readme: ---
  tags:
  - asteroid
  - audio
  - ConvTasNet
  - audio-to-audio
  datasets:
  - Libri1Mix
  - enh_single
  license: cc-by-sa-4.0
  ---
  ## Asteroid model `cankeles/ConvTasNet_WHAMR_enhsingle_16k`
  
  Description:
  
  This model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.
  
  The initial model was taken from here: https://huggingface.co/JorisCos/ConvTasNet_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:
  ```
   'sar': 13.612368475881558,
   'sar_imp': 9.709316571584433,
   'sdr': 13.612368475881558,
   'sdr_imp': 9.709316571584433,
   'si_sdr': 12.978640274976373,
   'si_sdr_imp': 9.161273840297232,
   'sir': inf,
   'sir_imp': nan,
   'stoi': 0.9214516928197306,
   'stoi_imp': 0.11657488247668318
  
  ```
  
  