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Pushing language model
53298d9
metadata
base_model: facebook/wav2vec2-xls-r-300m
license: apache-2.0
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: bambara-5-hours-bambara-asr-hf
    results: []

bambara-5-hours-bambara-asr-hf

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8042
  • Wer: 0.5257
  • Cer: 0.2399

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: 16
  • 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: 50

Training results

Training Loss Epoch Step Validation Loss Wer Cer
5.6981 1.13 200 2.9919 1.0 1.0
2.9496 2.27 400 2.9333 1.0 1.0
2.4081 3.4 600 1.4375 0.8869 0.4174
1.5268 4.53 800 1.1942 0.7734 0.3602
1.3291 5.67 1000 1.1261 0.6712 0.2994
1.2301 6.8 1200 1.0859 0.6470 0.2892
1.1379 7.93 1400 1.0552 0.6314 0.2813
1.0664 9.07 1600 1.0684 0.6079 0.2731
0.9866 10.2 1800 1.0584 0.6110 0.2756
0.9625 11.33 2000 1.0776 0.5757 0.2590
0.8828 12.46 2200 1.1372 0.5865 0.2596
0.8451 13.6 2400 1.0821 0.5645 0.2574
0.8016 14.73 2600 1.1293 0.5754 0.2608
0.7615 15.86 2800 1.1312 0.5586 0.2519
0.715 17.0 3000 1.1657 0.5635 0.2558
0.6792 18.13 3200 1.2197 0.5521 0.2517
0.6498 19.26 3400 1.1157 0.5606 0.2533
0.6221 20.4 3600 1.2632 0.5501 0.2507
0.5912 21.53 3800 1.1686 0.5520 0.2505
0.5614 22.66 4000 1.3080 0.5547 0.2542
0.5485 23.8 4200 1.2349 0.5601 0.2583
0.5235 24.93 4400 1.2541 0.5458 0.2489
0.5084 26.06 4600 1.2414 0.5500 0.2514
0.4741 27.2 4800 1.5119 0.5444 0.2470
0.4699 28.33 5000 1.2778 0.5525 0.2524
0.4511 29.46 5200 1.5488 0.5502 0.2494
0.4181 30.59 5400 1.3489 0.5522 0.2536
0.4149 31.73 5600 1.5685 0.5460 0.2454
0.3998 32.86 5800 1.4369 0.5434 0.2494
0.401 33.99 6000 1.5961 0.5376 0.2433
0.3744 35.13 6200 1.5695 0.5361 0.2452
0.36 36.26 6400 1.5968 0.5400 0.2445
0.3435 37.39 6600 1.6238 0.5334 0.2424
0.3337 38.53 6800 1.6619 0.5340 0.2440
0.3232 39.66 7000 1.6444 0.5345 0.2446
0.3224 40.79 7200 1.7175 0.5417 0.2451
0.303 41.93 7400 1.6966 0.5273 0.2417
0.3028 43.06 7600 1.7403 0.5325 0.2431
0.2899 44.19 7800 1.7688 0.5255 0.2398
0.2861 45.33 8000 1.7705 0.5269 0.2405
0.2744 46.46 8200 1.7792 0.5244 0.2396
0.2731 47.59 8400 1.7876 0.5286 0.2412
0.27 48.73 8600 1.8060 0.5264 0.2403
0.2643 49.86 8800 1.8042 0.5257 0.2399

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.17.0
  • Tokenizers 0.15.2