1.32 | 1600 | 0.5069 | 0.8431 |
215
+ | 0.3183 | 1.32 | 1610 | 0.5155 | 0.8398 |
216
+ | 0.4353 | 1.33 | 1620 | 0.5242 | 0.8332 |
217
+ | 0.6207 | 1.34 | 1630 | 0.5161 | 0.8340 |
218
+ | 0.3241 | 1.35 | 1640 | 0.5037 | 0.8406 |
219
+ | 0.3646 | 1.36 | 1650 | 0.4890 | 0.8439 |
220
+ | 0.2341 | 1.37 | 1660 | 0.4884 | 0.8496 |
221
+ | 0.4874 | 1.37 | 1670 | 0.4688 | 0.8562 |
222
+ | 0.6701 | 1.38 | 1680 | 0.4589 | 0.8554 |
223
+ | 0.391 | 1.39 | 1690 | 0.4684 | 0.8537 |
224
+ | 0.3333 | 1.4 | 1700 | 0.4738 | 0.8513 |
225
+ | 0.2449 | 1.41 | 1710 | 0.4753 | 0.8488 |
226
+ | 0.361 | 1.41 | 1720 | 0.4946 | 0.8496 |
227
+ | 0.2229 | 1.42 | 1730 | 0.4971 | 0.8463 |
228
+ | 0.5915 | 1.43 | 1740 | 0.4904 | 0.8513 |
229
+ | 0.1812 | 1.44 | 1750 | 0.4782 | 0.8537 |
230
+ | 0.5887 | 1.45 | 1760 | 0.4702 | 0.8570 |
231
+ | 0.2823 | 1.46 | 1770 | 0.4665 | 0.8570 |
232
+ | 0.3397 | 1.46 | 1780 | 0.4673 | 0.8546 |
233
+ | 0.4727 | 1.47 | 1790 | 0.4638 | 0.8578 |
234
+ | 0.3303 | 1.48 | 1800 | 0.4636 | 0.8578 |
235
+ | 0.4544 | 1.49 | 1810 | 0.4646 | 0.8587 |
236
+ | 0.366 | 1.5 | 1820 | 0.4643 | 0.8587 |
237
+
238
+
239
+ ### Framework versions
240
+
241
+ - Transformers 4.40.0.dev0
242
+ - Pytorch 2.2.1+cu121
243
+ - Datasets 2.18.1.dev0
244
+ - Tokenizers 0.15.2
runs/Mar29_13-56-28_DESKTOP-K2HL3M8/events.out.tfevents.1711716989.DESKTOP-K2HL3M8 CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:0eb9a4535db46cdab34f113af5106a1e720f6552d059cc4b960a30660aa16ee6
3
- size 86296
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:10a1fb7d77190675f9e9981ad4efbc93895e096c3090d46e529c3b11cf89de15
3
+ size 104061