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wav2vec2-xls-r-300m-zh-CN

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
common_voice
Generated from Trainer
hf-asr-leaderboard
robust-speech-event
sv
Inference Endpoints
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metadata
language:
  - zh-CN
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  - sv
datasets:
  - common_voice
model-index:
  - name: ''
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: zh-CN
        metrics:
          - name: Test CER
            type: cer
            value: 66.22
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: zh-CN
        metrics:
          - name: Test CER
            type: cer
            value: 37.51

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - ZH-CN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8122
  • Wer: 0.8392
  • Cer: 0.2059

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: 7.5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
69.215 0.74 500 74.9751 1.0 1.0
8.2109 1.48 1000 7.0617 1.0 1.0
6.4277 2.22 1500 6.3811 1.0 1.0
6.3513 2.95 2000 6.3061 1.0 1.0
6.2522 3.69 2500 6.2147 1.0 1.0
5.9757 4.43 3000 5.7906 1.1004 0.9924
5.0642 5.17 3500 4.2984 1.7729 0.8214
4.6346 5.91 4000 3.7129 1.8946 0.7728
4.267 6.65 4500 3.2177 1.7526 0.6922
3.9964 7.39 5000 2.8337 1.8055 0.6546
3.8035 8.12 5500 2.5726 2.1851 0.6992
3.6273 8.86 6000 2.3391 2.1029 0.6511
3.5248 9.6 6500 2.1944 2.3617 0.6859
3.3683 10.34 7000 1.9827 2.1014 0.6063
3.2411 11.08 7500 1.8610 1.6160 0.5135
3.1299 11.82 8000 1.7446 1.5948 0.4946
3.0574 12.56 8500 1.6454 1.1291 0.4051
2.985 13.29 9000 1.5919 1.0673 0.3893
2.9573 14.03 9500 1.4903 1.0604 0.3766
2.8897 14.77 10000 1.4614 1.0059 0.3653
2.8169 15.51 10500 1.3997 1.0030 0.3550
2.8155 16.25 11000 1.3444 0.9980 0.3441
2.7595 16.99 11500 1.2911 0.9703 0.3325
2.7107 17.72 12000 1.2462 0.9565 0.3227
2.6358 18.46 12500 1.2466 0.9955 0.3333
2.5801 19.2 13000 1.2059 1.0010 0.3226
2.5554 19.94 13500 1.1919 1.0094 0.3223
2.5314 20.68 14000 1.1703 0.9847 0.3156
2.509 21.42 14500 1.1733 0.9896 0.3177
2.4391 22.16 15000 1.1811 0.9723 0.3164
2.4631 22.89 15500 1.1382 0.9698 0.3059
2.4414 23.63 16000 1.0893 0.9644 0.2972
2.3771 24.37 16500 1.0930 0.9505 0.2954
2.3658 25.11 17000 1.0756 0.9609 0.2926
2.3215 25.85 17500 1.0512 0.9614 0.2890
2.3327 26.59 18000 1.0627 1.1984 0.3282
2.3055 27.33 18500 1.0582 0.9520 0.2841
2.299 28.06 19000 1.0356 0.9480 0.2817
2.2673 28.8 19500 1.0305 0.9367 0.2771
2.2166 29.54 20000 1.0139 0.9223 0.2702
2.2378 30.28 20500 1.0095 0.9268 0.2722
2.2168 31.02 21000 1.0001 0.9085 0.2691
2.1766 31.76 21500 0.9884 0.9050 0.2640
2.1715 32.5 22000 0.9730 0.9505 0.2719
2.1104 33.23 22500 0.9752 0.9362 0.2656
2.1158 33.97 23000 0.9720 0.9263 0.2624
2.0718 34.71 23500 0.9573 1.0005 0.2759
2.0824 35.45 24000 0.9609 0.9525 0.2643
2.0591 36.19 24500 0.9662 0.9570 0.2667
2.0768 36.93 25000 0.9528 0.9574 0.2646
2.0893 37.67 25500 0.9810 0.9169 0.2612
2.0282 38.4 26000 0.9556 0.8877 0.2528
1.997 39.14 26500 0.9523 0.8723 0.2501
2.0209 39.88 27000 0.9542 0.8773 0.2503
1.987 40.62 27500 0.9427 0.8867 0.2500
1.9663 41.36 28000 0.9546 0.9065 0.2546
1.9945 42.1 28500 0.9431 0.9119 0.2536
1.9604 42.84 29000 0.9367 0.9030 0.2490
1.933 43.57 29500 0.9071 0.8916 0.2432
1.9227 44.31 30000 0.9048 0.8882 0.2428
1.8784 45.05 30500 0.9106 0.8991 0.2437
1.8844 45.79 31000 0.8996 0.8758 0.2379
1.8776 46.53 31500 0.9028 0.8798 0.2395
1.8372 47.27 32000 0.9047 0.8778 0.2379
1.832 48.01 32500 0.9016 0.8941 0.2393
1.8154 48.74 33000 0.8915 0.8916 0.2372
1.8072 49.48 33500 0.8781 0.8872 0.2365
1.7489 50.22 34000 0.8738 0.8956 0.2340
1.7928 50.96 34500 0.8684 0.8872 0.2323
1.7748 51.7 35000 0.8723 0.8718 0.2321
1.7355 52.44 35500 0.8760 0.8842 0.2331
1.7167 53.18 36000 0.8746 0.8817 0.2324
1.7479 53.91 36500 0.8762 0.8753 0.2281
1.7428 54.65 37000 0.8733 0.8699 0.2277
1.7058 55.39 37500 0.8816 0.8649 0.2263
1.7045 56.13 38000 0.8733 0.8689 0.2297
1.709 56.87 38500 0.8648 0.8654 0.2232
1.6799 57.61 39000 0.8717 0.8580 0.2244
1.664 58.35 39500 0.8653 0.8723 0.2259
1.6488 59.08 40000 0.8637 0.8803 0.2271
1.6298 59.82 40500 0.8553 0.8768 0.2253
1.6185 60.56 41000 0.8512 0.8718 0.2240
1.574 61.3 41500 0.8579 0.8773 0.2251
1.6192 62.04 42000 0.8499 0.8743 0.2242
1.6275 62.78 42500 0.8419 0.8758 0.2216
1.5697 63.52 43000 0.8446 0.8699 0.2222
1.5384 64.25 43500 0.8462 0.8580 0.2200
1.5115 64.99 44000 0.8467 0.8674 0.2214
1.5547 65.73 44500 0.8505 0.8669 0.2204
1.5597 66.47 45000 0.8421 0.8684 0.2192
1.505 67.21 45500 0.8485 0.8619 0.2187
1.5101 67.95 46000 0.8489 0.8649 0.2204
1.5199 68.69 46500 0.8407 0.8619 0.2180
1.5207 69.42 47000 0.8379 0.8496 0.2163
1.478 70.16 47500 0.8357 0.8595 0.2163
1.4817 70.9 48000 0.8346 0.8496 0.2151
1.4827 71.64 48500 0.8362 0.8624 0.2169
1.4513 72.38 49000 0.8355 0.8451 0.2137
1.4988 73.12 49500 0.8325 0.8624 0.2161
1.4267 73.85 50000 0.8396 0.8481 0.2157
1.4421 74.59 50500 0.8355 0.8491 0.2122
1.4311 75.33 51000 0.8358 0.8476 0.2118
1.4174 76.07 51500 0.8289 0.8451 0.2101
1.4349 76.81 52000 0.8372 0.8580 0.2140
1.3959 77.55 52500 0.8325 0.8436 0.2116
1.4087 78.29 53000 0.8351 0.8446 0.2105
1.415 79.03 53500 0.8363 0.8476 0.2123
1.4122 79.76 54000 0.8310 0.8481 0.2112
1.3969 80.5 54500 0.8239 0.8446 0.2095
1.361 81.24 55000 0.8282 0.8427 0.2091
1.3611 81.98 55500 0.8282 0.8407 0.2092
1.3677 82.72 56000 0.8235 0.8436 0.2084
1.3361 83.46 56500 0.8231 0.8377 0.2069
1.3779 84.19 57000 0.8206 0.8436 0.2070
1.3727 84.93 57500 0.8204 0.8392 0.2065
1.3317 85.67 58000 0.8207 0.8436 0.2065
1.3332 86.41 58500 0.8186 0.8357 0.2055
1.3299 87.15 59000 0.8193 0.8417 0.2075
1.3129 87.89 59500 0.8183 0.8431 0.2065
1.3352 88.63 60000 0.8151 0.8471 0.2062
1.3026 89.36 60500 0.8125 0.8486 0.2067
1.3468 90.1 61000 0.8124 0.8407 0.2058
1.3028 90.84 61500 0.8122 0.8461 0.2051
1.2884 91.58 62000 0.8086 0.8427 0.2048
1.3005 92.32 62500 0.8110 0.8387 0.2055
1.2996 93.06 63000 0.8126 0.8328 0.2057
1.2707 93.8 63500 0.8098 0.8402 0.2047
1.3026 94.53 64000 0.8097 0.8402 0.2050
1.2546 95.27 64500 0.8111 0.8402 0.2055
1.2426 96.01 65000 0.8088 0.8372 0.2059
1.2869 96.75 65500 0.8093 0.8397 0.2048
1.2782 97.49 66000 0.8099 0.8412 0.2049
1.2457 98.23 66500 0.8134 0.8412 0.2062
1.2967 98.97 67000 0.8115 0.8382 0.2055
1.2817 99.7 67500 0.8128 0.8392 0.2063

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3.dev0
  • Tokenizers 0.11.0