readme: ---
  tags:
  - asteroid
  - audio
  - MultiDecoderDPRNN
  datasets:
  - Wsj0MixVar
  - sep_clean
  license: cc-by-sa-4.0
  ---
  ## Asteroid model
  
  ## Description:
  - Code: The code corresponding to this pretrained model can be found [here](https://github.com/JunzheJosephZhu/asteroid/tree/master/egs/wsj0-mix-var/Multi-Decoder-DPRNN).
  
  - [Paper](https://ieeexplore.ieee.org/document/9414205): "Multi-Decoder DPRNN: High Accuracy Source Counting and Separation", Junzhe Zhu, Raymond Yeh, Mark Hasegawa-Johnson. ICASSP(2021). 
  
  - Summary: This model achieves SOTA on the problem of source separation with an unknown number of speakers. It uses multiple decoder heads(each tackling a distinct number of speakers), in addition to a classifier head that selects which decoder head to use.
  
  - [Project Page](https://junzhejosephzhu.github.io/Multi-Decoder-DPRNN/)
  
  - [Original research repo](https://github.com/JunzheJosephZhu/MultiDecoder-DPRNN)
  
  This model was trained by Joseph Zhu using the wsj0-mix-var/Multi-Decoder-DPRNN recipe in Asteroid. 
  It was trained on the `sep_count` task of the Wsj0MixVar dataset.
   
  ## Training config:
  ```yaml
  filterbank:
    n_filters: 64
    kernel_size: 8
    stride: 4
  masknet:
    n_srcs: [2, 3, 4, 5]
    bn_chan: 128
    hid_size: 128
    chunk_size: 128
    hop_size: 64
    n_repeats: 8
    mask_act: 'sigmoid'
    bidirectional: true
    dropout: 0
    use_mulcat: false
  training:
    epochs: 200
    batch_size: 2
    num_workers: 2
    half_lr: yes
    lr_decay: yes
    early_stop: yes
    gradient_clipping: 5
  optim:
    optimizer: adam
    lr: 0.001
    weight_decay: 0.00000
  data:
    train_dir: "data/{}speakers/wav8k/min/tr"
    valid_dir: "data/{}speakers/wav8k/min/cv"
    task: sep_count
    sample_rate: 8000
    seglen: 4.0
    minlen: 2.0
  loss:
    lambda: 0.05
  ```
   
  ## Results:
  ```yaml
  'Accuracy': 0.9723333333333334, 'P-Si-SNR': 10.36027378628496
  ```
  
  ### License notice:
  This work "MultiDecoderDPRNN" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
  by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for 
  Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only). 
  "MultiDecoderDPRNN" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
  by Joseph Zhu.
  