Hugging Face's logo

asr-africa
/
w2v-bert-2.0-CV_Fleurs-lg-100hrs-v4

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2-bert
Generated from Trainer
Inference Endpoints
Model card Files Community
w2v-bert-2.0-CV_Fleurs-lg-100hrs-v4 / README.md
sulaimank's picture
sulaimank
End of training
9fea425 verified
preview code
|
raw
history blame contribute delete
3.15 kB
metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: w2v-bert-2.0-CV_Fleurs-lg-100hrs-v4
    results: []

w2v-bert-2.0-CV_Fleurs-lg-100hrs-v4

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2835
  • Wer: 0.2749
  • Cer: 0.0546

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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 42
  • 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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.3102 1.0 7057 0.3038 0.3739 0.0787
0.1944 2.0 14114 0.2340 0.3188 0.0634
0.1558 3.0 21171 0.2157 0.3021 0.0598
0.1373 4.0 28228 0.2037 0.2926 0.0574
0.1237 5.0 35285 0.2079 0.3025 0.0591
0.1152 6.0 42342 0.2044 0.2899 0.0583
0.1091 7.0 49399 0.2025 0.3012 0.0567
0.1042 8.0 56456 0.2033 0.2728 0.0552
0.0985 9.0 63513 0.2035 0.2760 0.0543
0.0949 10.0 70570 0.2054 0.2730 0.0541
0.0885 11.0 77627 0.2071 0.2758 0.0557
0.08 12.0 84684 0.2039 0.2743 0.0540
0.0719 13.0 91741 0.2045 0.2648 0.0531
0.063 14.0 98798 0.2133 0.2682 0.0544
0.0555 15.0 105855 0.2107 0.2658 0.0524
0.0471 16.0 112912 0.2408 0.2687 0.0529
0.0399 17.0 119969 0.2419 0.2721 0.0535
0.0341 18.0 127026 0.2587 0.2833 0.0551
0.0293 19.0 134083 0.2518 0.2735 0.0534
0.0255 20.0 141140 0.2795 0.2669 0.0530
0.022 21.0 148197 0.2748 0.2820 0.0543
0.0196 22.0 155254 0.2837 0.2785 0.0529
0.017 23.0 162311 0.2835 0.2749 0.0546

Framework versions

  • Transformers 4.46.0
  • Pytorch 2.1.0+cu118
  • Datasets 3.0.2
  • Tokenizers 0.20.1