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metaasr-1-b-mcv-11-v-1

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-1b-swahili-v12
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: sw
          split: test
          args: sw
        metrics:
          - name: Wer
            type: wer
            value: 0.20382121671954753

wav2vec2-large-xls-r-1b-swahili-v12

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4658
  • Wer: 0.2038

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: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.726 0.35 400 0.7214 0.6674
0.5241 0.69 800 0.5641 0.5345
0.4616 1.04 1200 0.5112 0.4755
0.4018 1.39 1600 0.4797 0.4158
0.3916 1.74 2000 0.4483 0.3985
0.3661 2.08 2400 0.4449 0.3931
0.3314 2.43 2800 0.4124 0.3549
0.3287 2.78 3200 0.4008 0.3651
0.317 3.13 3600 0.4460 0.3735
0.3026 3.47 4000 0.4165 0.3753
0.3061 3.82 4400 0.4112 0.3550
0.2808 4.17 4800 0.3951 0.3275
0.2641 4.52 5200 0.3934 0.3340
0.2709 4.86 5600 0.3963 0.3287
0.2586 5.21 6000 0.4114 0.3396
0.2487 5.56 6400 0.3821 0.3214
0.2618 5.91 6800 0.3987 0.3268
0.2297 6.25 7200 0.3810 0.3132
0.2337 6.6 7600 0.3740 0.3131
0.2285 6.95 8000 0.3715 0.3093
0.2173 7.29 8400 0.3878 0.3147
0.2251 7.64 8800 0.3862 0.3134
0.2215 7.99 9200 0.3621 0.2940
0.195 8.34 9600 0.3651 0.3005
0.201 8.68 10000 0.3837 0.3167
0.1964 9.03 10400 0.3719 0.2876
0.1741 9.38 10800 0.3637 0.2840
0.181 9.73 11200 0.3616 0.2914
0.1795 10.07 11600 0.3719 0.2753
0.1602 10.42 12000 0.3618 0.2856
0.1753 10.77 12400 0.3570 0.2788
0.1627 11.12 12800 0.3500 0.2719
0.1566 11.46 13200 0.3553 0.2808
0.1589 11.81 13600 0.3635 0.2699
0.1511 12.16 14000 0.3656 0.2692
0.1451 12.51 14400 0.3759 0.2759
0.1444 12.85 14800 0.3607 0.2677
0.1359 13.2 15200 0.3852 0.2660
0.1313 13.55 15600 0.3587 0.2679
0.1329 13.89 16000 0.3548 0.2584
0.1163 14.24 16400 0.3701 0.2535
0.1175 14.59 16800 0.3693 0.2638
0.1242 14.94 17200 0.3660 0.2565
0.1067 15.28 17600 0.3835 0.2581
0.1077 15.63 18000 0.3799 0.2504
0.1099 15.98 18400 0.3598 0.2478
0.0952 16.33 18800 0.3865 0.2563
0.1007 16.67 19200 0.3630 0.2565
0.0999 17.02 19600 0.3912 0.2505
0.0895 17.37 20000 0.3934 0.2631
0.0974 17.72 20400 0.3718 0.2462
0.0939 18.06 20800 0.4001 0.2587
0.0915 18.41 21200 0.4048 0.2468
0.0865 18.76 21600 0.3860 0.2415
0.0784 19.11 22000 0.4148 0.2454
0.0782 19.45 22400 0.3952 0.2471
0.0775 19.8 22800 0.3943 0.2434
0.0735 20.15 23200 0.4093 0.2405
0.0679 20.5 23600 0.3996 0.2362
0.0677 20.84 24000 0.4133 0.2365
0.0687 21.19 24400 0.4303 0.2330
0.0651 21.54 24800 0.4288 0.2326
0.0647 21.88 25200 0.4134 0.2347
0.0634 22.23 25600 0.4148 0.2312
0.0592 22.58 26000 0.4322 0.2315
0.06 22.93 26400 0.4050 0.2313
0.0561 23.27 26800 0.4260 0.2263
0.0546 23.62 27200 0.4228 0.2238
0.0548 23.97 27600 0.4140 0.2258
0.0505 24.32 28000 0.4304 0.2246
0.0501 24.66 28400 0.4241 0.2233
0.0481 25.01 28800 0.4385 0.2209
0.0469 25.36 29200 0.4451 0.2189
0.0464 25.71 29600 0.4397 0.2217
0.0438 26.05 30000 0.4419 0.2154
0.0432 26.4 30400 0.4366 0.2137
0.0419 26.75 30800 0.4371 0.2137
0.0419 27.1 31200 0.4552 0.2109
0.0392 27.44 31600 0.4496 0.2108
0.0386 27.79 32000 0.4585 0.2096
0.0387 28.14 32400 0.4496 0.2065
0.0367 28.48 32800 0.4646 0.2082
0.0357 28.83 33200 0.4553 0.2067
0.0355 29.18 33600 0.4615 0.2055
0.0345 29.53 34000 0.4670 0.2046
0.0346 29.87 34400 0.4658 0.2038

Framework versions

  • Transformers 4.29.0.dev0
  • Pytorch 2.0.0+cu117
  • Datasets 2.12.0
  • Tokenizers 0.13.3