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

Automatic Speech Recognition Transformers PyTorch
wav2vec2 common_voice generated_from_trainer hf-asr-leaderboard robust-speech-event zh Eval Results 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
  - zh
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-300m-zh-CN
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: zh-CN
        metrics:
          - name: Test WER
            type: wer
            value: 80
          - name: Test CER
            type: cer
            value: 40.11
      - 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: 69.1
      - 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: 43.08

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

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: 0.8828
  • Wer: 2.0604

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: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
60.2112 0.74 500 64.8189 1.0
8.1128 1.48 1000 6.8997 1.0
6.0492 2.22 1500 5.9677 1.9495
5.9326 2.95 2000 5.8845 1.4092
5.8763 3.69 2500 5.8460 1.6126
5.7888 4.43 3000 5.7545 2.2034
5.735 5.17 3500 5.6777 2.3350
5.6861 5.91 4000 5.5179 2.2232
5.381 6.65 4500 5.1420 2.1816
4.625 7.39 5000 3.9020 2.0722
4.214 8.12 5500 3.3394 2.1430
3.8992 8.86 6000 2.9085 2.1534
3.6481 9.6 6500 2.6208 2.3538
3.4658 10.34 7000 2.3172 2.2271
3.257 11.08 7500 2.0916 2.1351
3.1294 11.82 8000 1.8954 2.2133
3.0266 12.56 8500 1.7673 2.0896
2.9451 13.29 9000 1.6659 2.1381
2.8802 14.03 9500 1.5637 2.1969
2.78 14.77 10000 1.4921 2.2335
2.7049 15.51 10500 1.4132 2.2217
2.6768 16.25 11000 1.3667 2.2232
2.6358 16.99 11500 1.3111 2.1286
2.5802 17.72 12000 1.2679 2.1430
2.5012 18.46 12500 1.2365 2.1153
2.458 19.2 13000 1.2118 2.1573
2.4433 19.94 13500 1.1992 2.1336
2.438 20.68 14000 1.1803 2.1509
2.418 21.42 14500 1.1601 2.1232
2.3322 22.16 15000 1.1418 2.1930
2.3387 22.89 15500 1.1172 2.2464
2.3349 23.63 16000 1.1144 2.1856
2.291 24.37 16500 1.1018 2.1930
2.2766 25.11 17000 1.0883 2.1762
2.2534 25.85 17500 1.0744 2.1875
2.2393 26.59 18000 1.0561 2.1846
2.2085 27.33 18500 1.0466 2.1445
2.1966 28.06 19000 1.0382 2.1089
2.1794 28.8 19500 1.0264 1.9861
2.1423 29.54 20000 1.0246 1.9678
2.1649 30.28 20500 0.9982 2.0005
2.143 31.02 21000 0.9985 2.0450
2.1338 31.76 21500 0.9932 2.0025
2.1076 32.5 22000 0.9903 2.0505
2.0519 33.23 22500 0.9834 2.0737
2.0534 33.97 23000 0.9756 2.0247
2.0121 34.71 23500 0.9688 2.1440
2.0161 35.45 24000 0.9582 2.1232
2.0178 36.19 24500 0.9480 2.0896
2.0154 36.93 25000 0.9483 2.0787
1.9966 37.67 25500 0.9406 2.0297
1.9753 38.4 26000 0.9419 2.0346
1.9524 39.14 26500 0.9274 2.0698
1.9427 39.88 27000 0.9233 2.0787
1.9258 40.62 27500 0.9182 2.0529
1.9031 41.36 28000 0.9150 2.0787
1.9297 42.1 28500 0.9040 2.0505
1.9041 42.84 29000 0.9009 2.0579
1.8929 43.57 29500 0.8968 2.0327
1.9077 44.31 30000 0.8954 2.0619
1.8504 45.05 30500 0.8922 2.0737
1.8732 45.79 31000 0.8898 2.0683
1.877 46.53 31500 0.8849 2.0589
1.8587 47.27 32000 0.8843 2.0450
1.8236 48.01 32500 0.8810 2.0554
1.8392 48.74 33000 0.8820 2.0574
1.8428 49.48 33500 0.8816 2.0668

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_7_0 with split test
python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-zh-CN --dataset mozilla-foundation/common_voice_7_0 --config zh-CN --split test
  1. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-zh-CN --dataset speech-recognition-community-v2/dev_data --config zh-CN --split validation --chunk_length_s 5.0 --stride_length_s 1.0