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wav2vec2-xls-r-1b-italian-robust

Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Italian
wav2vec2
Generated from Trainer
hf-asr-leaderboard
robust-speech-event
Eval Results
Inference Endpoints
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metadata
language:
  - it
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-1b - Italian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: it
        metrics:
          - name: Test WER
            type: wer
            value: 32.74
          - name: Test CER
            type: cer
            value: 7.83
          - name: Test WER (+LM)
            type: wer
            value: 19.55
          - name: Test CER (+LM)
            type: cer
            value: 5.59
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: it
        metrics:
          - name: Test WER
            type: wer
            value: 43.23
          - name: Test CER
            type: cer
            value: 13.37
          - name: Test WER (+LM)
            type: wer
            value: 27.51
          - name: Test CER (+LM)
            type: cer
            value: 10.69
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: it
        metrics:
          - name: Test WER
            type: wer
            value: 51.12

wav2vec2-xls-r-1b-italian-robust

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the Common Voice 7 & Libri Speech datasets. It achieves the following results on the evaluation set:

  • Loss: 0.2428
  • Wer: 0.2960

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 10.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.07 400 1.0053 0.8058
1.5087 0.13 800 0.9127 0.8104
0.9552 0.2 1200 1.0360 0.8836
0.9555 0.27 1600 0.9980 0.8577
1.0259 0.34 2000 1.0103 0.8842
1.0259 0.4 2400 0.9119 0.8466
1.0365 0.47 2800 0.9000 0.8281
1.0069 0.54 3200 0.7976 0.7875
0.9688 0.61 3600 0.8126 0.8051
0.9638 0.67 4000 0.7921 0.7903
0.9638 0.74 4400 0.7703 0.7783
0.9327 0.81 4800 0.7253 0.7463
0.8992 0.88 5200 0.6841 0.7171
0.8693 0.94 5600 0.6867 0.7250
0.8433 1.01 6000 0.7077 0.7302
0.8433 1.08 6400 0.6685 0.7091
0.8499 1.14 6800 0.6355 0.6825
0.8159 1.21 7200 0.6283 0.6800
0.8001 1.28 7600 0.6288 0.6743
0.7883 1.35 8000 0.5995 0.6633
0.7883 1.41 8400 0.6195 0.6726
0.7863 1.48 8800 0.6039 0.6588
0.7713 1.55 9200 0.5842 0.6490
0.7572 1.62 9600 0.5975 0.6533
0.7442 1.68 10000 0.5508 0.6233
0.7442 1.75 10400 0.5521 0.6209
0.7296 1.82 10800 0.5760 0.6245
0.7205 1.89 11200 0.5593 0.6144
0.7106 1.95 11600 0.5672 0.6220
0.7146 2.02 12000 0.5134 0.5911
0.7146 2.09 12400 0.5069 0.5811
0.6944 2.15 12800 0.5022 0.5962
0.6817 2.22 13200 0.4989 0.5813
0.6721 2.29 13600 0.4941 0.5742
0.6774 2.36 14000 0.4775 0.5676
0.6774 2.42 14400 0.4694 0.5525
0.6621 2.49 14800 0.4720 0.5514
0.6599 2.56 15200 0.4714 0.5553
0.6591 2.63 15600 0.4578 0.5397
0.645 2.69 16000 0.4619 0.5452
0.645 2.76 16400 0.4578 0.5343
0.6431 2.83 16800 0.4514 0.5328
0.636 2.9 17200 0.4526 0.5325
0.6433 2.96 17600 0.4561 0.5325
0.6356 3.03 18000 0.4386 0.5191
0.6356 3.1 18400 0.4291 0.5065
0.6175 3.16 18800 0.4306 0.5170
0.6187 3.23 19200 0.4256 0.5036
0.607 3.3 19600 0.4198 0.5027
0.6004 3.37 20000 0.4149 0.4906
0.6004 3.43 20400 0.4114 0.4902
0.6002 3.5 20800 0.4116 0.4967
0.5926 3.57 21200 0.4066 0.4843
0.5836 3.64 21600 0.3956 0.4791
0.588 3.7 22000 0.3941 0.4729
0.588 3.77 22400 0.3972 0.4799
0.5739 3.84 22800 0.4018 0.4790
0.5778 3.91 23200 0.3936 0.4750
0.5768 3.97 23600 0.3936 0.4751
0.5651 4.04 24000 0.3953 0.4706
0.5651 4.11 24400 0.3906 0.4659
0.5704 4.17 24800 0.3807 0.4557
0.5594 4.24 25200 0.3817 0.4610
0.5509 4.31 25600 0.3755 0.4553
0.5439 4.38 26000 0.3705 0.4471
0.5439 4.44 26400 0.3744 0.4487
0.5426 4.51 26800 0.3716 0.4483
0.5393 4.58 27200 0.3600 0.4356
0.5408 4.65 27600 0.3573 0.4307
0.5327 4.71 28000 0.3638 0.4382
0.5327 4.78 28400 0.3587 0.4316
0.5324 4.85 28800 0.3598 0.4290
0.5378 4.91 29200 0.3508 0.4243
0.5246 4.98 29600 0.3522 0.4260
0.5284 5.05 30000 0.3520 0.4268
0.5284 5.12 30400 0.3506 0.4224
0.5154 5.18 30800 0.3556 0.4223
0.5138 5.25 31200 0.3526 0.4276
0.51 5.32 31600 0.3440 0.4220
0.5065 5.39 32000 0.3367 0.4120
0.5065 5.45 32400 0.3406 0.4136
0.5087 5.52 32800 0.3370 0.4125
0.503 5.59 33200 0.3387 0.4134
0.5085 5.66 33600 0.3346 0.4068
0.5044 5.72 34000 0.3325 0.4057
0.5044 5.79 34400 0.3304 0.4026
0.4879 5.86 34800 0.3274 0.4002
0.4924 5.92 35200 0.3286 0.3980
0.4991 5.99 35600 0.3231 0.3952
0.487 6.06 36000 0.3324 0.4005
0.487 6.13 36400 0.3264 0.3952
0.4754 6.19 36800 0.3234 0.3905
0.4683 6.26 37200 0.3149 0.3840
0.4653 6.33 37600 0.3122 0.3824
0.4667 6.4 38000 0.3151 0.3855
0.4667 6.46 38400 0.3217 0.3859
0.4628 6.53 38800 0.3085 0.3831
0.4644 6.6 39200 0.3121 0.3791
0.4612 6.67 39600 0.3093 0.3790
0.4552 6.73 40000 0.3087 0.3749
0.4552 6.8 40400 0.3027 0.3679
0.4544 6.87 40800 0.3048 0.3672
0.4507 6.93 41200 0.2963 0.3614
0.4489 7.0 41600 0.3086 0.3718
0.4367 7.07 42000 0.3100 0.3754
0.4367 7.14 42400 0.3057 0.3701
0.4376 7.2 42800 0.2930 0.3614
0.428 7.27 43200 0.2907 0.3516
0.4241 7.34 43600 0.2916 0.3590
0.4312 7.41 44000 0.2904 0.3523
0.4312 7.47 44400 0.2908 0.3476
0.4292 7.54 44800 0.2858 0.3467
0.426 7.61 45200 0.2864 0.3484
0.4225 7.68 45600 0.2820 0.3441
0.422 7.74 46000 0.2834 0.3441
0.422 7.81 46400 0.2784 0.3420
0.4158 7.88 46800 0.2814 0.3390
0.4139 7.94 47200 0.2777 0.3384
0.4076 8.01 47600 0.2741 0.3381
0.3997 8.08 48000 0.2738 0.3320
0.3997 8.15 48400 0.2720 0.3303
0.4009 8.21 48800 0.2705 0.3357
0.3928 8.28 49200 0.2708 0.3265
0.3923 8.35 49600 0.2678 0.3283
0.3897 8.42 50000 0.2649 0.3241
0.3897 8.48 50400 0.2640 0.3218
0.3879 8.55 50800 0.2616 0.3197
0.3805 8.62 51200 0.2599 0.3170
0.3874 8.69 51600 0.2592 0.3168
0.3799 8.75 52000 0.2589 0.3157
0.3799 8.82 52400 0.2566 0.3137
0.3834 8.89 52800 0.2552 0.3141
0.3811 8.95 53200 0.2523 0.3108
0.3821 9.02 53600 0.2539 0.3112
0.3636 9.09 54000 0.2529 0.3070
0.3636 9.16 54400 0.2500 0.3078
0.3706 9.22 54800 0.2510 0.3067
0.367 9.29 55200 0.2497 0.3069
0.3618 9.36 55600 0.2493 0.3043
0.3624 9.43 56000 0.2491 0.3040
0.3624 9.49 56400 0.2466 0.3016
0.3557 9.56 56800 0.2460 0.3014
0.3536 9.63 57200 0.2470 0.2997
0.3584 9.7 57600 0.2441 0.2989
0.3563 9.76 58000 0.2442 0.2970
0.3563 9.83 58400 0.2436 0.2966
0.3492 9.9 58800 0.2431 0.2967
0.3483 9.96 59200 0.2428 0.2960

Framework versions

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