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wav2vec2-large-mms-1b-zh-CN

This model is a fine-tuned version of facebook/mms-1b-all on the common_voice_6_1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9552
  • Cer: 0.2071

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 8

Training results

Training Loss Epoch Step Validation Loss Cer
42.0738 0.04 100 2.9914 0.4865
2.534 0.09 200 2.0714 0.3981
2.0311 0.13 300 1.9086 0.3844
1.9237 0.17 400 1.7770 0.3650
1.865 0.22 500 1.6745 0.3579
1.8275 0.26 600 1.6277 0.3414
1.8094 0.3 700 1.6812 0.3639
1.7503 0.35 800 1.6279 0.3427
1.7448 0.39 900 1.5611 0.3376
1.7459 0.43 1000 1.5413 0.3323
1.7191 0.47 1100 1.5259 0.3280
1.6317 0.52 1200 1.5102 0.3242
1.6881 0.56 1300 1.4851 0.3212
1.6401 0.6 1400 1.4589 0.3097
1.5909 0.65 1500 1.4985 0.3186
1.618 0.69 1600 1.4415 0.3122
1.6842 0.73 1700 1.4596 0.3161
1.5413 0.78 1800 1.4275 0.3003
1.6461 0.82 1900 1.4214 0.3073
1.5536 0.86 2000 1.3924 0.3003
1.545 0.91 2100 1.3727 0.2907
1.6354 0.95 2200 1.4157 0.3088
1.4913 0.99 2300 1.4012 0.3042
1.2739 1.04 2400 1.3079 0.2855
1.2292 1.08 2500 1.3085 0.2832
1.2424 1.12 2600 1.3273 0.2879
1.2181 1.16 2700 1.3241 0.2864
1.2101 1.21 2800 1.2526 0.2780
1.26 1.25 2900 1.2949 0.2815
1.2154 1.29 3000 1.2932 0.2787
1.2446 1.34 3100 1.2774 0.2792
1.1975 1.38 3200 1.2641 0.2751
1.2048 1.42 3300 1.2645 0.2773
1.1858 1.47 3400 1.2616 0.2741
1.202 1.51 3500 1.2572 0.2725
1.1802 1.55 3600 1.2554 0.2723
1.1912 1.6 3700 1.2703 0.2657
1.213 1.64 3800 1.2491 0.2743
1.1949 1.68 3900 1.2497 0.2734
1.1813 1.73 4000 1.2367 0.2709
1.1935 1.77 4100 1.2174 0.2677
1.1842 1.81 4200 1.2307 0.2660
1.215 1.86 4300 1.2275 0.2696
1.2102 1.9 4400 1.1964 0.2595
1.2206 1.94 4500 1.2046 0.2574
1.2292 1.98 4600 1.1900 0.2595
1.034 2.03 4700 1.1849 0.2547
0.8787 2.07 4800 1.1889 0.2558
0.9124 2.11 4900 1.1809 0.2590
0.9027 2.16 5000 1.1927 0.2608
0.9158 2.2 5100 1.1860 0.2556
0.8683 2.24 5200 1.1660 0.2522
0.8932 2.29 5300 1.1477 0.2533
0.9332 2.33 5400 1.1702 0.2543
0.9427 2.37 5500 1.1653 0.2523
0.9085 2.42 5600 1.1739 0.2539
0.9238 2.46 5700 1.2005 0.2589
0.9319 2.5 5800 1.1877 0.2567
0.9414 2.55 5900 1.1730 0.2505
0.9428 2.59 6000 1.1721 0.2576
0.942 2.63 6100 1.1793 0.2547
0.9273 2.67 6200 1.1787 0.2570
0.9963 2.72 6300 1.1570 0.2540
0.9519 2.76 6400 1.1738 0.2563
0.962 2.8 6500 1.1929 0.2628
0.9765 2.85 6600 1.1531 0.2527
0.9226 2.89 6700 1.1577 0.2553
0.9492 2.93 6800 1.1490 0.2506
0.9186 2.98 6900 1.1402 0.2500
0.8681 3.02 7000 1.1520 0.2516
0.7738 3.06 7100 1.1404 0.2527
0.7605 3.11 7200 1.1535 0.2514
0.7254 3.15 7300 1.1679 0.2490
0.7422 3.19 7400 1.1536 0.2502
0.823 3.24 7500 1.1516 0.2477
0.7909 3.28 7600 1.1442 0.2459
0.7748 3.32 7700 1.1522 0.2493
0.7957 3.36 7800 1.1383 0.2470
0.7383 3.41 7900 1.1343 0.2452
0.8093 3.45 8000 1.1426 0.2467
0.8141 3.49 8100 1.1357 0.2466
0.7891 3.54 8200 1.1552 0.2480
0.8246 3.58 8300 1.1555 0.2475
0.7958 3.62 8400 1.1615 0.2502
0.7721 3.67 8500 1.1041 0.2396
0.7773 3.71 8600 1.1215 0.2411
0.7847 3.75 8700 1.1130 0.2419
0.7971 3.8 8800 1.1056 0.2469
0.7801 3.84 8900 1.1129 0.2435
0.7843 3.88 9000 1.1027 0.2387
0.7842 3.93 9100 1.0981 0.2401
0.7661 3.97 9200 1.1060 0.2428
0.7622 4.01 9300 1.0790 0.2338
0.6405 4.06 9400 1.0871 0.2352
0.6102 4.1 9500 1.0860 0.2344
0.6419 4.14 9600 1.0782 0.2356
0.6058 4.18 9700 1.0739 0.2291
0.6632 4.23 9800 1.1008 0.2366
0.6373 4.27 9900 1.0847 0.2354
0.6358 4.31 10000 1.0722 0.2313
0.6531 4.36 10100 1.0796 0.2326
0.6383 4.4 10200 1.0736 0.2322
0.6537 4.44 10300 1.0723 0.2305
0.6321 4.49 10400 1.0703 0.2329
0.6683 4.53 10500 1.0769 0.2332
0.6272 4.57 10600 1.0555 0.2292
0.651 4.62 10700 1.0570 0.2323
0.6392 4.66 10800 1.0738 0.2313
0.665 4.7 10900 1.0536 0.2276
0.677 4.75 11000 1.0554 0.2277
0.6419 4.79 11100 1.0487 0.2258
0.6549 4.83 11200 1.0427 0.2287
0.6373 4.87 11300 1.0502 0.2291
0.6642 4.92 11400 1.0411 0.2255
0.6674 4.96 11500 1.0345 0.2248
0.6733 5.0 11600 1.0440 0.2278
0.5281 5.05 11700 1.0477 0.2253
0.5465 5.09 11800 1.0553 0.2284
0.5375 5.13 11900 1.0550 0.2309
0.5103 5.18 12000 1.0433 0.2237
0.5196 5.22 12100 1.0534 0.2301
0.5645 5.26 12200 1.0492 0.2278
0.5421 5.31 12300 1.0515 0.2281
0.5234 5.35 12400 1.0383 0.2229
0.571 5.39 12500 1.0569 0.2278
0.5392 5.44 12600 1.0469 0.2253
0.5867 5.48 12700 1.0373 0.2264
0.5819 5.52 12800 1.0164 0.2237
0.5504 5.57 12900 1.0183 0.2217
0.5532 5.61 13000 1.0167 0.2232
0.5575 5.65 13100 1.0292 0.2244
0.5593 5.69 13200 1.0368 0.2247
0.5498 5.74 13300 1.0215 0.2231
0.5462 5.78 13400 1.0330 0.2212
0.5751 5.82 13500 1.0179 0.2223
0.5492 5.87 13600 1.0224 0.2202
0.5746 5.91 13700 1.0151 0.2219
0.5288 5.95 13800 1.0154 0.2199
0.5614 6.0 13900 1.0158 0.2210
0.4563 6.04 14000 1.0120 0.2197
0.502 6.08 14100 1.0125 0.2201
0.4896 6.13 14200 1.0011 0.2160
0.4774 6.17 14300 1.0027 0.2180
0.4734 6.21 14400 1.0026 0.2170
0.486 6.26 14500 0.9994 0.2177
0.4815 6.3 14600 0.9977 0.2174
0.4972 6.34 14700 1.0004 0.2175
0.4832 6.38 14800 0.9922 0.2130
0.4682 6.43 14900 0.9998 0.2167
0.4654 6.47 15000 0.9886 0.2150
0.4665 6.51 15100 0.9844 0.2154
0.4696 6.56 15200 0.9801 0.2136
0.4732 6.6 15300 0.9830 0.2145
0.4391 6.64 15400 0.9886 0.2165
0.5035 6.69 15500 0.9872 0.2157
0.4721 6.73 15600 0.9895 0.2132
0.466 6.77 15700 0.9910 0.2147
0.4981 6.82 15800 0.9934 0.2157
0.4856 6.86 15900 0.9888 0.2126
0.4798 6.9 16000 0.9830 0.2150
0.4771 6.95 16100 0.9845 0.2153
0.473 6.99 16200 0.9814 0.2116
0.4256 7.03 16300 0.9771 0.2131
0.4133 7.08 16400 0.9803 0.2125
0.4051 7.12 16500 0.9778 0.2116
0.4274 7.16 16600 0.9809 0.2116
0.4307 7.2 16700 0.9720 0.2109
0.4223 7.25 16800 0.9730 0.2109
0.4246 7.29 16900 0.9710 0.2100
0.4478 7.33 17000 0.9670 0.2101
0.4016 7.38 17100 0.9664 0.2096
0.4289 7.42 17200 0.9667 0.2093
0.4107 7.46 17300 0.9661 0.2096
0.4643 7.51 17400 0.9665 0.2106
0.433 7.55 17500 0.9673 0.2097
0.4239 7.59 17600 0.9639 0.2096
0.4144 7.64 17700 0.9635 0.2091
0.428 7.68 17800 0.9604 0.2094
0.4312 7.72 17900 0.9585 0.2099
0.4164 7.77 18000 0.9599 0.2093
0.4308 7.81 18100 0.9587 0.2080
0.4177 7.85 18200 0.9575 0.2084
0.4509 7.89 18300 0.9567 0.2082
0.4244 7.94 18400 0.9558 0.2072
0.4246 7.98 18500 0.9552 0.2071

Framework versions

  • Transformers 4.33.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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