You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Thank you for sharing!
I can successfuly run the demo code and get the denoised and beamformed audio =]
However, I wondered that whether the input dimension is fixed or at least longer than a pre-defined length.
It failed when the len_input and len_pad were set to 512 and 0, respectively.
I also calculated the run time during each iteration of inferencing (input was a 3-sec audio ).
It took about 2 to 3 sec in the PMF prediction step
and it took about 40 to 50 sec to execute update_beamform_coef_with_weights function
Is it possible that the input and output is a short frame size (20~30 ms)
and the run time can be decreased to meet real-time criteria?
The text was updated successfully, but these errors were encountered:
saekisongalag
changed the title
input/output dimenssion & model complexity
input/output dimension & model complexity
Jul 19, 2022
Thank you for sharing!
I can successfuly run the demo code and get the denoised and beamformed audio =]
However, I wondered that whether the input dimension is fixed or at least longer than a pre-defined length.
It failed when the len_input and len_pad were set to 512 and 0, respectively.
I also calculated the run time during each iteration of inferencing (input was a 3-sec audio ).
It took about 2 to 3 sec in the PMF prediction step
and it took about 40 to 50 sec to execute update_beamform_coef_with_weights function
Is it possible that the input and output is a short frame size (20~30 ms)
and the run time can be decreased to meet real-time criteria?
The text was updated successfully, but these errors were encountered: