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README.md

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+---
+tags:
+- asteroid
+- audio
+- ConvTasNet
+- audio-source-separation
+datasets:
+- wham
+- sep_clean
+license: cc-by-sa-3.0
+inference: false
+---
+
+## Asteroid model `mpariente/ConvTasNet_WHAM_sepclean`
+Imported from [Zenodo](https://zenodo.org/record/3862942)
+
+### Description:
+This model was trained by Manuel Pariente 
+using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
+It was trained on the `sep_clean` task of the WHAM! dataset.
+
+### Training config:
+```yaml
+data:
+    channels: 1
+    n_src: 2
+    root_path: data
+    sample_rate: 16000
+    samples_per_track: 10
+    segment: 3.0
+    task: enh_both
+filterbank:
+    kernel_size: 20
+    n_filters: 256
+    stride: 10
+main_args:
+    exp_dir: exp/train_convtasnet
+    help: None
+masknet:
+    bn_chan: 256
+    conv_kernel_size: 3
+    hid_chan: 512
+    mask_act: relu
+    n_blocks: 8
+    n_repeats: 4
+    n_src: 2
+    norm_type: gLN
+    skip_chan: 256
+optim:
+    lr: 0.0003
+    optimizer: adam
+    weight_decay: 0.0
+positional arguments:
+training:
+   batch_size: 12
+    early_stop: True
+    epochs: 50
+    half_lr: True
+    num_workers: 12
+```
+
+### Results:
+```yaml
+si_sdr: 14.018196157142519
+si_sdr_imp: 14.017103133809577
+sdr: 14.498517291333885
+sdr_imp: 14.463389151567865
+sir: 24.149634529133372
+sir_imp: 24.11450638936735
+sar: 15.338597389045935
+sar_imp: -137.30634122401517
+stoi: 0.7639416744417206
+stoi_imp: 0.1843383526963759
+```
+
+### License notice:
+This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike.

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