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Transformer result (default transformer with initial learning rate = 1.0 and epochs = 25)

  • Environments
    • date: Tue Feb 25 16:18:51 EST 2020
    • system information: Linux node9 4.15.0-72-generic #81~16.04.1-Ubuntu SMP Tue Nov 26 16:34:21 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
    • python version: Python 3.6.8
    • espnet version: espnet 0.6.2
    • chainer version: chainer 6.0.0
    • pytorch version: pytorch 1.0.1.post2
    • Git hash: 9ec6939a580c4f8ab6cefb9cb13614e44e72a627
  • Model files (archived to model.v1.tar.gz)
    • model link: https://drive.google.com/drive/folders/1qsPhnQJOahBHcNmA4EnWwCBNp2oiZivC
    • training config file: conf/train.yaml
    • decoding config file: conf/decode.yaml
    • cmvn file: data/train_sp/cmvn.ark
    • e2e file: exp/train_sp_pytorch_train/results/model.last10.avg.best
    • e2e JSON file: exp/train_sp_pytorch_train/results/model.json
    • lm file: exp/train_rnnlm_pytorch_lm/rnnlm.model.best
    • lm JSON file: exp/train_rnnlm_pytorch_lm/model.json
  • Results
exp/train_sp_pytorch_train/decode_test_android_avg_best_lm/result.txt
|   SPKR     |   # Snt      # Wrd   |   Corr        Sub        Del        Ins        Err      S.Err   |
|   Sum/Avg  |   5000       49534   |   91.2        8.4        0.4        0.1        8.9       46.4   |
exp/train_sp_pytorch_train/decode_test_ios_avg_best_lm/result.txt
|   SPKR     |   # Snt      # Wrd   |   Corr        Sub        Del        Ins        Err      S.Err   |
|   Sum/Avg  |   5000       49534   |   92.6        7.1        0.3        0.1        7.5       41.7   |
exp/train_sp_pytorch_train/decode_test_mic_avg_best_lm/result.txt
|   SPKR     |   # Snt      # Wrd   |   Corr        Sub        Del        Ins        Err      S.Err   |
|   Sum/Avg  |   5000       49534   |   91.6        8.1        0.3        0.1        8.6       45.3   |
exp/train_sp_pytorch_train/decode_test_overall_avg_best_lm/result.txt
|   SPKR     |   # Snt      # Wrd   |   Corr        Sub        Del        Ins        Err      S.Err   |
|   Sum/Avg  |   15000      148602  |   91.8        7.9        0.3        0.1        8.3       44.5   |