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---
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/asteroid-team/asteroid/tree/master/egs/wsj0-mix-var/Multi-Decoder-DPRNN).
- Notebook: Colab Notebook with examples can be found [here](https://colab.research.google.com/drive/11MGx3_sgOrQrB6k8edyAvg5mGIxqR5ED?usp=sharing)
- [Paper](http://www.isle.illinois.edu/speech_web_lg/pubs/2021/zhu2021multi.pdf): "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.
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