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sew-small-100k-timit

Automatic Speech Recognition Transformers PyTorch TensorBoard
sew timit_asr generated_from_trainer Inference Endpoints
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metadata
license: apache-2.0
tags:
  - automatic-speech-recognition
  - timit_asr
  - generated_from_trainer
datasets:
  - timit_asr
model-index:
  - name: sew-small-100k-timit
    results: []

sew-small-100k-timit

This model is a fine-tuned version of asapp/sew-small-100k on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4926
  • Wer: 0.2988

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: 32
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 20.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.071 0.69 100 3.0262 1.0
2.9304 1.38 200 2.9297 1.0
2.8823 2.07 300 2.8367 1.0
1.5668 2.76 400 1.2310 0.8807
0.7422 3.45 500 0.7080 0.5957
0.4121 4.14 600 0.5829 0.5073
0.3981 4.83 700 0.5153 0.4461
0.5038 5.52 800 0.4908 0.4151
0.2899 6.21 900 0.5122 0.4111
0.2198 6.9 1000 0.4908 0.3803
0.2129 7.59 1100 0.4668 0.3789
0.3007 8.28 1200 0.4788 0.3562
0.2264 8.97 1300 0.5113 0.3635
0.1536 9.66 1400 0.4950 0.3441
0.1206 10.34 1500 0.5062 0.3421
0.2021 11.03 1600 0.4900 0.3283
0.1458 11.72 1700 0.5019 0.3307
0.1151 12.41 1800 0.4989 0.3270
0.0985 13.1 1900 0.4925 0.3173
0.1412 13.79 2000 0.4868 0.3125
0.1579 14.48 2100 0.4983 0.3147
0.1043 15.17 2200 0.4914 0.3091
0.0773 15.86 2300 0.4858 0.3102
0.1327 16.55 2400 0.5084 0.3064
0.1281 17.24 2500 0.5017 0.3025
0.0845 17.93 2600 0.5001 0.3012
0.0717 18.62 2700 0.4894 0.3004
0.0835 19.31 2800 0.4963 0.2998
0.1181 20.0 2900 0.4926 0.2988

Framework versions

  • Transformers 4.12.0.dev0
  • Pytorch 1.8.1
  • Datasets 1.14.1.dev0
  • Tokenizers 0.10.3