/uPIT-for-speech-separation

Speech separation with utterance-level PIT experiments

Primary LanguagePython

Speech Separation with uPIT

Speech separation with utterance-level PIT(Permutation Invariant Training)

Requirements

see requirements.txt

Usage

  1. Generate dataset using create-speaker-mixtures.zip

  2. Prepare cmvn, .scp and configure experiments in .yaml files

  3. Training:

    ./run_pit.py --config $conf --num-epoches 100 > $checkpoint/train.log 2>&1 &

  4. Inference:

    ./separate.py --dump-dir cache $mdl_dir/train.yaml $mdl_dir/epoch.40.pkl egs.scp

Experiments

Configure Mask Epoch FM FF MM FF/MM AVG
config-1 AM-ReLU 75 10.41 6.73 7.35 7.19 8.82
config-2 AM-sigmoid 50 9.95 5.99 6.72 6.35 8.26
config-3 PSM-ReLU 73 10.29 6.54 7.28 7.09 8.71
config-4 PSM-ReLU 80 10.37 6.59 7.29 7.10 8.76
config-5 PSM-ReLU 62 10.58 7.00 7.55 7.40 9.01
config-6 PSM-ReLU 62 10.47 7.44 7.78 7.69 9.10
config-7 PSM-ReLU 61 10.43 7.17 7.41 7.34 8.91
- IAM-oracle - 12.49 12.73 11.58 11.88 12.19
- IBM-oracle - 12.94 13.20 12.04 12.35 12.65
- IRM-oracle - 12.86 13.14 11.96 12.27 12.57
- PSM-oracle - 15.79 16.03 14.90 15.20 15.50

Reference

  • Kolbæk M, Yu D, Tan Z H, et al. Multitalker speech separation with utterance-level permutation invariant training of deep recurrent neural networks[J]. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 2017, 25(10): 1901-1913.