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wav2vec2-large-960h-lv60-self-en-atc-atcosim

Automatic Speech Recognition
Transformers
PyTorch
Safetensors
English
wav2vec2
audio
en-atc
Generated from Trainer
Eval Results
Inference Endpoints
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wav2vec2-large-960h-lv60-self-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-large-960h-lv60-self on the EXPERIMENTS/DATA/ATCOSIM_CORPUS/TRAIN - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0850
  • Wer: 0.0167

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
1.4757 6.41 500 0.0614 0.0347
0.0624 12.82 1000 0.0525 0.0277
0.0388 19.23 1500 0.0693 0.0241
0.03 25.64 2000 0.0666 0.0244
0.0235 32.05 2500 0.0604 0.0260
0.0226 38.46 3000 0.0625 0.0230
0.0163 44.87 3500 0.0603 0.0195
0.0157 51.28 4000 0.0628 0.0209
0.0152 57.69 4500 0.0692 0.0238
0.0122 64.1 5000 0.0607 0.0210
0.011 70.51 5500 0.0608 0.0213
0.0114 76.92 6000 0.0681 0.0211
0.0106 83.33 6500 0.0613 0.0210
0.0081 89.74 7000 0.0654 0.0196
0.0078 96.15 7500 0.0612 0.0191
0.0082 102.56 8000 0.0758 0.0237
0.0078 108.97 8500 0.0664 0.0206
0.0075 115.38 9000 0.0658 0.0197
0.0052 121.79 9500 0.0669 0.0218
0.0054 128.21 10000 0.0695 0.0211
0.0053 134.62 10500 0.0726 0.0227
0.0046 141.03 11000 0.0702 0.0212
0.0043 147.44 11500 0.0846 0.0200
0.0041 153.85 12000 0.0764 0.0200
0.0032 160.26 12500 0.0785 0.0201
0.0028 166.67 13000 0.0839 0.0197
0.0035 173.08 13500 0.0785 0.0210
0.0027 179.49 14000 0.0730 0.0188
0.002 185.9 14500 0.0794 0.0193
0.002 192.31 15000 0.0859 0.0211
0.0019 198.72 15500 0.0727 0.0183
0.0017 205.13 16000 0.0784 0.0187
0.0016 211.54 16500 0.0801 0.0196
0.0014 217.95 17000 0.0821 0.0185
0.0011 224.36 17500 0.0822 0.0176
0.001 230.77 18000 0.0856 0.0171
0.001 237.18 18500 0.0792 0.0176
0.001 243.59 19000 0.0826 0.0173
0.0006 250.0 19500 0.0854 0.0170
0.0007 256.41 20000 0.0850 0.0167

Framework versions

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