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Conv-TasNet

‼️new‼️: The modified training and testing code is now able to separate speech properly.

‼️new‼️: Updated model code, added code for skip connection section.

‼️notice‼️: Training Batch size setting 8/16

‼️notice‼️: The implementation of another article optimizing Conv-TasNet has been open sourced in "Deep-Encoder-Decoder-Conv-TasNet".

Demo Pages: Results of pure speech separation model

Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement

Luo Y, Mesgarani N. Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, 27(8): 1256-1266.

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Requirement

  • Pytorch 1.3.0
  • TorchAudio 0.3.1
  • PyYAML 5.1.2

Accomplished goal

  • Support Multi-GPU Training, you can see the train.yml
  • Use the Dataloader Method That Comes With Pytorch
  • Provide Pre-Training Models

Preparation files before training

  1. Generate dataset using create-speaker-mixtures.zip with WSJ0 or TIMI
  2. Generate scp file using script file of create_scp.py

Training this model

  • If you want to adjust the network parameters and the path of the training file, please modify the option/train/train.yml file.
  • Training Command 
    python train.py ./option/train/train.yml

Inference this model

  • Inference Command (Use this command if you need to test a large number of audio files.)

    python Separation.py -mix_scp 1.scp -yaml ./config/train/train.yml -model best.pt -gpuid [0,1,2,3,4,5,6,7] -save_path ./checkpoint

  • Inference Command (Use this command if you need to test a single audio files.)

    python Separation_wav.py -mix_wav 1.wav -yaml ./config/train/train.yml -model best.pt -gpuid [0,1,2,3,4,5,6,7] -save_path ./checkpoint

Results

  • Currently training, the results will be displayed when the training is over.
  • The following table is the experimental results of different parameters in the paper
N L B H Sc P X R Normalization Causal Receptive field Model Size SI-SNRi SDRi
128 40 128 256 128 3 7 2 gLN x 1.28 1.5M 13.0 13.3
256 40 128 256 128 3 7 2 gLN x 1.28 1.5M 13.1 13.4
512 40 128 256 128 3 7 2 gLN x 1.28 1.7M 13.3 13.6
512 40 128 256 256 3 7 2 gLN x 1.28 2.4M 13.0 13.3
512 40 128 512 128 3 7 2 gLN x 1.28 3.1M 13.3 13.6
512 40 128 512 512 3 7 2 gLN x 1.28 6.2M 13.5 13.8
512 40 256 256 256 3 7 2 gLN x 1.28 3.2M 13.0 13.3
512 40 256 512 256 3 7 2 gLN x 1.28 6.0M 13.4 13.7
512 40 256 512 512 3 7 2 gLN x 1.28 8.1M 13.2 13.5
512 40 128 512 128 3 6 4 gLN x 1.27 5.1M 14.1 14.4
512 40 128 512 128 3 4 6 gLN x 0.46 5.1M 13.9 14.2
512 40 128 512 128 3 8 3 gLN x 3.83 5.1M 14.5 14.8
512 32 128 512 128 3 8 3 gLN x 3.06 5.1M 14.7 15.0
512 16 128 512 128 3 8 3 gLN x 1.53 5.1M 15.3 15.6
512 16 128 512 128 3 8 3 cLN √ 1.53 5.1M 10.6 11.0

Pre-Train Model

‼️new‼️: Huggingface Pretrain Google Driver

Our Results Image

Reference