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mms-1b-all-bem-genbed-f-model

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
genbed
mms
Generated from Trainer
Inference Endpoints
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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - automatic-speech-recognition
  - genbed
  - mms
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: mms-1b-all-bem-genbed-f-model
    results: []

mms-1b-all-bem-genbed-f-model

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

  • Loss: 0.1823
  • Wer: 0.3431

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
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 30.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
6.6556 0.1370 100 0.5951 0.6643
0.4415 0.2740 200 0.2734 0.4565
0.3448 0.4110 300 0.2482 0.4289
0.3459 0.5479 400 0.2392 0.4149
0.3184 0.6849 500 0.2304 0.4085
0.3058 0.8219 600 0.2372 0.4108
0.3077 0.9589 700 0.2271 0.4172
0.2812 1.0959 800 0.2217 0.3983
0.3297 1.2329 900 0.2209 0.3984
0.2817 1.3699 1000 0.2163 0.4124
0.2927 1.5068 1100 0.2146 0.3863
0.2806 1.6438 1200 0.2106 0.3851
0.2574 1.7808 1300 0.2098 0.3866
0.2829 1.9178 1400 0.2067 0.3772
0.2764 2.0548 1500 0.2076 0.3789
0.2635 2.1918 1600 0.2076 0.3769
0.2761 2.3288 1700 0.2068 0.3801
0.2854 2.4658 1800 0.1994 0.3645
0.2557 2.6027 1900 0.2016 0.3861
0.2717 2.7397 2000 0.2011 0.3734
0.2504 2.8767 2100 0.1989 0.3674
0.2606 3.0137 2200 0.1990 0.3835
0.2583 3.1507 2300 0.2028 0.3666
0.2591 3.2877 2400 0.1952 0.3507
0.2408 3.4247 2500 0.1988 0.3637
0.2485 3.5616 2600 0.1972 0.3593
0.2474 3.6986 2700 0.1949 0.3534
0.2398 3.8356 2800 0.1959 0.3697
0.2512 3.9726 2900 0.1906 0.3559
0.2266 4.1096 3000 0.1905 0.3482
0.2538 4.2466 3100 0.1916 0.3521
0.2268 4.3836 3200 0.1914 0.3895
0.2249 4.5205 3300 0.1897 0.3417
0.2416 4.6575 3400 0.1877 0.3458
0.2421 4.7945 3500 0.1872 0.3412
0.244 4.9315 3600 0.1855 0.3528
0.2371 5.0685 3700 0.1871 0.3447
0.2383 5.2055 3800 0.1833 0.3523
0.2409 5.3425 3900 0.1886 0.3487
0.2312 5.4795 4000 0.1848 0.3438
0.2261 5.6164 4100 0.1866 0.3469
0.2169 5.7534 4200 0.1841 0.3376
0.2283 5.8904 4300 0.1865 0.3412
0.2182 6.0274 4400 0.1823 0.3431
0.2141 6.1644 4500 0.1858 0.3403
0.2127 6.3014 4600 0.1876 0.3356
0.229 6.4384 4700 0.1863 0.3361

Framework versions

  • Transformers 4.46.0.dev0
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0