No computation advantage in parallelizing deepspeech with torch?

[awsomecod]

The function inference(Input) uses deepspeech to transcript audio files. I need to run inference() function for 10 different inputs. I wrote the following code to perform these 10 different runs in parallel. The code works but I don't gain any advantage with respect to computation time. Why?

import torch

processes = []

for i in range(0,10):

    p = torch.multiprocessing.Process(target=inference, args=(Input[i],))

    p.start()
        
for p in processes:
        
    p.join()

A simplified version of inference() function is as follows:

def Inference: 

    ds=Model('./deepspeech-0.9.3-models.pbmm') 

    speech=ds.stt(audio)

I use nvidia GPU and run deepspeech on GPU.

Ubuntu 20.04
Python 3.8.10
GPU: nvidia
Cuda 10.1

[lissyx]

this is not a bug