ConvTasNet

A PyTorch implementation of the TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation

Requirements

• scipy 1.1.0
• torch 1.0.0
• numpy 1.14.3

Usage

• training: configure conf.py and start train.sh

• inference

./nnet/time_domain_separate.py /path/to/checkpoint --input /path/to/mix.scp --gpu 0 > separate.log 2>&1 &

• evaluate

./nnet/compute_si_snr.py /path/to/ref_spk1.scp,/path/to/ref_spk2.scp /path/to/inf_spk1.scp,/path/to/inf_spk2.scp

Result (on best configuratures in the paper)

ID	Settings	Causal	Norm	Param	Loss	Si-SDR
0	adam/lr:1e-3/wd:1e-5/32-batch/2gpu	N	BN/relu	8.75M	-17.59/-15.45	14.63
1	adam/lr:1e-2/wd:1e-5/20-batch/2gpu	N	gLN/relu	-	-16.09/-15.21	14.58
2	adam/lr:1e-3/wd:1e-5/20-batch/2gpu	N	gLN/relu	-	-17.91/-16.54	15.87
3	adam/lr:1e-2/wd:1e-5/32-batch/2gpu	N	BN/sigmoid	-	-14.51/-13.40	12.62
4	adam/lr:1e-2/wd:1e-5/32-batch/2gpu	N	BN/relu	-	-17.20/-15.38	14.58
5	adam/lr:1e-3/wd:1e-5/20-batch/2gpu	N	gLN/sigmoid	-	-17.20/-16.11	15.55
6	adam/lr:1e-3/wd:1e-5/32-batch/2gpu	Y	BN/relu	-	-15.25/-12.47	11.42
7	adam/lr:1e-3/wd:1e-5/24-batch/2gpu	N	cLN/relu	-	-18.72/-16.17	15.25

Reference

Luo Y, Mesgarani N. TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation[J]. arXiv preprint arXiv:1809.07454, 2018.