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cankeles
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ConvTasNet_WHAMR_enhsingle_16k

Audio-to-Audio
Asteroid
PyTorch
audio
ConvTasNet
Model card Files
xet
Community
ConvTasNet_WHAMR_enhsingle_16k
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---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- WHAMRmod
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model `cankeles/ConvTasNet_WHAMRmod_enhsingle_16k`

Description:

This model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings.
The initial model was taken from here: https://huggingface.co/JorisCos/DCCRNet_Libri1Mix_enhsingle_16k
This model was trained by M. Can Keles using the WHAM recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the WHAM dataset.

Training config:

```yml
data:
  mode: min
  nondefault_nsrc: null
  sample_rate: 16000
  task: enh_single
  train_dir: wav16k/min/tr/
  valid_dir: wav16k/min/cv/
filterbank:
  kernel_size: 16
  n_filters: 512
  stride: 8
main_args:
  exp_dir: exp/tmp
  help: null
masknet:
  bn_chan: 128
  hid_chan: 512
  mask_act: relu
  n_blocks: 8
  n_repeats: 3
  n_src: 1
  skip_chan: 128
optim:
  lr: 0.001
  optimizer: adam
  weight_decay: 0.0
positional arguments: {}
training:
  batch_size: 2
  early_stop: true
  epochs: 10
  half_lr: true
  num_workers: 4
```
  

Results:

On Libri1Mix min test set :
```yml
si_sdr: 14.743051006476085
si_sdr_imp: 11.293269700616385
sdr: 15.300522933671061
sdr_imp: 11.797860134458015
sir: Infinity
sir_imp: NaN
sar: 15.300522933671061
sar_imp: 11.797860134458015
stoi: 0.9310514162434267
stoi_imp: 0.13513159270288563
```