Hugging Face's logo

lgris
/
wavlm-large-CORAA-pt-cv7

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Portuguese
wavlm
mozilla-foundation/common_voice_7_0
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
wavlm-large-CORAA-pt-cv7 / README.md
lgris's picture
lgris
Update README.md
abbdddf
preview code
|
raw
history blame contribute delete
4.51 kB
metadata
language:
  - pt
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - pt
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: wavlm-large-CORAA-pt-cv7
    results: []

wavlm-large-CORAA-pt-cv7

This model is a fine-tuned version of lgris/WavLM-large-CORAA-pt on the common_voice dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2546
  • Wer: 0.2261

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • training_steps: 5000

Training results

Training Loss Epoch Step Validation Loss Wer
0.6029 0.13 100 0.3679 0.3347
0.5297 0.26 200 0.3516 0.3227
0.5134 0.39 300 0.3327 0.3167
0.4941 0.52 400 0.3281 0.3122
0.4816 0.65 500 0.3154 0.3102
0.4649 0.78 600 0.3199 0.3058
0.461 0.91 700 0.3047 0.2974
0.4613 1.04 800 0.3006 0.2900
0.4198 1.17 900 0.2951 0.2891
0.3864 1.3 1000 0.2989 0.2862
0.3963 1.43 1100 0.2932 0.2830
0.3953 1.56 1200 0.2936 0.2829
0.3962 1.69 1300 0.2952 0.2773
0.3811 1.82 1400 0.2915 0.2748
0.3736 1.95 1500 0.2839 0.2684
0.3507 2.08 1600 0.2914 0.2678
0.3277 2.21 1700 0.2895 0.2652
0.3344 2.34 1800 0.2843 0.2673
0.335 2.47 1900 0.2821 0.2635
0.3559 2.6 2000 0.2830 0.2599
0.3254 2.73 2100 0.2711 0.2577
0.3263 2.86 2200 0.2685 0.2546
0.3266 2.99 2300 0.2679 0.2521
0.3066 3.12 2400 0.2727 0.2526
0.2998 3.25 2500 0.2648 0.2537
0.2961 3.38 2600 0.2630 0.2519
0.3046 3.51 2700 0.2684 0.2506
0.3006 3.64 2800 0.2604 0.2492
0.2992 3.77 2900 0.2682 0.2508
0.2775 3.9 3000 0.2732 0.2440
0.2903 4.03 3100 0.2659 0.2427
0.2535 4.16 3200 0.2650 0.2433
0.2714 4.29 3300 0.2588 0.2394
0.2636 4.42 3400 0.2652 0.2434
0.2647 4.55 3500 0.2624 0.2371
0.2796 4.67 3600 0.2611 0.2373
0.2644 4.8 3700 0.2604 0.2341
0.2657 4.93 3800 0.2567 0.2331
0.2423 5.06 3900 0.2594 0.2322
0.2556 5.19 4000 0.2587 0.2323
0.2327 5.32 4100 0.2639 0.2299
0.2613 5.45 4200 0.2569 0.2310
0.2382 5.58 4300 0.2585 0.2298
0.2404 5.71 4400 0.2543 0.2287
0.2368 5.84 4500 0.2553 0.2286
0.2514 5.97 4600 0.2517 0.2279
0.2415 6.1 4700 0.2524 0.2270
0.2338 6.23 4800 0.2540 0.2265
0.219 6.36 4900 0.2549 0.2263
0.2428 6.49 5000 0.2546 0.2261

Framework versions

  • Transformers 4.16.1
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.2
  • Tokenizers 0.11.0