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MMS-Wolof-20-hour-Mixed-dataset

Automatic Speech Recognition
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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: MMS-Wolof-20-hour-Mixed-dataset
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: wo_sn
          split: None
          args: wo_sn
        metrics:
          - name: Wer
            type: wer
            value: 0.4748502317169662

Visualize in Weights & Biases

MMS-Wolof-20-hour-Mixed-dataset

This model is a fine-tuned version of facebook/mms-1b-all on the fleurs dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3654
  • Wer: 0.4749
  • Cer: 0.1708

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: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Wer Cer
9.5604 0.7369 500 0.7373 0.5227 0.1732
2.6975 1.4738 1000 0.6973 0.5301 0.1779
2.726 2.2108 1500 0.8814 0.5966 0.2092
2.8093 2.9477 2000 0.8858 0.6151 0.2303
2.78 3.6846 2500 0.8017 0.5918 0.2146
2.8066 4.4215 3000 1.0143 0.6655 0.2453
2.8916 5.1584 3500 0.8609 0.6299 0.2351
2.7853 5.8954 4000 0.8444 0.6100 0.2304
2.6374 6.6323 4500 0.9584 0.6420 0.2505
2.5197 7.3692 5000 0.9859 0.6703 0.2562
2.4943 8.1061 5500 0.8587 0.6267 0.2405
2.39 8.8430 6000 0.9456 0.6221 0.2388
2.2478 9.5800 6500 0.9030 0.6598 0.2536
2.1688 10.3169 7000 0.8462 0.6527 0.2567
2.0468 11.0538 7500 0.8851 0.6080 0.2302
1.9318 11.7907 8000 0.8898 0.6276 0.2301
1.8937 12.5276 8500 0.7846 0.5826 0.2191
1.7943 13.2646 9000 0.8560 0.6137 0.2326
1.7337 14.0015 9500 0.8789 0.6005 0.2271
1.608 14.7384 10000 0.8736 0.6092 0.2270
1.5533 15.4753 10500 0.9045 0.5951 0.2287
1.4501 16.2122 11000 0.8505 0.6288 0.2415
1.4074 16.9492 11500 0.8023 0.5853 0.2227
1.3339 17.6861 12000 0.8177 0.5967 0.2211
1.2208 18.4230 12500 0.8922 0.5761 0.2126
1.1803 19.1599 13000 0.8207 0.5637 0.2076
1.1159 19.8968 13500 0.8114 0.5473 0.2013
1.0415 20.6338 14000 0.8646 0.5533 0.2036
0.9767 21.3707 14500 0.9001 0.5569 0.2088
0.9803 22.1076 15000 0.8485 0.5696 0.2100
0.8933 22.8445 15500 0.8164 0.5452 0.2041
0.8509 23.5814 16000 0.9136 0.5545 0.2037
0.8398 24.3183 16500 0.8095 0.5350 0.1950
0.7741 25.0553 17000 0.9116 0.5448 0.1998
0.7303 25.7922 17500 0.9380 0.5339 0.1948
0.7132 26.5291 18000 0.8357 0.5143 0.1888
0.6655 27.2660 18500 0.9127 0.5495 0.2049
0.6452 28.0029 19000 0.8722 0.5258 0.1933
0.5913 28.7399 19500 0.9262 0.5227 0.1929
0.5792 29.4768 20000 0.9722 0.5239 0.1883
0.5528 30.2137 20500 0.9868 0.5259 0.1937
0.5488 30.9506 21000 0.9860 0.5268 0.1945
0.5023 31.6875 21500 0.9549 0.5134 0.1874
0.4668 32.4245 22000 1.0188 0.5200 0.1943
0.4751 33.1614 22500 1.0139 0.5112 0.1852
0.434 33.8983 23000 1.0354 0.5073 0.1815
0.4149 34.6352 23500 0.9920 0.5170 0.1874
0.4044 35.3721 24000 1.1387 0.5051 0.1840
0.3839 36.1091 24500 1.1052 0.5034 0.1848
0.3576 36.8460 25000 1.0593 0.4889 0.1811
0.3379 37.5829 25500 1.0930 0.5007 0.1823
0.336 38.3198 26000 1.1091 0.4968 0.1808
0.3148 39.0567 26500 1.1871 0.4993 0.1810
0.3005 39.7937 27000 1.1890 0.4993 0.1801
0.2964 40.5306 27500 1.1436 0.4899 0.1758
0.2731 41.2675 28000 1.1677 0.4940 0.1780
0.2641 42.0044 28500 1.1943 0.4943 0.1783
0.2435 42.7413 29000 1.2838 0.4906 0.1787
0.2486 43.4783 29500 1.2935 0.4938 0.1767
0.2259 44.2152 30000 1.3013 0.4898 0.1749
0.218 44.9521 30500 1.2965 0.4856 0.1749
0.2143 45.6890 31000 1.2891 0.4823 0.1739
0.195 46.4259 31500 1.3284 0.4791 0.1724
0.1943 47.1629 32000 1.3182 0.4805 0.1734
0.1851 47.8998 32500 1.3429 0.4769 0.1719
0.1833 48.6367 33000 1.3515 0.4723 0.1708
0.1739 49.3736 33500 1.3654 0.4749 0.1708

Framework versions

  • Transformers 4.46.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.17.0
  • Tokenizers 0.20.3