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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
Model card Files Community
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metadata
language:
  - it
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - 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: 0.296

wav2vec2-xls-r-1b-italian-augmented-cv-ls

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the None dataset. 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