đ victor-hg-ptbr-2.0
This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It can achieve high - quality speech recognition results with low loss and word error rate.
đ Quick Start
This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
It achieves the following results on the evaluation set:
đ Documentation
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Wer |
4.4069 |
0.21 |
400 |
1.1372 |
0.9140 |
0.8079 |
0.43 |
800 |
0.5822 |
0.5339 |
0.5821 |
0.64 |
1200 |
0.4226 |
0.4177 |
0.5159 |
0.86 |
1600 |
0.4074 |
0.3970 |
0.4484 |
1.07 |
2000 |
0.3144 |
0.3220 |
0.3937 |
1.29 |
2400 |
0.3160 |
0.3264 |
0.3911 |
1.5 |
2800 |
0.2863 |
0.2956 |
0.3761 |
1.71 |
3200 |
0.3029 |
0.3128 |
0.3722 |
1.93 |
3600 |
0.2771 |
0.2933 |
0.3193 |
2.14 |
4000 |
0.2603 |
0.2795 |
0.3013 |
2.36 |
4400 |
0.2682 |
0.2703 |
0.3039 |
2.57 |
4800 |
0.2630 |
0.2618 |
0.3133 |
2.79 |
5200 |
0.2578 |
0.2629 |
0.3173 |
3.0 |
5600 |
0.2640 |
0.2746 |
0.2521 |
3.22 |
6000 |
0.2797 |
0.2662 |
0.2654 |
3.43 |
6400 |
0.2762 |
0.2640 |
0.2586 |
3.64 |
6800 |
0.2642 |
0.2596 |
0.265 |
3.86 |
7200 |
0.2656 |
0.2794 |
0.2432 |
4.07 |
7600 |
0.2459 |
0.2497 |
0.226 |
4.29 |
8000 |
0.2533 |
0.2509 |
0.2385 |
4.5 |
8400 |
0.2332 |
0.2394 |
0.2332 |
4.72 |
8800 |
0.2500 |
0.2569 |
0.2358 |
4.93 |
9200 |
0.2384 |
0.2489 |
0.2169 |
5.14 |
9600 |
0.2410 |
0.2380 |
0.2038 |
5.36 |
10000 |
0.2426 |
0.2333 |
0.2109 |
5.57 |
10400 |
0.2480 |
0.2473 |
0.2147 |
5.79 |
10800 |
0.2341 |
0.2272 |
0.2153 |
6.0 |
11200 |
0.2402 |
0.2424 |
0.186 |
6.22 |
11600 |
0.2560 |
0.2489 |
0.1854 |
6.43 |
12000 |
0.2444 |
0.2402 |
0.1915 |
6.65 |
12400 |
0.2720 |
0.2531 |
0.1929 |
6.86 |
12800 |
0.2516 |
0.2342 |
0.1842 |
7.07 |
13200 |
0.2480 |
0.2304 |
0.1682 |
7.29 |
13600 |
0.2393 |
0.2276 |
0.1753 |
7.5 |
14000 |
0.2514 |
0.2263 |
0.1798 |
7.72 |
14400 |
0.2191 |
0.2178 |
0.1736 |
7.93 |
14800 |
0.2351 |
0.2197 |
0.1668 |
8.15 |
15200 |
0.2315 |
0.2194 |
0.1545 |
8.36 |
15600 |
0.2291 |
0.2079 |
0.1508 |
8.57 |
16000 |
0.2351 |
0.2134 |
0.1662 |
8.79 |
16400 |
0.2298 |
0.2197 |
0.1621 |
9.0 |
16800 |
0.2314 |
0.2219 |
0.1416 |
9.22 |
17200 |
0.2306 |
0.2192 |
0.1455 |
9.43 |
17600 |
0.2466 |
0.2184 |
0.1522 |
9.65 |
18000 |
0.2392 |
0.2255 |
0.1434 |
9.86 |
18400 |
0.2464 |
0.2208 |
0.1362 |
10.08 |
18800 |
0.2351 |
0.2095 |
0.127 |
10.29 |
19200 |
0.2373 |
0.2110 |
0.133 |
10.5 |
19600 |
0.2269 |
0.2031 |
0.1308 |
10.72 |
20000 |
0.2400 |
0.2096 |
0.1331 |
10.93 |
20400 |
0.2243 |
0.2083 |
0.125 |
11.15 |
20800 |
0.2334 |
0.2063 |
0.1236 |
11.36 |
21200 |
0.2195 |
0.2044 |
0.1263 |
11.58 |
21600 |
0.2263 |
0.2050 |
0.1235 |
11.79 |
22000 |
0.2217 |
0.2087 |
0.1301 |
12.0 |
22400 |
0.2332 |
0.2094 |
0.1123 |
12.22 |
22800 |
0.2195 |
0.2068 |
0.117 |
12.43 |
23200 |
0.2266 |
0.2110 |
0.1156 |
12.65 |
23600 |
0.2469 |
0.2063 |
0.1117 |
12.86 |
24000 |
0.2379 |
0.2035 |
0.1124 |
13.08 |
24400 |
0.2156 |
0.1963 |
0.106 |
13.29 |
24800 |
0.2310 |
0.1988 |
0.1066 |
13.5 |
25200 |
0.2334 |
0.1950 |
0.1069 |
13.72 |
25600 |
0.2230 |
0.2011 |
0.1089 |
13.93 |
26000 |
0.2233 |
0.2003 |
0.0977 |
14.15 |
26400 |
0.2273 |
0.1895 |
0.0972 |
14.36 |
26800 |
0.2265 |
0.1887 |
0.1005 |
14.58 |
27200 |
0.2196 |
0.1934 |
0.1058 |
14.79 |
27600 |
0.2213 |
0.1870 |
0.1027 |
15.01 |
28000 |
0.2361 |
0.1916 |
0.0886 |
15.22 |
28400 |
0.2275 |
0.1815 |
0.0885 |
15.43 |
28800 |
0.2230 |
0.1891 |
0.0911 |
15.65 |
29200 |
0.2237 |
0.1989 |
0.0923 |
15.86 |
29600 |
0.2200 |
0.1857 |
0.0868 |
16.08 |
30000 |
0.2248 |
0.1875 |
0.0812 |
16.29 |
30400 |
0.2240 |
0.1874 |
0.0829 |
16.51 |
30800 |
0.2198 |
0.1814 |
0.0832 |
16.72 |
31200 |
0.2328 |
0.1892 |
0.0822 |
16.93 |
31600 |
0.2283 |
0.1862 |
0.0828 |
17.15 |
32000 |
0.2283 |
0.1806 |
0.0791 |
17.36 |
32400 |
0.2197 |
0.1787 |
0.0801 |
17.58 |
32800 |
0.2249 |
0.1815 |
0.0804 |
17.79 |
33200 |
0.2304 |
0.1789 |
0.0833 |
18.01 |
33600 |
0.2235 |
0.1832 |
0.0762 |
18.22 |
34000 |
0.2358 |
0.1784 |
0.0688 |
18.44 |
34400 |
0.2183 |
0.1758 |
0.0751 |
18.65 |
34800 |
0.2169 |
0.1805 |
0.0729 |
18.86 |
35200 |
0.2296 |
0.1770 |
0.0681 |
19.08 |
35600 |
0.2380 |
0.1770 |
0.067 |
19.29 |
36000 |
0.2153 |
0.1777 |
0.0669 |
19.51 |
36400 |
0.2260 |
0.1742 |
0.0824 |
19.72 |
36800 |
0.0289 |
0.0310 |
0.0857 |
19.94 |
37200 |
0.0289 |
0.0322 |
0.0799 |
20.15 |
37600 |
0.0264 |
0.0298 |
0.0767 |
20.36 |
38000 |
0.0273 |
0.0318 |
0.079 |
20.58 |
38400 |
0.0274 |
0.0320 |
0.0791 |
20.79 |
38800 |
0.0279 |
0.0318 |
0.0805 |
21.01 |
39200 |
0.0285 |
0.0330 |
0.0622 |
21.22 |
39600 |
0.0263 |
0.0306 |
0.0622 |
21.44 |
40000 |
0.0290 |
0.0318 |
0.0672 |
21.65 |
40400 |
0.0278 |
0.0330 |
0.0706 |
21.86 |
40800 |
0.0270 |
0.0297 |
0.0619 |
22.08 |
41200 |
0.0288 |
0.0328 |
0.0633 |
22.29 |
41600 |
0.0256 |
0.0303 |
0.0618 |
22.51 |
42000 |
0.0263 |
0.0299 |
0.0576 |
22.72 |
42400 |
0.0273 |
0.0301 |
0.0583 |
22.94 |
42800 |
0.0282 |
0.0297 |
0.0565 |
23.15 |
43200 |
0.0256 |
0.0280 |
0.0557 |
23.37 |
43600 |
0.0268 |
0.0280 |
0.0548 |
23.58 |
44000 |
0.0266 |
0.0291 |
0.056 |
23.79 |
44400 |
0.0264 |
0.0290 |
0.0546 |
24.01 |
44800 |
0.0273 |
0.0284 |
0.0496 |
24.22 |
45200 |
0.0261 |
0.0279 |
0.0512 |
24.44 |
45600 |
0.0256 |
0.0281 |
0.0482 |
24.65 |
46000 |
0.0264 |
0.0285 |
0.0503 |
24.87 |
46400 |
0.0256 |
0.0268 |
0.0471 |
25.08 |
46800 |
0.0270 |
0.0282 |
0.0453 |
25.29 |
47200 |
0.0255 |
0.0267 |
0.0431 |
25.51 |
47600 |
0.0251 |
0.0264 |
0.0464 |
25.72 |
48000 |
0.0262 |
0.0261 |
0.0431 |
25.94 |
48400 |
0.0257 |
0.0265 |
0.0405 |
26.15 |
48800 |
0.0260 |
0.0251 |
0.0406 |
26.37 |
49200 |
0.0246 |
0.0250 |
0.0397 |
26.58 |
49600 |
0.0252 |
0.0254 |
0.0403 |
26.8 |
50000 |
0.0250 |
0.0256 |
0.0385 |
27.01 |
50400 |
0.0254 |
0.0241 |
0.0398 |
27.22 |
50800 |
0.0255 |
0.0242 |
0.0363 |
27.44 |
51200 |
0.0250 |
0.0236 |
0.0372 |
27.65 |
51600 |
0.0247 |
0.0232 |
0.0362 |
27.87 |
52000 |
0.0240 |
0.0226 |
0.0367 |
28.08 |
52400 |
0.0246 |
0.0224 |
0.0347 |
28.3 |
52800 |
0.0247 |
0.0229 |
0.0348 |
28.51 |
53200 |
0.0241 |
0.0229 |
0.0331 |
28.72 |
53600 |
0.0242 |
0.0224 |
0.0339 |
28.94 |
54000 |
0.0241 |
0.0220 |
0.0336 |
29.15 |
54400 |
0.0244 |
0.0221 |
0.0336 |
29.37 |
54800 |
0.0243 |
0.0215 |
0.0349 |
29.58 |
55200 |
0.0239 |
0.0217 |
0.0308 |
29.8 |
55600 |
0.0240 |
0.0219 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.8.1+cu111
- Datasets 2.2.1
- Tokenizers 0.12.1
đ License
This project is licensed under the Apache - 2.0 license.