Asteroid model JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k
Description:
This model was trained by Joris Cosentino using the librimix recipe in Asteroid.
It was trained on the sep_noisy
task of the Libri2Mix dataset.
Training config:
data:
n_src: 2
sample_rate: 16000
segment: 3
task: sep_noisy
train_dir: data/wav16k/min/train-360
valid_dir: data/wav16k/min/dev
filterbank:
kernel_size: 32
n_filters: 512
stride: 16
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 2
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
training:
batch_size: 6
early_stop: true
epochs: 200
half_lr: true
num_workers: 4
Results:
On Libri2Mix min test set :
si_sdr: 10.617130949793383
si_sdr_imp: 12.551811412989263
sdr: 11.231867464482065
sdr_imp: 13.059765009747343
sir: 24.461138352988346
sir_imp: 24.371856452307703
sar: 11.5649982725426
sar_imp: 4.662525705768228
stoi: 0.8701085138712695
stoi_imp: 0.2245418019822898
License notice:
This work "ConvTasNet_Libri2Mix_sepnoisy_16k" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov, used underCC BY 4.0; of The WSJ0 Hipster Ambient Mixtures dataset by Whisper.ai, used under CC BY-NC 4.0 (Research only). "ConvTasNet_Libri2Mix_sepnoisy_16k" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino
- Downloads last month
- 148
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.