Hugging Face's logo

saattrupdan
/
wav2vec2-xls-r-300m-ftspeech

Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Danish
wav2vec2
Eval Results
Inference Endpoints
Model card Files Community
2
wav2vec2-xls-r-300m-ftspeech / README.md
saattrupdan's picture
saattrupdan
update model card README.md
75f9b2e
preview code
|
raw history blame
No virus
4.84 kB
metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: wav2vec2-xls-r-300m-ftspeech
    results: []

wav2vec2-xls-r-300m-ftspeech

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 17.8348
  • Wer: 0.1186

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: 0.0001
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 4242
  • gradient_accumulation_steps: 32
  • 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: 2000
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
311.274 0.02 500 329.9529 1.0
296.713 0.03 1000 305.5616 1.0000
69.6128 0.05 1500 77.2267 0.6466
47.4542 0.06 2000 56.0227 0.5101
39.2415 0.08 2500 40.1751 0.3483
35.9888 0.1 3000 33.1659 0.2619
34.1621 0.11 3500 30.4220 0.2296
32.3383 0.13 4000 28.3836 0.2214
31.1862 0.14 4500 28.7228 0.2220
29.818 0.16 5000 28.3220 0.2259
29.4729 0.18 5500 26.5646 0.2024
27.6171 0.19 6000 26.3382 0.1995
27.4549 0.21 6500 24.1257 0.1697
27.9176 0.22 7000 24.8758 0.1945
27.4036 0.24 7500 24.1006 0.1746
26.5633 0.26 8000 23.0034 0.1582
26.3558 0.27 8500 24.7499 0.1913
25.9604 0.29 9000 22.5813 0.1674
25.6154 0.31 9500 22.4642 0.1499
25.6231 0.32 10000 21.8089 0.1534
26.7554 0.34 10500 21.9619 0.1543
25.2901 0.35 11000 22.0643 0.1593
24.8642 0.37 11500 21.1113 0.1480
25.4664 0.39 12000 21.2492 0.1458
24.6433 0.4 12500 20.7650 0.1419
24.8455 0.42 13000 21.8535 0.1490
25.1176 0.43 13500 20.7491 0.1429
24.4585 0.45 14000 20.7948 0.1423
24.1613 0.47 14500 20.5817 0.1431
23.7281 0.48 15000 20.1209 0.1333
23.0396 0.5 15500 20.2883 0.1383
24.7056 0.51 16000 19.6813 0.1330
23.608 0.53 16500 20.0252 0.1394
23.9536 0.55 17000 19.9039 0.1341
23.1848 0.56 17500 19.9114 0.1308
23.1835 0.58 18000 19.7044 0.1345
23.9372 0.59 18500 19.2201 0.1296
23.2182 0.61 19000 19.3723 0.1350
22.3118 0.63 19500 19.2624 0.1344
22.9372 0.64 20000 19.5823 0.1387
23.1536 0.66 20500 18.9077 0.1289
22.3477 0.67 21000 18.7098 0.1257
22.3701 0.69 21500 19.0815 0.1300
22.6709 0.71 22000 18.4433 0.1242
22.2519 0.72 22500 18.7482 0.1275
21.8536 0.74 23000 18.6565 0.1236
22.4479 0.76 23500 18.6478 0.1264
21.6824 0.77 24000 18.4383 0.1257
22.1622 0.79 24500 18.4086 0.1212
22.2626 0.8 25000 18.4613 0.1230
21.0009 0.82 25500 18.1851 0.1165
20.554 0.84 26000 17.7352 0.1165
21.5141 0.85 26500 18.3084 0.1207
20.5925 0.87 27000 17.9997 0.1207
21.0997 0.88 27500 17.7534 0.1193
21.7098 0.9 28000 17.8348 0.1186

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0