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wav2vec2-xls-r-300m-en-atc-atcosim

Automatic Speech Recognition
Transformers
PyTorch
English
wav2vec2
audio
en-atc
Generated from Trainer
Eval Results
Inference Endpoints
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wav2vec2-xls-r-300m-en-atc-atcosim / README.md
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metadata
license: apache-2.0
tags:
  - automatic-speech-recognition
  - experiments/data/atcosim_corpus/train
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: 0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl
    results: []

0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the EXPERIMENTS/DATA/ATCOSIM_CORPUS/TRAIN - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0988
  • Wer: 0.0736

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: 0.0005
  • train_batch_size: 24
  • eval_batch_size: 24
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 96
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 20000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.9105 6.41 500 0.1622 0.1531
0.1119 12.82 1000 0.0971 0.0936
0.0614 19.23 1500 0.1002 0.0983
0.044 25.64 2000 0.1011 0.0929
0.0366 32.05 2500 0.0932 0.0828
0.0315 38.46 3000 0.0926 0.0880
0.0297 44.87 3500 0.0972 0.0882
0.0216 51.28 4000 0.0911 0.0774
0.0211 57.69 4500 0.0982 0.0891
0.0187 64.1 5000 0.1009 0.0863
0.02 70.51 5500 0.0953 0.0852
0.0163 76.92 6000 0.1028 0.0804
0.0128 83.33 6500 0.0930 0.0856
0.0127 89.74 7000 0.0892 0.0676
0.0116 96.15 7500 0.0857 0.0753
0.0139 102.56 8000 0.1078 0.0481
0.0107 108.97 8500 0.0955 0.0683
0.0096 115.38 9000 0.0846 0.0697
0.0089 121.79 9500 0.0854 0.0675
0.0084 128.21 10000 0.0875 0.0779
0.0074 134.62 10500 0.0840 0.0770
0.0061 141.03 11000 0.0903 0.0754
0.0076 147.44 11500 0.0872 0.0769
0.0069 153.85 12000 0.0891 0.0772
0.0061 160.26 12500 0.0971 0.0774
0.0049 166.67 13000 0.0984 0.0726
0.0045 173.08 13500 0.0952 0.0765
0.0039 179.49 14000 0.1015 0.0762
0.0031 185.9 14500 0.0937 0.0712
0.0032 192.31 15000 0.0982 0.0635
0.0028 198.72 15500 0.0981 0.0743
0.0024 205.13 16000 0.1019 0.0712
0.0024 211.54 16500 0.0957 0.0732
0.002 217.95 17000 0.0941 0.0732
0.0015 224.36 17500 0.1009 0.0717
0.0017 230.77 18000 0.0955 0.0730
0.0013 237.18 18500 0.0989 0.0732
0.0013 243.59 19000 0.0967 0.0738
0.0011 250.0 19500 0.0980 0.0734
0.0008 256.41 20000 0.0988 0.0736

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.13.0+cu117
  • Datasets 2.6.1
  • Tokenizers 0.13.2