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---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_6_1
model-index:
- name: wav2vec2-large-mms-1b-zh-CN
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-mms-1b-zh-CN
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/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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