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wav2vec2-xls-r-300m-en-atc-uwb-atcc-and-atcosim

Automatic Speech Recognition
Transformers
PyTorch
Safetensors
English
wav2vec2
audio
en-atc
Generated from Trainer
Eval Results
Inference Endpoints
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wav2vec2-xls-r-300m-en-atc-uwb-atcc-and-atcosim / README.md
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metadata
license: apache-2.0
tags:
  - automatic-speech-recognition
  - experiments/data/atcosim_uwb_atcc/train
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: 0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc
    results: []

0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc

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

  • Loss: 0.5595
  • Wer: 0.1687

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: 24
  • eval_batch_size: 12
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.63 500 3.0458 1.0
2.9181 1.27 1000 1.1503 0.4723
2.9181 1.9 1500 0.8275 0.3500
0.7646 2.53 2000 0.6990 0.2845
0.7646 3.17 2500 0.5828 0.2509
0.5394 3.8 3000 0.5363 0.2487
0.5394 4.44 3500 0.5467 0.2171
0.4558 5.07 4000 0.5290 0.2090
0.4558 5.7 4500 0.4992 0.2046
0.3773 6.34 5000 0.4934 0.2052
0.3773 6.97 5500 0.4700 0.1983
0.3301 7.6 6000 0.4938 0.1874
0.3301 8.24 6500 0.5364 0.1893
0.2938 8.87 7000 0.5170 0.1830
0.2938 9.51 7500 0.5408 0.1815
0.2674 10.14 8000 0.5581 0.1733
0.2674 10.77 8500 0.5389 0.1719
0.24 11.41 9000 0.5344 0.1714
0.24 12.04 9500 0.5503 0.1686
0.211 12.67 10000 0.5595 0.1687

Framework versions

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